pax_global_header00006660000000000000000000000064146730156230014521gustar00rootroot0000000000000052 comment=3b599e113d9615240fca193d71166a7026aa8057 cobrapy-0.29.1/000077500000000000000000000000001467301562300132515ustar00rootroot00000000000000cobrapy-0.29.1/.editorconfig000066400000000000000000000005071467301562300157300ustar00rootroot00000000000000# Editor Configuration (http://editorconfig.org) root = true [*] charset = utf-8 indent_style = space indent_size = 4 end_of_line = lf insert_final_newline = true trim_trailing_whitespace = true max_line_length = 88 [*.{json,yml}] indent_size = 2 [*.{md,rst}] trim_trailing_whitespace = false [Makefile] indent_style = tab cobrapy-0.29.1/.github/000077500000000000000000000000001467301562300146115ustar00rootroot00000000000000cobrapy-0.29.1/.github/CODE_OF_CONDUCT.md000066400000000000000000000127741467301562300174230ustar00rootroot00000000000000 # Contributor Covenant Code of Conduct ## Our Pledge We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community. ## Our Standards Examples of behavior that contributes to a positive environment for our community include: * Demonstrating empathy and kindness toward other people * Being respectful of differing opinions, viewpoints, and experiences * Giving and gracefully accepting constructive feedback * Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience * Focusing on what is best not just for us as individuals, but for the overall community Examples of unacceptable behavior include: * The use of sexualized language or imagery, and sexual attention or advances of any kind * Trolling, insulting or derogatory comments, and personal or political attacks * Public or private harassment * Publishing others' private information, such as a physical or email address, without their explicit permission * Other conduct which could reasonably be considered inappropriate in a professional setting ## Enforcement Responsibilities Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful. Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate. ## Scope This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at [conduct@opencobra.org](mailto:conduct@opencobra.org). All complaints will be reviewed and investigated promptly and fairly. All community leaders are obligated to respect the privacy and security of the reporter of any incident. The current team handling Code of Conduct incidents consists of: * [Maureen Carey](mailto:MAC9JC@virginia.edu) * [Svetlana Kutuzova](mailto:svegal@biosustain.dtu.dk) * [Greg Medlock](mailto:GLM5UH@virginia.edu) If your issue is with one or more of these members of the team, please do not write to conduct@opencobra.org but contact the remaining members individually. ## Enforcement Guidelines Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct: ### 1. Correction **Community Impact**: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community. **Consequence**: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested. ### 2. Warning **Community Impact**: A violation through a single incident or series of actions. **Consequence**: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban. ### 3. Temporary Ban **Community Impact**: A serious violation of community standards, including sustained inappropriate behavior. **Consequence**: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban. ### 4. Permanent Ban **Community Impact**: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals. **Consequence**: A permanent ban from any sort of public interaction within the community. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 2.0, available at https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. Community Impact Guidelines were inspired by [Mozilla's code of conduct enforcement ladder](https://github.com/mozilla/diversity). [homepage]: https://www.contributor-covenant.org For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https://www.contributor-covenant.org/translations. cobrapy-0.29.1/.github/CONTRIBUTING.rst000066400000000000000000000174301467301562300172570ustar00rootroot00000000000000============ Contributing ============ Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given. You can contribute in many ways: Report Bugs =========== Report bugs at https://github.com/opencobra/cobrapy/issues. If you are reporting a bug, please follow the template guide lines. The more detailed your report, the easier and thus faster we can help you. Fix Bugs ======== Look through the GitHub issues for bugs. Anything tagged with "bug" and "help wanted" is open to whoever wants to implement it. Implement Features ================== Look through the GitHub issues for features. Anything tagged with "enhancement" and "help wanted" is open to whoever wants to implement it. Write Documentation =================== cobrapy could always use more documentation, whether as part of the official documentation, in docstrings, or even on the web in blog posts, articles, and such. Submit Feedback =============== The best way to send feedback is to file an issue at https://github.com/opencobra/cobrapy/issues. If you are proposing a feature: * Explain in detail how it would work. * Keep the scope as narrow as possible, to make it easier to implement. * Remember that this is a volunteer-driven project, and that contributions are welcome :) Get Started! ============ Ready to contribute? Here's how to set up cobrapy for local development. 1. Fork the https://github.com/opencobra/cobrapy repository on GitHub. If you have never done this before, `follow the official guide `_ 2. Clone your fork locally as described in the same guide. 3. Install your local copy into a a Python virtual environment. You can `read this guide to learn more `_ about them and how to create one. Alternatively, particularly if you are a Windows or Mac user, you can also use `Anaconda `_. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development .. code-block:: console mkvirtualenv my-env cd cobrapy/ pip install -e ".[development]" 4. Create a branch for local development using the ``devel`` branch as a starting point. Use ``fix``, ``refactor``, or ``feat`` as a prefix .. code-block:: console git checkout devel git checkout -b fix-name-of-your-bugfix Now you can make your changes locally. 5. When making changes locally, it is helpful to ``git commit`` your work regularly. On one hand to save your work and on the other hand, the smaller the steps, the easier it is to review your work later. Please use `semantic commit messages `_. .. code-block:: console git add . git commit -m "fix: Your summary of changes" 6. When you're done making changes, check that your changes pass our test suite. This is all included with tox .. code-block:: console tox You can also run all ``tox`` tests in parallel. As of ``tox 3.7``, you can run .. code-block:: console tox --parallel 7. Push your branch to GitHub. .. code-block:: console git push origin fix-name-of-your-bugfix 8. Open the link displayed in the message when pushing your new branch in order to submit a pull request. Please follow the template presented to you in the web interface to complete your pull request. For larger features that you want to work on collaboratively with other cobrapy team members, you may consider to first request to join the cobrapy developers team to get write access to the repository so that you can create a branch in the main repository (or simply ask the maintainer to create a branch for you). Once you have a new branch you can push your changes directly to the main repository and when finished, submit a pull request from that branch to ``devel``. Pull Request Guidelines ----------------------- Before you submit a pull request, check that it meets these guidelines: 1. The pull request should include tests in the ``cobra/test`` directory. Except in rare circumstances, code coverage must not decrease (as reported by codecov which runs automatically when you submit your pull request) 2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring and consider creating a notebook that demonstrates the usage in ``documentation_builder`` (documentation is written as jupyter notebooks in the ``documentation_builder`` directory, which are then converted to rst by the ``autodoc.sh`` script.) 3. The pull request will be tested for several different Python versions. 4. Someone from the @opencobra/cobrapy-core team will review your work and guide you to a successful contribution. Unit tests and benchmarks ------------------------- cobrapy uses `pytest `_ for its unit-tests and new features should in general always come with new tests that make sure that the code runs as intended. Since COBRA rapidly can become quite resource intensive fundamental methods such as model manipulation, adding and removing reactions, metabolites etc also must work efficiently. We use `pytest-benchmark `_ to compare different implementations to make sure that new code do not come with unacceptable increased computation time. If you add benchmarked tests, make sure to also include a test with and without the benchmark as we do not want to slow down continuous integration by running benchmarks, for examples, see e.g. ``test_add_metabolite`` in `test_model.py `_. ``test_add_metabolite`` is the main test, ``test_add_metabolite_benchmark`` takes the special ``benchmark`` fixture that enables profiling the important code snippet but is skipped when running:: (cobrapy)$ pytest --benchmark-skip When the test function itself is small and can safely be assumed to not take many resources, we can directly profile the test as in ``test_subtract_metabolite_benchmark`` which calls ``benchmark(self.test_subtract_metabolite, model)``. To run all tests and benchmarks do:: (cobrapy)$ pytest and to compare two implementations you may keep them in two branches e.g. ``old`` and ``new`` and then do:: (cobrapy)$ git checkout old (cobrapy)$ pytest --benchmark-save (cobrapy)$ git checkout new (cobrapy)$ pytest --benchmark-compare Branching model --------------- ``devel`` Is the branch all pull-requests should be based on. ``stable`` Is only touched by maintainers and is the branch with only tested, reviewed code that is released or ready for the next release. ``{fix, bugfix, doc, feature}/descriptive-name`` Is the recommended naming scheme for smaller improvements, bugfixes, documentation improvement and new features respectively. Please use concise descriptive commit messages and consider using ``git pull --rebase`` when you update your own fork to avoid merge commits. Thank you very much for contributing to cobrapy! FAQs ---- Q1. Why do all of the tests that involve loading a pickled model fail on my branch? A: Pickling is the standard method for serializing objects in python, which is commonly done during operations like multiprocessing. Because of this, we need to maintain tests that run on pickled models, otherwise contributors may inadvertantly break multiprocessing features. If changes you made to cobrapy modify attributes of the ``cobra.Model`` class, the pickled models stored in the repository won't contain those changes and may fail tests that you add or modify. To resolve these errors, just run ``cobra/test/data/update_pickles.py`` on your branch, which will repickle the models. cobrapy-0.29.1/.github/ISSUE_TEMPLATE/000077500000000000000000000000001467301562300167745ustar00rootroot00000000000000cobrapy-0.29.1/.github/ISSUE_TEMPLATE/01-bug-report.yaml000066400000000000000000000042031467301562300221630ustar00rootroot00000000000000name: 🐞 Bug report description: Report a problem to help improve this project title: "[BUG] " labels: [bug, triage] body: - type: checkboxes attributes: label: Is there an existing issue for this? description: Please search to see if an [issue already exists](https://github.com/opencobra/cobrapy/issues) for the bug you encountered. options: - label: I have searched the existing issues required: true - type: textarea attributes: label: Problem description description: | A concise description of what you're experiencing. Please explain: * **what** you tried to achieve, * **how** you went about it (referring to the code sample), and * **why** the current behaviour is a problem and what output you expected instead. validations: required: false - type: textarea attributes: label: Code sample description: > Create a [minimal, complete, verifiable example](https://stackoverflow.com/help/mcve). Please, paste your code between the ``` tickmarks below or link to a [gist](https://gist.github.com/). value: | Code run: ```python ``` Traceback: ```text ``` validations: required: false - type: textarea attributes: label: Environment description: > Please paste the output of running `depinfo --markdown cobra` in your environment between the `details` tags below. value: |
validations: required: true - type: textarea attributes: label: Anything else? description: | Links? References? Anything that will give us more context about the issue you are encountering! Tip: You can attach images or log files by clicking this area to highlight it and then dragging files in. validations: required: false cobrapy-0.29.1/.github/ISSUE_TEMPLATE/02-feature-request.yaml000066400000000000000000000031611467301562300232210ustar00rootroot00000000000000 name: Feature request description: Suggest an idea for this project title: "[Feature] " labels: [enhancement] body: - type: checkboxes attributes: label: Checklist description: > Please make sure you check all these items before submitting your feature request. options: - label: There are [no similar issues or pull requests](https://github.com/opencobra/cobrapy/issues) for this yet. required: true - type: textarea attributes: label: Problem description: > A clear and concise description of what you are trying to achieve. placeholder: > "I want to be able to [...] but I can't because [...]". validations: required: false - type: textarea attributes: label: Solution description: > A clear and concise description of what you would want to happen. For API changes, try to provide a code snippet of what you would like the new API to look like. validations: required: false - type: textarea attributes: label: Alternatives description: > Please describe any alternative solutions or features you've considered to solve your problem and why they didn't help. validations: required: false - type: textarea attributes: label: Anything else? description: > Provide any additional context, screenshots, tracebacks, etc. about the feature here. validations: required: false cobrapy-0.29.1/.github/ISSUE_TEMPLATE/config.yml000066400000000000000000000011061467301562300207620ustar00rootroot00000000000000# Ref: https://help.github.com/en/github/building-a-strong-community/configuring-issue-templates-for-your-repository#configuring-the-template-chooser blank_issues_enabled: true contact_links: - name: Question url: https://github.com/opencobra/cobrapy/discussions about: > Please ask general questions about constraint-based modeling or COBRApy specifically here. - name: 💬 Gitter url: https://gitter.im/opencobra/cobrapy about: > Please ask more specific programming questions in this Gitter channel or discuss ideas and feature requests with the developers. cobrapy-0.29.1/.github/PULL_REQUEST_TEMPLATE.md000066400000000000000000000002371467301562300204140ustar00rootroot00000000000000* [ ] fix #(issue number) * [ ] description of feature/fix * [ ] tests added/passed * [ ] add an entry to the [next release](../release-notes/next-release.md) cobrapy-0.29.1/.github/SUPPORT.rst000066400000000000000000000015501467301562300165200ustar00rootroot00000000000000======= Support ======= * The mailing list is a good place to ask about general modeling questions. Please use Google's excellent search capabilities before posting a new question. By writing a well formulated question, with sufficient detail, you are much more likely to quickly receive a good answer! Please refer to these `StackOverflow guidelines `_ on how to ask questions. `Google Group `_ * There is the cobrapy `gitter chat `_ where you can ask questions as well. It is a bit more developer-centric than the mailing list. * You can post questions in `GitHub issues `_, too. Please reserve this channel for code problems and reserve conceptual questions for the mailing list. cobrapy-0.29.1/.github/workflows/000077500000000000000000000000001467301562300166465ustar00rootroot00000000000000cobrapy-0.29.1/.github/workflows/biosimulators_dispatch.yml000066400000000000000000000010021467301562300241350ustar00rootroot00000000000000name: BioSimulators Dispatch on: release: types: [published] jobs: dispatch: runs-on: ubuntu-latest steps: - name: Dispatch new release version to BioSimulators uses: benc-uk/workflow-dispatch@v1 with: workflow: Continuous integration repo: biosimulators/Biosimulators_COBRApy ref: dev token: ${{ secrets.BIOSIM_DISPATCH_TOKEN }} inputs: '{ "simulatorVersion": "${{ github.ref_name }}", "simulatorVersionLatest": "true" }' cobrapy-0.29.1/.github/workflows/cron.yml000066400000000000000000000002311467301562300203260ustar00rootroot00000000000000name: Cron Test on: schedule: # Run every Tuesday at 10:30. - cron: '30 10 * * 2' jobs: prerequisites: uses: ./.github/workflows/test.yml cobrapy-0.29.1/.github/workflows/main.yml000066400000000000000000000002731467301562300203170ustar00rootroot00000000000000name: CI on: push: branches: - stable - devel pull_request: branches: - stable - devel jobs: prerequisites: uses: ./.github/workflows/test.yml cobrapy-0.29.1/.github/workflows/release.yml000066400000000000000000000030231467301562300210070ustar00rootroot00000000000000name: CD on: push: tags: - "[0-9]+.[0-9]+.[0-9]+" - "[0-9]+.[0-9]+.[0-9]+(a|b|rc|post|dev)[0-9]+" jobs: prerequisites: uses: ./.github/workflows/test.yml release: needs: [prerequisites] strategy: matrix: os: [ubuntu-latest] python-version: ["3.11"] runs-on: ${{ matrix.os }} permissions: # Write permissions are needed to create OIDC tokens. id-token: write # Write permissions are needed to make GitHub releases. contents: write steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip setuptools wheel python -m pip install build - name: Build package run: python -m build # We rely on a trusted publisher configuration being present on PyPI, # see https://docs.pypi.org/trusted-publishers/. - name: Publish to PyPI uses: pypa/gh-action-pypi-publish@release/v1 - name: GH release uses: softprops/action-gh-release@v2 with: body_path: "release-notes/${{ github.ref_name }}.md" draft: false prerelease: false - name: Publish to website run: ./scripts/deploy_website.sh shell: bash env: TAG: ${{ github.ref_name }} WORKSPACE: ${{ github.workspace }} WEBSITE_DEPLOY_TOKEN: ${{ secrets.WEBSITE_DEPLOY_TOKEN }} cobrapy-0.29.1/.github/workflows/test.yml000066400000000000000000000030411467301562300203460ustar00rootroot00000000000000name: Test Suite on: workflow_dispatch: {} workflow_call: {} jobs: lint: strategy: fail-fast: false matrix: os: [ubuntu-latest] python-version: ["3.11"] runs-on: ${{ matrix.os }} timeout-minutes: 60 steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip setuptools wheel python -m pip install tox tox-gh-actions - name: isort run: tox -e isort - name: black run: tox -e black - name: flake8 run: tox -e flake8 test: runs-on: ${{ matrix.os }} strategy: fail-fast: false matrix: os: [ubuntu-latest, macos-latest, windows-latest] python-version: ["3.8", "3.11"] timeout-minutes: 360 steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip setuptools wheel python -m pip install tox tox-gh-actions - name: safety run: tox -e safety - name: Test with tox run: tox -- --benchmark-skip timeout-minutes: 60 - name: Report coverage shell: bash run: bash <(curl -s https://codecov.io/bash) cobrapy-0.29.1/.gitignore000066400000000000000000000114501467301562300152420ustar00rootroot00000000000000# Created by .ignore support plugin (hsz.mobi) ### macOS template # General .DS_Store .AppleDouble .LSOverride # Icon must end with two \r Icon # Thumbnails ._* # Files that might appear in the root of a volume .DocumentRevisions-V100 .fseventsd .Spotlight-V100 .TemporaryItems .Trashes .VolumeIcon.icns .com.apple.timemachine.donotpresent # Directories potentially created on remote AFP share .AppleDB .AppleDesktop Network Trash Folder Temporary Items .apdisk ### Linux template *~ # temporary files which can be created if a process still has a handle open of a deleted file .fuse_hidden* # KDE directory preferences .directory # Linux trash folder which might appear on any partition or disk .Trash-* # .nfs files are created when an open file is removed but is still being accessed .nfs* ### Windows template # Windows thumbnail cache files Thumbs.db Thumbs.db:encryptable ehthumbs.db ehthumbs_vista.db # Dump file *.stackdump # Folder config file [Dd]esktop.ini # Recycle Bin used on file shares $RECYCLE.BIN/ # Windows Installer files *.cab *.msi *.msix *.msm *.msp # Windows shortcuts *.lnk ### JupyterNotebooks template # gitignore template for Jupyter Notebooks # website: http://jupyter.org/ .ipynb_checkpoints */.ipynb_checkpoints/* # IPython profile_default/ ipython_config.py # Remove previous ipynb_checkpoints # git rm -r .ipynb_checkpoints/ ### JetBrains template # Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 # User-specific stuff .idea/ # Generated files .idea/**/contentModel.xml # Sensitive or high-churn files .idea/**/dataSources/ .idea/**/dataSources.ids .idea/**/dataSources.local.xml .idea/**/sqlDataSources.xml .idea/**/dynamic.xml .idea/**/uiDesigner.xml .idea/**/dbnavigator.xml # Gradle .idea/**/gradle.xml .idea/**/libraries # Gradle and Maven with auto-import # When using Gradle or Maven with auto-import, you should exclude module files, # since they will be recreated, and may cause churn. Uncomment if using # auto-import. # .idea/artifacts # .idea/compiler.xml # .idea/jarRepositories.xml # .idea/modules.xml # .idea/*.iml # .idea/modules # *.iml # *.ipr # CMake cmake-build-*/ # Mongo Explorer plugin .idea/**/mongoSettings.xml # File-based project format *.iws # IntelliJ out/ # mpeltonen/sbt-idea plugin .idea_modules/ # JIRA plugin atlassian-ide-plugin.xml # Cursive Clojure plugin .idea/replstate.xml # Crashlytics plugin (for Android Studio and IntelliJ) com_crashlytics_export_strings.xml crashlytics.properties crashlytics-build.properties fabric.properties # Editor-based Rest Client .idea/httpRequests # Android studio 3.1+ serialized cache file .idea/caches/build_file_checksums.ser ### Python template # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ pip-wheel-metadata/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # custom gurobi.log /documentation /documentation_builder/test*\.* /.benchmarks /.testmondata cobrapy-0.29.1/.readthedocs.yaml000066400000000000000000000014061467301562300165010ustar00rootroot00000000000000# .readthedocs.yaml # Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 # Set the version of Python and other tools you might need build: os: ubuntu-22.04 tools: python: "3.10" # You can also specify other tool versions: # nodejs: "16" # rust: "1.55" # golang: "1.17" # Build documentation in the docs/ directory with Sphinx sphinx: configuration: documentation_builder/conf.py # If using Sphinx, optionally build your docs in additional formats such as PDF # formats: # - pdf # Optionally declare the Python requirements required to build your docs python: install: - requirements: documentation_builder/requirements.txt - method: pip path: . cobrapy-0.29.1/INSTALL.rst000066400000000000000000000035351467301562300151170ustar00rootroot00000000000000======================= Installation of COBRApy ======================= For installation help, please use the `Google Group `_. For usage instructions, please see the `documentation `_. We only test against Python 3.7+, however, Python 3.4 or higher work mostly. For Windows users and possibly also Mac OS users, we recommend using the `Anaconda Python `_ distribution. Stable version installation =========================== COBRApy can be installed with any recent installation of pip. Instructions for several operating systems are below: Mac OS X or Linux ----------------- 1. We highly recommend that you create a `Python virtual environment `_. 2. Install COBRApy when an environment is active by running ``pip install cobra`` in the terminal. Microsoft Windows ----------------- If you heed our recommendation to use Anaconda, you can open an Anaconda shell and install COBRApy from the ``conda-forge`` channel. .. code-block:: console conda install -c conda-forge cobra Installation for development ============================ Get the `detailed contribution instructions <.github/CONTRIBUTING.rst>`_ for contributing to COBRApy. Solvers ======= COBRApy uses `optlang `_ to interface the mathematical solvers used to optimize the created COBRA models. At the time of writing the supported solvers are: - ILOG/CPLEX (available with `Academic `_ and `Commercial `_ licenses) - `Gurobi `_ - `GLPK `_ which is automatically installed as swiglpk cobrapy-0.29.1/LICENSE000066400000000000000000001155401467301562300142640ustar00rootroot00000000000000 GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The licenses for most software are designed to take away your freedom to share and change it. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change free software--to make sure the software is free for all its users. This General Public License applies to most of the Free Software Foundation's software and to any other program whose authors commit to using it. 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END OF TERMS AND CONDITIONS cobrapy-0.29.1/MANIFEST.in000066400000000000000000000000471467301562300150100ustar00rootroot00000000000000include README.rst INSTALL.rst LICENSE cobrapy-0.29.1/README.rst000066400000000000000000000121641467301562300147440ustar00rootroot00000000000000================================================================ COBRApy - Constraint-Based Reconstruction and Analysis in Python ================================================================ .. image:: https://img.shields.io/pypi/v/cobra.svg :target: https://pypi.org/project/cobra/ :alt: Current PyPI Version .. image:: https://img.shields.io/pypi/pyversions/cobra.svg :target: https://pypi.org/project/cobra/ :alt: Supported Python Versions .. image:: https://img.shields.io/pypi/l/cobra.svg :target: https://www.gnu.org/licenses/old-licenses/lgpl-2.0.html :alt: GNU Lesser General Public License 2 or later .. image:: https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg :target: https://github.com/opencobra/cobrapy/blob/devel/.github/CODE_OF_CONDUCT.md :alt: Code of Conduct .. image:: https://github.com/opencobra/cobrapy/actions/workflows/main.yml/badge.svg :target: https://github.com/opencobra/cobrapy/actions/workflows/main.yml :alt: GitHub Actions CI/CD Status .. image:: https://codecov.io/gh/opencobra/cobrapy/branch/master/graph/badge.svg :target: https://codecov.io/gh/opencobra/cobrapy :alt: Codecov .. image:: https://readthedocs.org/projects/cobrapy/badge/?version=latest :target: https://cobrapy.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status .. image:: https://badges.gitter.im/opencobra/cobrapy.svg :target: https://gitter.im/opencobra/cobrapy :alt: Gitter Chat Room .. image:: https://img.shields.io/badge/code%20style-black-000000.svg :target: https://github.com/ambv/black :alt: Black .. image:: https://zenodo.org/badge/6510063.svg :target: https://zenodo.org/badge/latestdoi/6510063 :alt: Zenodo DOI What is COBRApy? ================ COBRA methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. **COBRApy** is a constraint-based modeling package that is designed to accommodate the biological complexity of the next generation of COBRA models and provides access to commonly used COBRA methods, such as flux balance analysis, flux variability analysis, and gene deletion analyses. Our aim with COBRApy is to provide useful, efficient infrastructure for: - creating and managing metabolic models - accessing popular solvers - analyzing models with methods such as FVA, FBA, pFBA, MOMA etc. - inspecting models and drawing conclusions on gene essentiality, testing consequences of knock-outs etc. Our goal with COBRApy is for it to be useful on its own, and for it to be the natural choice of infrastructure for developers that want to build new COBRA related python packages for e.g. visualization, strain-design and data driven analysis. By re-using the same classes and design principles, we can make new methods both easier to implement and easier to use, thereby bringing the power of COBRA to more researchers. The documentation is browseable online at `readthedocs `_ and can also be `downloaded `_. Please use the `Google Group `_ for help. By writing a well formulated question, with sufficient detail, you are much more likely to quickly receive a good answer! Please refer to these `StackOverflow guidelines `_ on how to ask questions. Alternatively, you can use `gitter.im `_ for quick questions and discussions about COBRApy (faster response times). Please keep in mind that answers are provided on a volunteer basis. More information about opencobra is available at the `website `_. If you use COBRApy in a scientific publication, please cite `doi:10.1186/1752-0509-7-74 `_ Installation ============ Use pip to `install COBRApy from PyPI `_ (we recommend doing this inside a `virtual environment `_):: pip install cobra If you want to load MATLAB models, you will need additional dependencies. Please install:: pip install cobra[array] For further information, please follow the `detailed installation instructions `_. Contributing ============ Contributions are always welcome! Please read the `contributing guidelines `_ to get started. License ======= The COBRApy source is released under both the GPL and LGPL licenses version 2 or later. You may choose which license you choose to use the software under. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License or the GNU Lesser General Public License as published by the Free Software Foundation, either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. cobrapy-0.29.1/benchmarks/000077500000000000000000000000001467301562300153665ustar00rootroot00000000000000cobrapy-0.29.1/benchmarks/cobra-cameo-merge-benchmarks-before-merge.ipynb000066400000000000000000000161321467301562300263510ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import cobra\n", "from cobra.io import read_sbml_model, load_model\n", "from cobra import Model" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cobra.__path__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0.24.0'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cobra.__version__" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "benchmark_results = {}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling...\n", " A: min|aij| = 1.000e+00 max|aij| = 1.000e+00 ratio = 1.000e+00\n", "Problem data seem to be well scaled\n" ] } ], "source": [ "m = load_model('iJO1366')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12 s ± 1.45 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -o\n", "m2 = Model()\n", "for r in m.reactions:\n", " m2.add_reaction(r)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "benchmark_results['add_reaction'] = _" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "565 ms ± 83 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -o\n", "m2 = Model()\n", "m2.add_reactions(m.reactions)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "benchmark_results['add_reactions'] = _" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra/core/group.py:148: UserWarning: need to pass in a list\n", " warn(\"need to pass in a list\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "7.06 s ± 368 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -o\n", "model = load_model('iJO1366')\n", "for reaction in model.reactions:\n", " model.remove_reactions([reaction])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "benchmark_results['remove_reaction'] = _" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "291 ms ± 18.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -o\n", "model = load_model('e_coli_core')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "benchmark_results['load_model'] = _" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "model = load_model('e_coli_core')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "133 ms ± 5.07 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -o\n", "for reaction in model.reactions:\n", " model.objective = reaction\n", " solution = model.optimize()\n", " solution.objective_value" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "benchmark_results['iteratete_all_reactions_set_objective_solve'] = _" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15.9 ms ± 1.55 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -o\n", "model.copy()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "benchmark_results['model_copy'] = _" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 } cobrapy-0.29.1/benchmarks/cobra-cameo-merge-benchmarks.ipynb000066400000000000000000000207631467301562300240210ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import cobra\n", "from cobra.io import load_model\n", "from cobra import Model" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cobra.__path__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0.24.0'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cobra.__version__" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "benchmark_results = {}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling...\n", " A: min|aij| = 1.000e+00 max|aij| = 1.000e+00 ratio = 1.000e+00\n", "Problem data seem to be well scaled\n" ] } ], "source": [ "m = load_model('iJO1366')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "m.solver = 'glpk'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "11.3 s ± 1.26 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -o\n", "m2 = Model()\n", "for r in m.reactions:\n", " m2.add_reaction(r)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "benchmark_results['add_reaction'] = _" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "726 ms ± 33.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -o\n", "m2 = Model()\n", "m2.add_reactions(m.reactions)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "benchmark_results['add_reactions'] = _" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.82 s ± 585 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -o\n", "model = load_model('iJO1366')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8.47 s ± 1.98 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -o\n", "model = load_model('iJO1366')\n", "for reaction in model.reactions:\n", " model.remove_reactions([reaction])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "benchmark_results['remove_reaction'] = _" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7.93 s ± 508 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -o\n", "model = load_model('iJO1366')\n", "model.remove_reactions(model.reactions)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "benchmark_results['remove_reactions'] = _" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "model = load_model('iJO1366')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1min 20s ± 11.3 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -o\n", "for reaction in model.reactions:\n", " model.objective = reaction\n", " solution = model.optimize()\n", " solution.fluxes" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "benchmark_results['iterate_all_reactions_set_objective_optimize'] = _" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "22 s ± 1.59 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -o\n", "for reaction in model.reactions:\n", " model.objective = reaction\n", " solution = model.slim_optimize() # If you only care about the flux of the objective\n", " solution" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "benchmark_results['iterate_all_reactions_set_objective_slim_optimize'] = _" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "688 ms ± 88 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%timeit -o\n", "model.copy()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "benchmark_results['model_copy'] = _" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 } cobrapy-0.29.1/benchmarks/geometric_fba.ipynb000066400000000000000000000275121467301562300212260ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import logging" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "logging.basicConfig(level=\"DEBUG\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from cobra.io import load_model" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from cobra.flux_analysis import geometric_fba" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:cobra.core.model:The current solver interface glpk doesn't support setting the optimality tolerance.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Scaling...\n", " A: min|aij| = 1.000e+00 max|aij| = 1.000e+00 ratio = 1.000e+00\n", "Problem data seem to be well scaled\n" ] } ], "source": [ "model = load_model(\"textbook\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "model.solver = \"cplex\"" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "logging.getLogger().setLevel(logging.DEBUG)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:cobra.flux_analysis.geometric:Iteration: 1; delta: 4e-13; status: optimal.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 481 ms, sys: 0 ns, total: 481 ms\n", "Wall time: 480 ms\n" ] }, { "data": { 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" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time geometric_fba(model)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 } cobrapy-0.29.1/benchmarks/objective_benchmark.ipynb000066400000000000000000000135271467301562300224250ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from cobra.io import read_sbml_model" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from optlang.symbolics import Zero, One, add, mul, Add, Mul, Real" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "one = Real(1.0)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "model = read_sbml_model(\"/home/moritz/Projects/memote/Models/iJO1366.xml.gz\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import cobra.util.solver as sutil" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from itertools import chain" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "model.solver = \"glpk\"" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "reaction_variables = ((rxn.forward_variable, rxn.reverse_variable)\n", " for rxn in model.reactions)\n", "variables = list(chain(*reaction_variables))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "146 ms ± 3.94 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "%%timeit\n", "with model:\n", " model.objective = model.problem.Objective(\n", " Zero, direction='min', sloppy=True,\n", " name=\"_pfba_objective\")\n", " model.objective.set_linear_coefficients({v: 1.0 for v in variables})" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "149 ms ± 2.13 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "%%timeit\n", "with model:\n", " model.objective = model.problem.Objective(\n", " add([mul(one, v) for v in variables]),\n", " direction='min', sloppy=True, name=\"_pfba_objective\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "model.solver = \"gurobi\"" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "reaction_variables = ((rxn.forward_variable, rxn.reverse_variable)\n", " for rxn in model.reactions)\n", "variables = list(chain(*reaction_variables))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "13.7 ms ± 269 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%%timeit\n", "with model:\n", " model.objective = model.problem.Objective(\n", " Zero, direction='min', sloppy=True,\n", " name=\"_pfba_objective\")\n", " model.objective.set_linear_coefficients({v: 1.0 for v in variables})" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "218 ms ± 2.55 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%%timeit\n", "with model:\n", " model.objective = model.problem.Objective(\n", " add([mul(one, v) for v in variables]),\n", " direction='min', sloppy=True, name=\"_pfba_objective\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "model.solver = \"cplex\"" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "reaction_variables = ((rxn.forward_variable, rxn.reverse_variable)\n", " for rxn in model.reactions)\n", "variables = list(chain(*reaction_variables))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.04 s ± 32.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%%timeit\n", "with model:\n", " model.objective = model.problem.Objective(\n", " Zero, direction='min', sloppy=True,\n", " name=\"_pfba_objective\")\n", " model.objective.set_linear_coefficients({v: 1.0 for v in variables})" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "183 ms ± 5.28 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%%timeit\n", "with model:\n", " model.objective = model.problem.Objective(\n", " add([mul(one, v) for v in variables]),\n", " direction='min', sloppy=True, name=\"_pfba_objective\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.5" } }, "nbformat": 4, "nbformat_minor": 2 } cobrapy-0.29.1/benchmarks/parallel_fva.ipynb000066400000000000000000000221271467301562300210650ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import logging" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "logging.basicConfig(level=\"INFO\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from cobra.io import load_model" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from cobra.flux_analysis import flux_variability_analysis" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:cobra.core.model:The current solver interface glpk doesn't support setting the optimality tolerance.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Scaling...\n", " A: min|aij| = 1.000e+00 max|aij| = 1.000e+00 ratio = 1.000e+00\n", "Problem data seem to be well scaled\n" ] } ], "source": [ "model = load_model(\"iJO1366\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 7.15 s, sys: 119 ms, total: 7.27 s\n", "Wall time: 7.55 s\n" ] }, { "data": { "text/html": [ "
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minimummaximum
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minimummaximum
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growthstatus
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" ], "text/plain": [ " growth status\n", "ids \n", "(b1779) 5.034815e+00 optimal\n", "(b2276) 5.475871e+00 optimal\n", "(b0114) 1.224159e+00 optimal\n", "(b0008) -5.857507e-17 optimal" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time single_gene_deletion(model, method=\"linear room\", gene_list=genes, processes=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 } cobrapy-0.29.1/benchmarks/single_gene_deletion_room.ipynb000066400000000000000000000245301467301562300236330ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import logging" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "logging.basicConfig(level=\"DEBUG\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:optlang.util:Gurobi python bindings not available.\n", "DEBUG:optlang.util:GLPK python bindings found at /Users/uridavidakavia/PycharmProjects/venv_cobrapy3_7/lib/python3.7/site-packages/swiglpk\n", "DEBUG:optlang.util:Mosek python bindings not available.\n", "DEBUG:optlang.util:CPLEX python bindings not available.\n", "DEBUG:optlang.util:OSQP python bindings not available.\n", "DEBUG:optlang.util:COINOR_CBC python bindings not available.\n", "DEBUG:optlang.util:Scipy linprog function found at /Users/uridavidakavia/PycharmProjects/venv_cobrapy3_7/lib/python3.7/site-packages/scipy/optimize/__init__.py\n" ] } ], "source": [ "from cobra.io import load_model" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from cobra.flux_analysis import single_gene_deletion" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:cobra.core.model:The current solver interface glpk doesn't support setting the optimality tolerance.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Scaling...\n", " A: min|aij| = 1.000e+00 max|aij| = 1.000e+00 ratio = 1.000e+00\n", "Problem data seem to be well scaled\n" ] } ], "source": [ "model = load_model(\"textbook\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "genes = ['b0008', 'b0114', 'b2276', 'b1779']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "model.solver = \"cplex\"" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "logging.getLogger().setLevel(logging.DEBUG)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:Warning: No solution found from 6 MIP starts.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 21.4 s, sys: 84.2 ms, total: 21.5 s\n", "Wall time: 3.23 s\n" ] }, { "data": { "text/html": [ "
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SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = BUILDDIR = ../documentation AUTODIR = _autogen # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . # the i18n builder cannot share the environment and doctrees with the others I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . .PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext help: @echo "Please use \`make ' where is one of" @echo " html to make standalone HTML files" @echo " dirhtml to make HTML files named index.html in directories" @echo " singlehtml to make a single large HTML file" @echo " pickle to make pickle files" @echo " json to make JSON files" @echo " htmlhelp to make HTML files and a HTML help project" @echo " qthelp to make HTML files and a qthelp project" @echo " devhelp to make HTML files and a Devhelp project" @echo " epub to make an epub" @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" @echo " latexpdf to make LaTeX files and run them through pdflatex" @echo " text to make text files" @echo " man to make manual pages" @echo " texinfo to make Texinfo files" @echo " info to make Texinfo files and run them through makeinfo" @echo " gettext to make PO message catalogs" @echo " changes to make an overview of all changed/added/deprecated items" @echo " linkcheck to check all external links for integrity" @echo " doctest to run all doctests embedded in the documentation (if enabled)" clean: -rm -rf $(AUTODIR) -rm -rf $(BUILDDIR)/* html: # sphinx-apidoc -f -d 2 -e -o $(AUTODIR) ../cobra $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." singlehtml: $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml @echo @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle @echo @echo "Build finished; now you can process the pickle files." json: $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json @echo @echo "Build finished; now you can process the JSON files." htmlhelp: $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in $(BUILDDIR)/htmlhelp." qthelp: $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in $(BUILDDIR)/qthelp, like this:" @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/cobra.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/cobra.qhc" devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp @echo @echo "Build finished." @echo "To view the help file:" @echo "# mkdir -p $$HOME/.local/share/devhelp/cobra" @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/cobra" @echo "# devhelp" epub: $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub @echo @echo "Build finished. The epub file is in $(BUILDDIR)/epub." latex: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." @echo "Run \`make' in that directory to run these through (pdf)latex" \ "(use \`make latexpdf' here to do that automatically)." latexpdf: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo "Running LaTeX files through pdflatex..." $(MAKE) -C $(BUILDDIR)/latex all-pdf @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." text: $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text @echo @echo "Build finished. The text files are in $(BUILDDIR)/text." man: $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man @echo @echo "Build finished. The manual pages are in $(BUILDDIR)/man." texinfo: $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo @echo @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." @echo "Run \`make' in that directory to run these through makeinfo" \ "(use \`make info' here to do that automatically)." info: $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo @echo "Running Texinfo files through makeinfo..." make -C $(BUILDDIR)/texinfo info @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." gettext: $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale @echo @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." cobrapy-0.29.1/documentation_builder/building_model.ipynb000066400000000000000000000477701467301562300236670ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Building a Model\n", "## Model, Reactions and Metabolites" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "This simple example demonstrates how to create a model, create a reaction, and then add the reaction to the model.\n", "\n", "We'll use the '3OAS140' reaction from the STM_1.0 model:\n", "\n", "1.0 malACP[c] + 1.0 h[c] + 1.0 ddcaACP[c] $\\rightarrow$ 1.0 co2[c] + 1.0 ACP[c] + 1.0 3omrsACP[c]\n", "\n", "First, create the model and reaction." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "from cobra import Model, Reaction, Metabolite" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "model = Model('example_model')\n", "\n", "reaction = Reaction('R_3OAS140')\n", "reaction.name = '3 oxoacyl acyl carrier protein synthase n C140 '\n", "reaction.subsystem = 'Cell Envelope Biosynthesis'\n", "reaction.lower_bound = 0. # This is the default\n", "reaction.upper_bound = 1000. # This is the default" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We need to create metabolites as well. If we were using an existing model, we could use `Model.get_by_id` to get the appropriate Metabolite objects instead." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "ACP_c = Metabolite(\n", " 'ACP_c',\n", " formula='C11H21N2O7PRS',\n", " name='acyl-carrier-protein',\n", " compartment='c')\n", "omrsACP_c = Metabolite(\n", " 'M3omrsACP_c',\n", " formula='C25H45N2O9PRS',\n", " name='3-Oxotetradecanoyl-acyl-carrier-protein',\n", " compartment='c')\n", "co2_c = Metabolite('co2_c', formula='CO2', name='CO2', compartment='c')\n", "malACP_c = Metabolite(\n", " 'malACP_c',\n", " formula='C14H22N2O10PRS',\n", " name='Malonyl-acyl-carrier-protein',\n", " compartment='c')\n", "h_c = Metabolite('h_c', formula='H', name='H', compartment='c')\n", "ddcaACP_c = Metabolite(\n", " 'ddcaACP_c',\n", " formula='C23H43N2O8PRS',\n", " name='Dodecanoyl-ACP-n-C120ACP',\n", " compartment='c')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Side note: SId**\n", "\n", "It is highly recommended that the ids for reactions, metabolites and genes are valid SBML identifiers (`SId`). \n", "`SId` is a data type derived from the basic XML typestring, but with restrictions about the characters \n", "permitted and the sequences in which those characters may appear. \n", "```\n", "letter ::= ’a’..’z’,’A’..’Z’\n", "digit ::= ’0’..’9’\n", "idChar ::= letter | digit | ’_’\n", "SId ::= ( letter | ’_’ ) idChar*\n", "```\n", "The main limitation is that ids cannot start with numbers. Using `SId`s allows serialization to SBML. In addition\n", "features such as code completion and object access via the dot syntax will work in `cobrapy`." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Adding metabolites to a reaction uses a dictionary of the metabolites and their stoichiometric coefficients. A group of metabolites can be added all at once, or they can be added one at a time." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "'ddcaACP_c + h_c + malACP_c --> ACP_c + M3omrsACP_c + co2_c'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reaction.add_metabolites({\n", " malACP_c: -1.0,\n", " h_c: -1.0,\n", " ddcaACP_c: -1.0,\n", " co2_c: 1.0,\n", " ACP_c: 1.0,\n", " omrsACP_c: 1.0\n", "})\n", "\n", "reaction.reaction # This gives a string representation of the reaction" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The gene_reaction_rule is a boolean representation of the gene requirements for this reaction to be active as described in [Schellenberger et al 2011 Nature Protocols 6(9):1290-307](http://dx.doi.org/doi:10.1038/nprot.2011.308). We will assign the gene reaction rule string, which will automatically create the corresponding gene objects." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "frozenset({, })" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reaction.gene_reaction_rule = '( STM2378 or STM1197 )'\n", "reaction.genes" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "At this point in time, the model is still empty" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 reactions initially\n", "0 metabolites initially\n", "0 genes initially\n" ] } ], "source": [ "print(f'{len(model.reactions)} reactions initially')\n", "print(f'{len(model.metabolites)} metabolites initially')\n", "print(f'{len(model.genes)} genes initially')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We will add the reaction to the model, which will also add all associated metabolites and genes" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 reactions\n", "6 metabolites\n", "2 genes\n" ] } ], "source": [ "model.add_reactions([reaction])\n", "\n", "# The objects have been added to the model\n", "print(f'{len(model.reactions)} reactions')\n", "print(f'{len(model.metabolites)} metabolites')\n", "print(f'{len(model.genes)} genes')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We can iterate through the model objects to observe the contents" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reactions\n", "---------\n", "R_3OAS140 : ddcaACP_c + h_c + malACP_c --> ACP_c + M3omrsACP_c + co2_c\n", "\n", "Metabolites\n", "-----------\n", " malACP_c : C14H22N2O10PRS\n", " h_c : H\n", "ddcaACP_c : C23H43N2O8PRS\n", " co2_c : CO2\n", " ACP_c : C11H21N2O7PRS\n", "M3omrsACP_c : C25H45N2O9PRS\n", "\n", "Genes\n", "-----\n", "STM2378 is associated with reactions: {R_3OAS140}\n", "STM1197 is associated with reactions: {R_3OAS140}\n" ] } ], "source": [ "# Iterate through the the objects in the model\n", "print(\"Reactions\")\n", "print(\"---------\")\n", "for x in model.reactions:\n", " print(\"%s : %s\" % (x.id, x.reaction))\n", "\n", "print(\"\")\n", "print(\"Metabolites\")\n", "print(\"-----------\")\n", "for x in model.metabolites:\n", " print('%9s : %s' % (x.id, x.formula))\n", "\n", "print(\"\")\n", "print(\"Genes\")\n", "print(\"-----\")\n", "for x in model.genes:\n", " associated_ids = (i.id for i in x.reactions)\n", " print(\"%s is associated with reactions: %s\" %\n", " (x.id, \"{\" + \", \".join(associated_ids) + \"}\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Objective" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Last we need to set the objective of the model. Here, we just want this to be the maximization of the flux in the single reaction we added and we do this by assigning the reaction's identifier to the `objective` property of the model." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "model.objective = 'R_3OAS140'" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The created objective is a symbolic algebraic expression and we can examine it by printing it" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0*R_3OAS140 - 1.0*R_3OAS140_reverse_60acb\n", "max\n" ] } ], "source": [ "print(model.objective.expression)\n", "print(model.objective.direction)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "which here shows that the solver will maximize the flux in the forward direction." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Validation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For exchange with other tools you can validate and export the model to SBML.\n", "For more information on serialization and available formats see the section \"Reading and Writing Models\"" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(,\n", " {'COBRA_CHECK': [],\n", " 'COBRA_ERROR': [],\n", " 'COBRA_FATAL': [],\n", " 'COBRA_WARNING': [],\n", " 'SBML_ERROR': [],\n", " 'SBML_FATAL': [],\n", " 'SBML_SCHEMA_ERROR': [],\n", " 'SBML_WARNING': []})\n" ] } ], "source": [ "import tempfile\n", "from pprint import pprint\n", "from cobra.io import write_sbml_model, validate_sbml_model\n", "with tempfile.NamedTemporaryFile(suffix='.xml') as f_sbml:\n", " write_sbml_model(model, filename=f_sbml.name)\n", " report = validate_sbml_model(filename=f_sbml.name)\n", "\n", "pprint(report)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model is valid with no COBRA or SBML errors or warnings." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exchanges, Sinks and Demands\n", "Boundary reactions can be added using the model's method `add_boundary`.\n", "There are three different types of pre-defined boundary reactions: exchange, demand, and sink reactions. All of them are unbalanced pseudo reactions, that means they fulfill a function for modeling by adding to or removing metabolites from the model system but are not based on real biology. An exchange reaction is a reversible reaction that adds to or removes an extracellular metabolite from the extracellular compartment. A demand reaction is an irreversible reaction that consumes an intracellular metabolite. A sink is similar to an exchange but specifically for intracellular metabolites, i.e., a reversible reaction that adds or removes an intracellular metabolite." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "There are no boundary reactions in this model. Therefore specific types of boundary reactions such as 'exchanges', 'demands' or 'sinks' cannot be identified.\n", "There are no boundary reactions in this model. Therefore specific types of boundary reactions such as 'exchanges', 'demands' or 'sinks' cannot be identified.\n", "There are no boundary reactions in this model. Therefore specific types of boundary reactions such as 'exchanges', 'demands' or 'sinks' cannot be identified.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "exchanges []\n", "demands []\n", "sinks []\n" ] } ], "source": [ "print(\"exchanges\", model.exchanges)\n", "print(\"demands\", model.demands)\n", "print(\"sinks\", model.sinks)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Boundary reactions are defined on metabolites. First we add two metabolites to the model then\n", "we define the boundary reactions. We add glycogen to the cytosolic compartment `c` and CO2 to the external compartment `e`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "model.add_metabolites([\n", " Metabolite(\n", " 'glycogen_c',\n", " name='glycogen',\n", " compartment='c'\n", " ),\n", " Metabolite(\n", " 'co2_e',\n", " name='CO2',\n", " compartment='e'\n", " ),\n", "])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierEX_co2_e
NameCO2 exchange
Memory address0x07fef04703d90
Stoichiometry\n", "

co2_e <=>

\n", "

CO2 <=>

\n", "
GPR
Lower bound-1000.0
Upper bound1000.0
\n", " " ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create exchange reaction\n", "model.add_boundary(model.metabolites.get_by_id(\"co2_e\"), type=\"exchange\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierSK_glycogen_c
Nameglycogen sink
Memory address0x07fef046eb2e0
Stoichiometry\n", "

glycogen_c <=>

\n", "

glycogen <=>

\n", "
GPR
Lower bound-1000.0
Upper bound1000.0
\n", " " ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create exchange reaction\n", "model.add_boundary(model.metabolites.get_by_id(\"glycogen_c\"), type=\"sink\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "exchanges []\n", "sinks []\n", "demands []\n" ] } ], "source": [ "# Now we have an additional exchange and sink reaction in the model\n", "print(\"exchanges\", model.exchanges)\n", "print(\"sinks\", model.sinks)\n", "print(\"demands\", model.demands)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To create a demand reaction instead of a sink use type `demand` instead of `sink`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Information on all boundary reactions is available via the model's property `boundary`." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[,\n", " ]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# boundary reactions\n", "model.boundary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A neat trick to get all metabolic reactions is" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# metabolic reactions\n", "set(model.reactions) - set(model.boundary)" ] } ], "metadata": { "kernelspec": { "display_name": "cobrapy", "language": "python", "name": "cobrapy" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 } cobrapy-0.29.1/documentation_builder/conf.py000066400000000000000000000077121467301562300211360ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # cobra documentation build configuration file, created by # sphinx-quickstart on Wed Jun 13 19:17:34 2012. # # This file is execfile()d with the current directory set to its containing # dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys from os.path import dirname, join # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. SRC_PATH = join(dirname(dirname(__file__)), "src") sys.path.insert(0, SRC_PATH) # -- General configuration ---------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.intersphinx", "sphinx.ext.mathjax", "sphinx.ext.viewcode", "sphinx.ext.napoleon", "sphinx.ext.autosummary", "autoapi.extension", "nbsphinx", ] # Document Python Code autoapi_dirs = [join(SRC_PATH, "cobra")] autoapi_add_toctree_entry = False # Enable typehints autodoc_typehints = "signature" # Napoleon settings napoleon_numpy_docstring = True # The master toctree document. master_doc = "index" # General information about the project. project = "cobra" copyright = "2016-2022, The cobrapy core team" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # This import has to be here. from cobra import __version__ as release # noqa: E402 version = ".".join(release.split(".")[:2]) # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ["_build", ".ipynb_checkpoints"] pygments_style = "sphinx" # -- Options for HTML output -------------------------------------------------- mathjax_path = ( "https://cdn.mathjax.org/mathjax/latest/" "MathJax.js?config=TeX-AMS-MML_HTMLorMML" ) # -- Options for LaTeX output -------------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). "papersize": "a4paper", # The font size ('10pt', '11pt' or '12pt'). # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. "preamble": r"\usepackage{amsmath,amssymb}", } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ( "index", "cobra.tex", "cobra Documentation", "The cobrapy core team", "manual", ) ] # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ("index", "cobra", u"cobra Documentation", [u"The cobrapy core team"], 1) ] # -- Options for Texinfo output ------------------------------------------------ # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( "index", "cobra", "cobra Documentation", "The cobrapy core team", "cobra", "A package for constraints-based modeling of biological networks", "Miscellaneous", ) ] # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { "http://docs.python.org/": None, "http://docs.scipy.org/doc/numpy/": None, "http://docs.scipy.org/doc/scipy/reference": None, } intersphinx_cache_limit = 10 # days to keep the cached inventories cobrapy-0.29.1/documentation_builder/configuration.ipynb000066400000000000000000000252221467301562300235450ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Global Configuration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With cobra > 0.13.4, we introduce a global configuration object. For now, you can configure default reaction bounds and optimization solver which will be respected by newly created reactions and models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The configuration object" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can get a configuration object[1](#f1) in the following way:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import cobra" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "cobra_config = cobra.Configuration()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1The configuration object is a [singleton](https://en.wikipedia.org/wiki/Singleton_pattern). That means only one instance can exist and it is respected everywhere in COBRApy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reaction bounds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The object has the following attributes which you can inspect but also change as desired." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-1000.0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cobra_config.lower_bound" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1000.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cobra_config.upper_bound" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-1000.0, 1000.0)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cobra_config.bounds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Changing bounds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you modify the above values before creating a reaction they will be used." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "cobra_config.bounds = -10, 20" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierR1
Name
Memory address0x7f8642e5bba8
Stoichiometry\n", "

-->

\n", "

-->

\n", "
GPR
Lower bound0.0
Upper bound20
\n", " " ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cobra.Reaction(\"R1\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please note that by default reactions are irreversible. You can change this behavior by unsetting the lower bound argument." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierR2
Name
Memory address0x7f8642e5bda0
Stoichiometry\n", "

<=>

\n", "

<=>

\n", "
GPR
Lower bound-10
Upper bound20
\n", " " ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cobra.Reaction(\"R2\", lower_bound=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**N.B.: Most models define reaction bounds explicitly which takes precedence over the configured values.**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from cobra.io import load_model" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling...\n", " A: min|aij| = 1.000e+00 max|aij| = 1.000e+00 ratio = 1.000e+00\n", "Problem data seem to be well scaled\n" ] } ], "source": [ "model = load_model(\"textbook\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierACt2r
NameR acetate reversible transport via proton - symport
Memory address0x7f8642f174e0
Stoichiometry\n", "

ac_e + h_e <=> ac_c + h_c

\n", "

Acetate + H+ <=> Acetate + H+

\n", "
GPR
Lower bound-1000.0
Upper bound1000.0
\n", " " ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.reactions.ACt2r" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solver" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can define the default solver used by newly instantiated models. The default solver depends on your environment. In order we test for the availability of Gurobi, CPLEX, and GLPK. GLPK is assumed to always be present in the environment." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.solver" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Changing solver" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "cobra_config.solver = \"glpk_exact\"" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "new_model = load_model(\"textbook\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_model.solver" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Changing global configuration values is mostly useful at the beginning of a work session." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Table of Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 1 } cobrapy-0.29.1/documentation_builder/consistency.ipynb000066400000000000000000000124511467301562300232370ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Consistency testing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For most problems, multiple flux states can achieve the same optimum and thus we try to obtain a consistent network. By this, we mean that there will be mulitple blocked reactions in the network, which gives rise to this inconsistency. To solve this problem, we use algorithms which can detect all the blocked reactions and also give us consistent networks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us take a toy network, like so:\n", "\n", "\\begin{align}\n", "v_1 &: {} \\rightarrow 2A \\\\\n", "v_2 &: A \\leftrightarrow B \\\\\n", "v_3 &: A \\rightarrow D \\\\\n", "v_4 &: A \\rightarrow C \\\\\n", "v_5 &: C \\rightarrow D \\\\\n", "v_6 &: D \\rightarrow\n", "\\end{align}\n", "\n", "Here, $v_{x}$, where $x \\in \\{1, 2, \\ldots, 6\\}$ represent the flux carried by the reactions as shown above." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import cobra" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "unknown metabolite 'A' created\n", "unknown metabolite 'B' created\n", "unknown metabolite 'D' created\n", "unknown metabolite 'C' created\n" ] } ], "source": [ "test_model = cobra.Model(\"test_model\")\n", "v1 = cobra.Reaction(\"v1\")\n", "v2 = cobra.Reaction(\"v2\")\n", "v3 = cobra.Reaction(\"v3\")\n", "v4 = cobra.Reaction(\"v4\")\n", "v5 = cobra.Reaction(\"v5\")\n", "v6 = cobra.Reaction(\"v6\")\n", "\n", "test_model.add_reactions([v1, v2, v3, v4, v5, v6])\n", "\n", "v1.reaction = \"-> 2 A\"\n", "v2.reaction = \"A <-> B\"\n", "v3.reaction = \"A -> D\"\n", "v4.reaction = \"A -> C\"\n", "v5.reaction = \"C -> D\"\n", "v6.reaction = \"D ->\"\n", "\n", "v1.bounds = (0.0, 3.0)\n", "v2.bounds = (-3.0, 3.0)\n", "v3.bounds = (0.0, 3.0)\n", "v4.bounds = (0.0, 3.0)\n", "v5.bounds = (0.0, 3.0)\n", "v6.bounds = (0.0, 3.0)\n", "\n", "test_model.objective = v6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using FVA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first approach we can follow is to use FVA (Flux Variability Analysis) which among many other applications, is used to detect blocked reactions. The `cobra.flux_analysis.find_blocked_reactions()` function will return a list of all the blocked reactions obtained using FVA." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['v2']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cobra.flux_analysis.find_blocked_reactions(test_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we see above, we are able to obtain the blocked reaction, which in this case is $v_2$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using FASTCC" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second approach to obtaining consistent network in `cobrapy` is to use FASTCC. Using this method, you can expect to efficiently obtain an accurate consistent network. For more details regarding the algorithm, please see [Vlassis N, Pacheco MP, Sauter T (2014)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003424)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[,\n", " ,\n", " ,\n", " ,\n", " ]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "consistent_model = cobra.flux_analysis.fastcc(test_model)\n", "consistent_model.reactions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similar to the FVA approach, we are able to identify that $v_2$ is indeed the blocked reaction." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Table of Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 } cobrapy-0.29.1/documentation_builder/constraints_objectives.ipynb000066400000000000000000002010721467301562300254610ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tailored constraints, variables and objectives" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thanks to the use of symbolic expressions via the optlang mathematical modeling package, it is relatively straight-forward to add new variables, constraints and advanced objectives that cannot be easily formulated as a combination of different reaction and their corresponding upper and lower bounds. Here we demonstrate this optlang functionality which is exposed via the `model.solver.interface`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constraints" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose we want to ensure that two reactions have the same flux in our model. We can add this criteria as constraint to our model using the optlang solver interface by simply defining the relevant expression as follows." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from cobra.io import load_model\n", "model = load_model('textbook')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "same_flux = model.problem.Constraint(\n", " model.reactions.FBA.flux_expression - model.reactions.NH4t.flux_expression,\n", " lb=0,\n", " ub=0)\n", "model.add_cons_vars(same_flux)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The flux for our reaction of interest is obtained by the `model.reactions.FBA.flux_expression` which is simply the sum of the forward and reverse flux, i.e.," ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0*FBA - 1.0*FBA_reverse_84806" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.reactions.FBA.flux_expression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now I can maximize growth rate whilst the fluxes of reactions 'FBA' and 'NH4t' are constrained to be (near) identical." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.662749047738151 4.662749047738149 0.855110960926157\n" ] } ], "source": [ "solution = model.optimize()\n", "print(solution.fluxes['FBA'], solution.fluxes['NH4t'],\n", " solution.objective_value)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to add many constraints at once. For large models, with constraints involving many reactions, the efficient way to do this is to first build a dictionary of the linear coefficients for every flux, and then add the constraint at once. For example, suppose we want to add a constrain on the sum of the absolute values of every flux in the network to be less than 100:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "coefficients = dict()\n", "for rxn in model.reactions:\n", " coefficients[rxn.forward_variable] = 1.\n", " coefficients[rxn.reverse_variable] = 1.\n", "constraint = model.problem.Constraint(0, lb=0, ub=100)\n", "model.add_cons_vars(constraint)\n", "model.solver.update()\n", "constraint.set_linear_coefficients(coefficients=coefficients)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Objectives\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simple objective such as the maximization of the flux through one or more reactions can conveniently be done by simply \n", "assigning to the `model.objective` property as we have seen in previous chapters, e.g.," ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8739215069684307\n" ] } ], "source": [ "model = load_model('textbook')\n", "with model:\n", " model.objective = {model.reactions.Biomass_Ecoli_core: 1}\n", " model.optimize()\n", " print(model.reactions.Biomass_Ecoli_core.flux)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The objectives mathematical expression is seen by" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0*Biomass_Ecoli_core - 1.0*Biomass_Ecoli_core_reverse_2cdba" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.objective.expression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But suppose we need a more complicated objective, such as minimizing the Euclidean distance of the solution to the origin minus another variable, while subject to additional linear constraints. This is an objective function with both linear and quadratic components. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider the example problem:\n", "\n", "> **min** $\\frac{1}{2}\\left(x^2 + y^2 \\right) - y$\n", "\n", "> *subject to*\n", "\n", "> $x + y = 2$\n", "\n", "> $x \\ge 0$\n", "\n", "> $y \\ge 0$\n", "\n", "This (admittedly very artificial) problem can be visualized graphically where the optimum is indicated by the blue dot on the line of feasible solutions." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/pdf": 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37DObJYaaDF2oXsKfTznJaTKsCR1w3ASrY83CFljv7hsCpWMdjTuE1KYrTjB6XvDfLPxei/6e9PPJIIm+pv0ABpFB65IrvEUzpbBNKQpVIgy8zumHaubyxhBIxE89+QeRXgx8UiPi7BWpA1JivcLqiFIljs6vtHlFHumkuiZZro5grwHPwAJO1cYpzqg+azsUuItDFfqC+qcDwqUruNc1FEKiz3AHhz9+OlSQVtXpVHydrxI3wvOho52RRwqBFsChdBmdD+3WVg5isbvKEFQbEUOtdhAO+nOZDD2URTr5ICIvtFXEhu6zumfHfAGokiwQsbVjJ1VzGVvNHhHqOvYbEEMEc5e/Yf+spKFuIHw6o2bc95ZHQBrV+5hVjpSsV9Xb76A6P9Fl+Q7pk2W7eu1GoXo33b2WCgi4pC87zmdHLELb4eqUayDkkMqHSnFOPHx7QAPHn5IJcAYIOZzeqWSbrbJ0pln9wbgcxOtdzJOFACtKYrlImA7LhGRACbBWrtAkQlYMQcASRio3Q/AeZb2Elqp35fITkRawQmhVkCTX44ZKK5FuEAS4tfflzWSpaoVKMO1dwxuSvIcXEEN0QryeCOJdIRlL66oU59I3OC/eCwjwZtmgIu00mjrAU4E0szHFmtBYDe6LDFxGRsIB7wS9cJ/cPLlUroxX7d/2MLOcIo40Y4IZQGgBQG8+SrQcoK+i+XCeE7gtuOeuQvOmVlV02IaaEk0pz1BXGXacgZiZcIHT4Kah9nELeCWh0Tnz97zvKKe+vdUbK4+uDo2kaEhzkSqA0TWqAZ7WSDIKNMMz+jTIr0M+QJpkiThvptgdORKvRlwrlyfQ5iEaN5qbrYLhd0ZSJ2n/4oQET4d2wXublauZLJFQyl3EElJHUIfVUVPjZ26YVWaCYniLlUIhgRAsqHtOFEl9ijJVrUTyFzwbn8jRDFWJak8ClUNhfXiIkwnYQ1wuszafhbvMoq2pqtiBxaqKl3W4pnHUGoqRv5cdDTBcBw8lI4ewVCcmb1iRIHBSSoJFqJIJXslSXXeKBoqaFChn0+8Go2rZGklRyqxUugWtIOOSXie5KF8iSVcmVcUNnpZBuPRLTi/1S5vzMfxn1nLoA/bC1Z1i5bFSWkcYQoJD8K58gRC75GW3utzOwfclUjUApvoQiy3FrCO7hnsaf3XKYGCC71II2hFzAwRJ3pxtAA5r3E5XluYNZnCwaxBwiaoaLlCpCWO7sI23XGYyZcKhUTl0Mswd3QD2dsRq1ETlYhWP1IBCJ61AUY38eFxk+Fyae6sulJTpUpN7vIf25KEjym/SLl2TFcc+4RFnQ06McN5jvzC9HTqM699xAEJtxOI/Yf+sfNXUPskxYAHKsygI4gfbQVqtRElO26VHMqM8TnogmoQbdA5UVhWOX5ZUUWCi6oLfGhvJn9WdKy4TboOBJjEgsV6jdY5u7o5BLYJgVzoRyYzUr+0akYnEVuV0BEwkXWCYmEZHjLsjGoM7hCqruqarokObMeQjjkXyScZvMeM6xKRhgKQygsSd0Hodr1KDi31znJIPt+Obeg5LxPPFhfkiyQkFE4Vy/ND8CgcwQC6igq4Kj/kSTMNtbXl9Ba+vDG8OVVmcZOBPJ10m2+N1AQ9lDizjkJtb5vJdVQDIF096xyMRAtxbmppbrAMpxJyiiEgg/KZzza0+lbOlriodKOvCrbTp1k52jfDscpXgIB71WbtUgqsGLp+UqNeooKMU87SSIUdFUo9yovUm69HQFE9W94Fr2yE3lDbe0kSaUaaKwrDmHJrYnszg1M+2OAHrcInPXIzH1ITgx9oF5mudvib5qQZ51Ojn72NAi9UAd4Bs00Ejb65xKVIZ7tY8D5PfG7mLnjbHYTH8bSrXwQz7nraH6ogI2W4z+LB8RtyPTSiaIpQDDzLPGiQNw7pS1pIyiJ1OB7SoaIhZ01DsUDm9TjWKlexU6ZUcebxanAAcuKAebnIW6hYjKZd0G0jtLCH6lEMeonmookZwC8xXvRDZnuEZdkb6IE5aFyeNwol7VSzfh6yIRqUOIlnA0aipGisZR10mUzlWDvLJBlM9Edok557Ha8N5lNFZJpYVkZn0pCkFr9xSTYRw/qw64VThWRQJmUe32LlFJ+mY5khyvRw5R3F/nNQnpkd7O9UWTRWBVeJr8SX7mftRwsSf+hv4PH7PAvOhDqRAUZF7q1KqTvAF+ltKwoQ4zRK2d42hDLWT0YzcJBJYTx6W6YYfuKDJjNE+ABfQgJDZbX8AOud2qr9+AEY1hjnAUL43IAUGppKG9QF40TutxPTVHae7Gvvgw/yA6wq+wRICv994QNISqOjz2G+8sZHFB9d/3v5ZOW6oLbunWvriOBLeZI7TmNP2uJA6zlMJu995mDG81ZrMRJ3GUaoypKLejNxrlCgrq+klnlArQ92TNHPsqeYH+hkPG+qixZSiSq2qdeRolKC5AkqVOwOa6vx6rIRotRr0gcAhqt6qUqDqVdg1VTldQlNFaMbInMcnuekcbsxyFP06Ye6o/oXYDHFV5uU/SspccSOezsUuFmmsZelrBPg5lI2yoaoxninNWy62VYl6jeySmZghQktdBfyca6ZbENFxdMRNTWyo+aK68WFP9HlWNtNUJ0vtyiR5HyGlh8/6qZxqoiNvEi22Et2do4JMwqg5RGBJg6zJIzcHcQhxtokNW3kfZXtyQ/ZDcyqkdutUdKB29QDMPDOMpivNFWlITo0Zo2dTfq9RIk0pAocuFKoqqfIWFJNVypn2Hfi8qoq+H0q61Ox2dWvqDkJ5oYSGmE6vQ+MbD3xS04de7RmOmVxJvo7bxCwlygnv1F0lOXDZM7SEZpEYrgr8FU0bnoDHaU45ZKv3Rnw4Jd5lHGp0XdSnsaIsawdB7bZv1qIBvarLgxDnjHJebx6Se1wzvgobGVHdxRmJOkSqqBLnaqOz9+N02tesKvXKTCIQM2Oas8Gobxc0RAPEW/KpIkU+oNpkV4m3pnyyNxIzfpHtYNE9ZM9sZhvdWlVZHnINfsAmKqkUjdbgbgZTtXHkeyxbzH3dZKvtEh+maDw4lTFPtVvKTd0xDrWVoxqTIEDBVQQqSwD+iGSsqw4oBpYqUNppp1XxdKqZKg4Gj+fpAnDisELNznTVJJoXv4FBIgsnr7BMgBA/6PZjmm9Bg8iAQoyrTgf5qSYfd9eAn0ZssvPRRjRGEqiO3/SAvcqwZpNLZuehEXBnnkBtFBYlgcDB1tzyS30eMidMkNytR4bJ06MfBDnwuHZyzJ3S6WFrnFPjOds9OGTP0RCKV415x1CFZuEOsKoZyRbE+EQFVyyUPJnF1ZrnQVcTmTM4VShRm6pNyiarZjkObgMXWWOGGnJsq0WZlgDkFLfnyNQTWTF1fMd/zXWUFa3CO190s+6Mhs2NX3hDDR6Xy37jI9KNkzt/wv5ZudMt9rn9xYW4M2kc4QfbUfvq58lnSBZWHNmcHunNxXVLXPRwoQQoWJeW88BxVWhk11xgiplt4OnUiKeHEwQbigRUX6gsHR+xo0nDLNTg8rArL3e4Cu7R8DE1pHK3so85PDigWkD0xoS8GpTYavzEwJYmXVKMMW0VZWfMkep00e2d+6kWiLP3w+1L/TflejXFeXvkWBpi6lsGBYOtVUNXhEOQ4wid5NnIrVaKekT89eHd4a4426T5WrAvXXGmqh9X4yNKlsPflQ5M9VrlyWou18PGyEwV8yQo+l7hpdF7VIlOYUUiGosu3cMcHKSyEuDowAmHt/VnVqSWYrkaAC27WNzc1Nv1qtQWFXq4OrmjcDqHbGhX4x/uWSRQfkXNoE3PWmowX7LJVg1mJjBZnKW6sFMcbXjSMflzA/KkHEkLhyG80gc0IEIA/dB6XKVTG7abHzUxWoZ2U4XtdOo2o5mBhdsC4j7nafDBfkobNHCxQ1Ko3KOhECUfmhsakXFqaHq5riakcLJhzsNPRvVnin2R0+WgR85taAkaam5znK7dVNe/2aqCfnlvjQ8gl1M6lY36kEPLnK9h421q1rXDae8+Q/xbTYFlM7lw5MhSiugiLilDyUwqzlrBRbZhqp+nLwFSjb0c+v4JraEHSpbV+J5navKGs5FZuzGPtEHO1dg5zW1rjsTHJO0xlMIXzfD2M3AT7TWxMEvQoqIiguwiRkoOFcGexuyIRJomOXR11sdL7XIsVQz08jgtsXj1szRoKQ0pVlc3K4WI5EEknJIowrB6SpgcHmmzj9Fuq8nsR9R3PJxXIrNGO0YvRSOI2qPiHNqz7/NwJmpaebbr3UpF1+HMxi6JHP3ZXZ8hixU6GlNWxlzUUBvBvfrSgijW/qq/r+9XjBRKpN00FFVjPYmuhFVW3dlUvXKKr8o0a9eI4z6cAKN3a86H7VQjrTGH0zS1VC6WgkqF5KoGo+qi4qbZGpXXiycu11YPynaNmDSNN6q5CeCHnFY9Vg3HEpOOcj2/leaulr9BWaLbeQq80zV4zSZMVaY4n8Bx9Y9FyFMzMmf+0YlwT32eD1mkidshG5ygzxhrQR6UjxzktsSYx/7GWRpfARj2PPYbx6mke0jl5+2Hj28My/b7q+aP9Pnue9FnOeN9lwUkj7vFg+KD63PZzfJ6mfX19OjOuexueb3s4/fS77+gfr0sPfQNtFb93cufXzL//0/6/lkbDw566g83JlpJ/mGbb394+fU3jy/+TSPHj2/+4M+mv/kdquIfcM9/REt889XLv37z8vX9XrUIy0lEs3jn/3Kv8v5e+fHVxx19+zT9vNvtG/b4Orzqy8R3Fr0tPktILHRx57g0ouz/TH5Qcqu8BWL+8bvHJ698+XAlD/LHTuSQ4gwvxj25sFyLeb1GHZP3Fv/W9/H1cOJVw40+8U38meIrGkFS/+yDMTzkJ9/89vvVN+v7y2+fVN8uv1k/XP7u8/zXy9+sf8vBuMgfKW1N2hV/xPf3+JZuY9ISso+up/4Ct3p9tdu/M+Htde//EoQ367r9mxT+nzzsRVL5PE2YKhn8bmWaRA1rdGg/Xvv9z97huNzXL/8LSwBVYwplbmRzdHJlYW0KZW5kb2JqCjEyIDAgb2JqCjcxMDcKZW5kb2JqCjEwIDAgb2JqClsgXQplbmRvYmoKMTcgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA4MyA+PgpzdHJlYW0KeJxFjLsNwDAIRHumYAR+JvY+UZTC3r8NECVuuCfdPVwdCZkpbjPDQwaeDCyGXXGB9JYwC1xHUI6d7KNh1b7qBI31plLz7w+Unuys4obrAQJCGmYKZW5kc3RyZWFtCmVuZG9iagoxOCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDUxID4+CnN0cmVhbQp4nDM2tFAwUDA0MAeSRoZAlpGJQoohF0gAxMzlggnmgFkGQBqiOAeuJocrgysNAOG0DZgKZW5kc3RyZWFtCmVuZG9iagoxOSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDI1MSA+PgpzdHJlYW0KeJwtUUlyA0EIu88r9IRmp99jlyuH5P/XCMoHBg2LQHRa4qCMnyAsV7zlkatow98zMYLfBYd+K9dtWORAVCBJY1A1oXbxevQe2HGYCcyT1rAMZqwP/Iwp3OjF4TEZZ7fXZdQQ7F2vPZlByaxcxCUTF0zVYSNnDj+ZMi60cz03IOdGWJdhkG5WGjMSjjSFSCGFqpukzgRBEoyuRo02chT7pS+PdIZVjagx7HMtbV/PTThr0OxYrPLklB5dcS4nFy+sHPT1NgMXUWms8kBIwP1uD/VzspPfeEvnzhbT43vNyfLCVGDFm9duQDbV4t+8iOP7jK/n5/n8A19gW4gKZW5kc3RyZWFtCmVuZG9iagoyMCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDIxNSA+PgpzdHJlYW0KeJw1UTkOAyEM7PcV/kAkjC94T6Iozf6/zYzRVh7BXIa0lCGZ8lKTqCHlUz56mS6cutzXzGo055a0LXOAuLa8L62SwIlmiIPBaZi4AZo8AUPX0ahRQxce0NSlUyiw3AQ+irduD91jtYGXtiHniSBiKBksQc2pRRMWbc8npDW/Xosb3pft3chTpcaWGIEGAVY4HNfo1/CVPU8m0XQVMtSrNcsYCRNFIjz5jqbVE+taNNIyEtTGEaxqA7w7/TBOAAATccsCZJ9KlLPkxG+x9LMGV/r+AZ9HVJYKZW5kc3RyZWFtCmVuZG9iagoxNSAwIG9iago8PCAvQmFzZUZvbnQgL0JNUVFEVitEZWphVnVTYW5zIC9DaGFyUHJvY3MgMTYgMCBSCi9FbmNvZGluZyA8PCAvRGlmZmVyZW5jZXMgWyA0NiAvcGVyaW9kIDQ4IC96ZXJvIC9vbmUgL3R3byBdIC9UeXBlIC9FbmNvZGluZyA+PgovRmlyc3RDaGFyIDAgL0ZvbnRCQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvRm9udERlc2NyaXB0b3IgMTQgMCBSCi9Gb250TWF0cml4IFsgMC4wMDEgMCAwIDAuMDAxIDAgMCBdIC9MYXN0Q2hhciAyNTUgL05hbWUgL0JNUVFEVitEZWphVnVTYW5zCi9TdWJ0eXBlIC9UeXBlMyAvVHlwZSAvRm9udCAvV2lkdGhzIDEzIDAgUiA+PgplbmRvYmoKMTQgMCBvYmoKPDwgL0FzY2VudCA5MjkgL0NhcEhlaWdodCAwIC9EZXNjZW50IC0yMzYgL0ZsYWdzIDMyCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZvbnROYW1lIC9CTVFRRFYrRGVqYVZ1U2FucwovSXRhbGljQW5nbGUgMCAvTWF4V2lkdGggMTM0MiAvU3RlbVYgMCAvVHlwZSAvRm9udERlc2NyaXB0b3IgL1hIZWlnaHQgMCA+PgplbmRvYmoKMTMgMCBvYmoKWyA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMAo2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDMxOCA0MDEgNDYwIDgzOCA2MzYKOTUwIDc4MCAyNzUgMzkwIDM5MCA1MDAgODM4IDMxOCAzNjEgMzE4IDMzNyA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2CjYzNiA2MzYgMzM3IDMzNyA4MzggODM4IDgzOCA1MzEgMTAwMCA2ODQgNjg2IDY5OCA3NzAgNjMyIDU3NSA3NzUgNzUyIDI5NQoyOTUgNjU2IDU1NyA4NjMgNzQ4IDc4NyA2MDMgNzg3IDY5NSA2MzUgNjExIDczMiA2ODQgOTg5IDY4NSA2MTEgNjg1IDM5MCAzMzcKMzkwIDgzOCA1MDAgNTAwIDYxMyA2MzUgNTUwIDYzNSA2MTUgMzUyIDYzNSA2MzQgMjc4IDI3OCA1NzkgMjc4IDk3NCA2MzQgNjEyCjYzNSA2MzUgNDExIDUyMSAzOTIgNjM0IDU5MiA4MTggNTkyIDU5MiA1MjUgNjM2IDMzNyA2MzYgODM4IDYwMCA2MzYgNjAwIDMxOAozNTIgNTE4IDEwMDAgNTAwIDUwMCA1MDAgMTM0MiA2MzUgNDAwIDEwNzAgNjAwIDY4NSA2MDAgNjAwIDMxOCAzMTggNTE4IDUxOAo1OTAgNTAwIDEwMDAgNTAwIDEwMDAgNTIxIDQwMCAxMDIzIDYwMCA1MjUgNjExIDMxOCA0MDEgNjM2IDYzNiA2MzYgNjM2IDMzNwo1MDAgNTAwIDEwMDAgNDcxIDYxMiA4MzggMzYxIDEwMDAgNTAwIDUwMCA4MzggNDAxIDQwMSA1MDAgNjM2IDYzNiAzMTggNTAwCjQwMSA0NzEgNjEyIDk2OSA5NjkgOTY5IDUzMSA2ODQgNjg0IDY4NCA2ODQgNjg0IDY4NCA5NzQgNjk4IDYzMiA2MzIgNjMyIDYzMgoyOTUgMjk1IDI5NSAyOTUgNzc1IDc0OCA3ODcgNzg3IDc4NyA3ODcgNzg3IDgzOCA3ODcgNzMyIDczMiA3MzIgNzMyIDYxMSA2MDUKNjMwIDYxMyA2MTMgNjEzIDYxMyA2MTMgNjEzIDk4MiA1NTAgNjE1IDYxNSA2MTUgNjE1IDI3OCAyNzggMjc4IDI3OCA2MTIgNjM0CjYxMiA2MTIgNjEyIDYxMiA2MTIgODM4IDYxMiA2MzQgNjM0IDYzNCA2MzQgNTkyIDYzNSA1OTIgXQplbmRvYmoKMTYgMCBvYmoKPDwgL29uZSAxNyAwIFIgL3BlcmlvZCAxOCAwIFIgL3R3byAxOSAwIFIgL3plcm8gMjAgMCBSID4+CmVuZG9iagozIDAgb2JqCjw8IC9GMSAxNSAwIFIgPj4KZW5kb2JqCjQgMCBvYmoKPDwgL0ExIDw8IC9DQSAwIC9UeXBlIC9FeHRHU3RhdGUgL2NhIDEgPj4KL0EyIDw8IC9DQSAxIC9UeXBlIC9FeHRHU3RhdGUgL2NhIDEgPj4gPj4KZW5kb2JqCjUgMCBvYmoKPDwgPj4KZW5kb2JqCjYgMCBvYmoKPDwgPj4KZW5kb2JqCjcgMCBvYmoKPDwgPj4KZW5kb2JqCjIgMCBvYmoKPDwgL0NvdW50IDEgL0tpZHMgWyAxMSAwIFIgXSAvVHlwZSAvUGFnZXMgPj4KZW5kb2JqCjIxIDAgb2JqCjw8IC9DcmVhdGlvbkRhdGUgKEQ6MjAyMjA0MDQyMDQwNDYtMDQnMDAnKQovQ3JlYXRvciAoTWF0cGxvdGxpYiB2My41LjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcpCi9Qcm9kdWNlciAoTWF0cGxvdGxpYiBwZGYgYmFja2VuZCB2My41LjEpID4+CmVuZG9iagp4cmVmCjAgMjIKMDAwMDAwMDAwMCA2NTUzNSBmIAowMDAwMDAwMDE2IDAwMDAwIG4gCjAwMDAwMTAzMzAgMDAwMDAgbiAKMDAwMDAxMDEzNiAwMDAwMCBuIAowMDAwMDEwMTY4IDAwMDAwIG4gCjAwMDAwMTAyNjcgMDAwMDAgbiAKMDAwMDAxMDI4OCAwMDAwMCBuIAowMDAwMDEwMzA5IDAwMDAwIG4gCjAwMDAwMDAwNjUgMDAwMDAgbiAKMDAwMDAwMDM1MiAwMDAwMCBuIAowMDAwMDA3NTU1IDAwMDAwIG4gCjAwMDAwMDAyMDggMDAwMDAgbiAKMDAwMDAwNzUzNCAwMDAwMCBuIAowMDAwMDA5MDA5IDAwMDAwIG4gCjAwMDAwMDg4MDIgMDAwMDAgbiAKMDAwMDAwODQ2NSAwMDAwMCBuIAowMDAwMDEwMDYyIDAwMDAwIG4gCjAwMDAwMDc1NzUgMDAwMDAgbiAKMDAwMDAwNzczMCAwMDAwMCBuIAowMDAwMDA3ODUzIDAwMDAwIG4gCjAwMDAwMDgxNzcgMDAwMDAgbiAKMDAwMDAxMDM5MCAwMDAwMCBuIAp0cmFpbGVyCjw8IC9JbmZvIDIxIDAgUiAvUm9vdCAxIDAgUiAvU2l6ZSAyMiA+PgpzdGFydHhyZWYKMTA1NDcKJSVFT0YK\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import plot_helper\n", "\n", "plot_helper.plot_qp2()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We return to the textbook model and set the solver to one that can handle quadratic objectives such as cplex. We then add the linear constraint that the sum of our x and y reactions, that we set to FBA and NH4t, must equal 2." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.solver = 'cplex'\n", "sum_two = model.problem.Constraint(\n", " model.reactions.FBA.flux_expression + model.reactions.NH4t.flux_expression,\n", " lb=2,\n", " ub=2)\n", "model.add_cons_vars(sum_two)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we add the quadratic objective" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "quadratic_objective = model.problem.Objective(\n", " 0.5 * model.reactions.NH4t.flux_expression**2 + 0.5 *\n", " model.reactions.FBA.flux_expression**2 -\n", " model.reactions.FBA.flux_expression,\n", " direction='min')\n", "model.objective = quadratic_objective\n", "solution = model.optimize(objective_sense=None)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5 1.5\n" ] } ], "source": [ "print(solution.fluxes['NH4t'], solution.fluxes['FBA'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also create additional variables to facilitate studying the effects of new constraints and variables. Suppose we want to study the difference in flux between nitrogen and carbon uptake whilst we block other reactions. For this it will may help to add another variable representing this difference." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "model = load_model('textbook')\n", "difference = model.problem.Variable('difference')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use constraints to define what values this variable shall take" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "constraint = model.problem.Constraint(\n", " model.reactions.EX_glc__D_e.flux_expression -\n", " model.reactions.EX_nh4_e.flux_expression - difference,\n", " lb=0,\n", " ub=0)\n", "model.add_cons_vars([difference, constraint])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can access that difference directly during our knock-out exploration by looking at its primal value." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-5.234680806802513\n", "-5.234680806802513\n", "-5.234680806802513\n", "-0.9322222222222226\n", "-0.9322222222222231\n" ] } ], "source": [ "for reaction in model.reactions[:5]:\n", " with model:\n", " reaction.knock_out()\n", " model.optimize()\n", " print(model.solver.variables.difference.primal)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 } cobrapy-0.29.1/documentation_builder/deletions.ipynb000066400000000000000000000705431467301562300226720ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulating Deletions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas\n", "from time import time\n", "\n", "from cobra.io import load_model\n", "from cobra.flux_analysis import (\n", " single_gene_deletion, single_reaction_deletion, double_gene_deletion,\n", " double_reaction_deletion)\n", "\n", "cobra_model = load_model(\"textbook\")\n", "ecoli_model = load_model(\"iJO1366\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Knocking out single genes and reactions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A commonly asked question when analyzing metabolic models is what will happen if a certain reaction was not allowed to have any flux at all. This can tested using cobrapy by" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "complete model: \n", "pfk knocked out: \n" ] } ], "source": [ "print('complete model: ', cobra_model.optimize())\n", "with cobra_model:\n", " cobra_model.reactions.PFK.knock_out()\n", " print('pfk knocked out: ', cobra_model.optimize())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For evaluating genetic manipulation strategies, it is more interesting to examine what happens if given genes are knocked out as doing so can affect no reactions in case of redundancy, or more reactions if gene when is participating in more than one reaction." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "complete model: \n", "pfkA knocked out: \n", "pfkB knocked out: \n" ] } ], "source": [ "print('complete model: ', cobra_model.optimize())\n", "with cobra_model:\n", " cobra_model.genes.b1723.knock_out()\n", " print('pfkA knocked out: ', cobra_model.optimize())\n", " cobra_model.genes.b3916.knock_out()\n", " print('pfkB knocked out: ', cobra_model.optimize())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Single Deletions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perform all single gene deletions on a model" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "deletion_results = single_gene_deletion(cobra_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These can also be done for only a subset of genes" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idsgrowthstatus
0{b1849}0.873922optimal
1{b1478}0.873922optimal
2{b1276}0.873922optimal
3{b0351}0.873922optimal
4{b3733}0.374230optimal
5{b1241}0.873922optimal
6{b0116}0.782351optimal
7{b0727}0.858307optimal
8{b0356}0.873922optimal
9{b2587}0.873922optimal
10{b0118}0.873922optimal
11{b0726}0.858307optimal
12{b3736}0.374230optimal
13{b3735}0.374230optimal
14{b2296}0.873922optimal
15{b3115}0.873922optimal
16{b3732}0.374230optimal
17{b0474}0.873922optimal
18{s0001}0.211141optimal
19{b3734}0.374230optimal
\n", "
" ], "text/plain": [ " ids growth status\n", "0 {b1849} 0.873922 optimal\n", "1 {b1478} 0.873922 optimal\n", "2 {b1276} 0.873922 optimal\n", "3 {b0351} 0.873922 optimal\n", "4 {b3733} 0.374230 optimal\n", "5 {b1241} 0.873922 optimal\n", "6 {b0116} 0.782351 optimal\n", "7 {b0727} 0.858307 optimal\n", "8 {b0356} 0.873922 optimal\n", "9 {b2587} 0.873922 optimal\n", "10 {b0118} 0.873922 optimal\n", "11 {b0726} 0.858307 optimal\n", "12 {b3736} 0.374230 optimal\n", "13 {b3735} 0.374230 optimal\n", "14 {b2296} 0.873922 optimal\n", "15 {b3115} 0.873922 optimal\n", "16 {b3732} 0.374230 optimal\n", "17 {b0474} 0.873922 optimal\n", "18 {s0001} 0.211141 optimal\n", "19 {b3734} 0.374230 optimal" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "single_gene_deletion(cobra_model, cobra_model.genes[:20])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This can also be done for reactions" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idsgrowthstatus
0{AKGt2r}8.739215e-01optimal
1{D_LACt2}8.739215e-01optimal
2{Biomass_Ecoli_core}0.000000e+00optimal
3{ETOHt2r}8.739215e-01optimal
4{ADK1}8.739215e-01optimal
5{ACONTb}3.279963e-17optimal
6{ACt2r}8.739215e-01optimal
7{ACONTa}3.344590e-15optimal
8{ACKr}8.739215e-01optimal
9{ALCD2x}8.739215e-01optimal
10{ATPS4r}3.742299e-01optimal
11{ACALD}8.739215e-01optimal
12{ENO}1.357454e-16optimal
13{EX_ac_e}8.739215e-01optimal
14{ACALDt}8.739215e-01optimal
15{CS}4.757918e-15optimal
16{AKGDH}8.583074e-01optimal
17{CO2t}4.616696e-01optimal
18{CYTBD}2.116629e-01optimal
19{ATPM}9.166475e-01optimal
\n", "
" ], "text/plain": [ " ids growth status\n", "0 {AKGt2r} 8.739215e-01 optimal\n", "1 {D_LACt2} 8.739215e-01 optimal\n", "2 {Biomass_Ecoli_core} 0.000000e+00 optimal\n", "3 {ETOHt2r} 8.739215e-01 optimal\n", "4 {ADK1} 8.739215e-01 optimal\n", "5 {ACONTb} 3.279963e-17 optimal\n", "6 {ACt2r} 8.739215e-01 optimal\n", "7 {ACONTa} 3.344590e-15 optimal\n", "8 {ACKr} 8.739215e-01 optimal\n", "9 {ALCD2x} 8.739215e-01 optimal\n", "10 {ATPS4r} 3.742299e-01 optimal\n", "11 {ACALD} 8.739215e-01 optimal\n", "12 {ENO} 1.357454e-16 optimal\n", "13 {EX_ac_e} 8.739215e-01 optimal\n", "14 {ACALDt} 8.739215e-01 optimal\n", "15 {CS} 4.757918e-15 optimal\n", "16 {AKGDH} 8.583074e-01 optimal\n", "17 {CO2t} 4.616696e-01 optimal\n", "18 {CYTBD} 2.116629e-01 optimal\n", "19 {ATPM} 9.166475e-01 optimal" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "single_reaction_deletion(cobra_model, cobra_model.reactions[:20])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Double Deletions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Double deletions run in a similar way." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idsgrowthstatus
0{b2465, b3919}0.7040optimal
1{b2935, b2464}0.8739optimal
2{b0008, b2465}0.8739optimal
3{b2465}0.8739optimal
4{b0008}0.8739optimal
5{b0008, b2464}0.8648optimal
6{b2465, b2935}-0.0000optimal
7{b0008, b3919}0.7040optimal
8{b2465, b2464}0.8739optimal
9{b0008, b2935}0.8739optimal
10{b3919}0.7040optimal
11{b2464}0.8739optimal
12{b2935}0.8739optimal
13{b2935, b3919}0.7040optimal
14{b3919, b2464}0.7040optimal
\n", "
" ], "text/plain": [ " ids growth status\n", "0 {b2465, b3919} 0.7040 optimal\n", "1 {b2935, b2464} 0.8739 optimal\n", "2 {b0008, b2465} 0.8739 optimal\n", "3 {b2465} 0.8739 optimal\n", "4 {b0008} 0.8739 optimal\n", "5 {b0008, b2464} 0.8648 optimal\n", "6 {b2465, b2935} -0.0000 optimal\n", "7 {b0008, b3919} 0.7040 optimal\n", "8 {b2465, b2464} 0.8739 optimal\n", "9 {b0008, b2935} 0.8739 optimal\n", "10 {b3919} 0.7040 optimal\n", "11 {b2464} 0.8739 optimal\n", "12 {b2935} 0.8739 optimal\n", "13 {b2935, b3919} 0.7040 optimal\n", "14 {b3919, b2464} 0.7040 optimal" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "double_gene_deletion(\n", " cobra_model, cobra_model.genes[-5:]).round(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, the double deletion function will automatically use multiprocessing, splitting the task over up to 4 cores if they are available. The number of cores can be manually specified as well. Setting use of a single core will disable use of the multiprocessing library, which often aids debugging." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Double gene deletions for 200 genes completed in 1.29 sec with 2 cores\n", "Double gene deletions for 200 genes completed in 1.74 sec with 1 core\n", "Speedup of 1.35x\n" ] } ], "source": [ "start = time() # start timer()\n", "double_gene_deletion(\n", " ecoli_model, ecoli_model.genes[:25], processes=2)\n", "t1 = time() - start\n", "print(\"Double gene deletions for 200 genes completed in \"\n", " \"%.2f sec with 2 cores\" % t1)\n", "\n", "start = time() # start timer()\n", "double_gene_deletion(\n", " ecoli_model, ecoli_model.genes[:25], processes=1)\n", "t2 = time() - start\n", "print(\"Double gene deletions for 200 genes completed in \"\n", " \"%.2f sec with 1 core\" % t2)\n", "\n", "print(\"Speedup of %.2fx\" % (t2 / t1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Double deletions can also be run for reactions." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idsgrowthstatus
0{ACONTa}0.0000optimal
1{ACt2r, ACONTa}0.0000optimal
2{ACKr}0.8739optimal
3{ACt2r, ACKr}0.8739optimal
4{ADK1, ACONTb}0.0000optimal
5{ACONTa, ACONTb}0.0000optimal
6{ACKr, ACONTa}0.0000optimal
7{ACKr, ACONTb}0.0000optimal
8{ACt2r, ADK1}0.8739optimal
9{ACt2r, ACONTb}0.0000optimal
10{ADK1, ACKr}0.8739optimal
11{ACONTb}0.0000optimal
12{ADK1, ACONTa}0.0000optimal
13{ACt2r}0.8739optimal
14{ADK1}0.8739optimal
\n", "
" ], "text/plain": [ " ids growth status\n", "0 {ACONTa} 0.0000 optimal\n", "1 {ACt2r, ACONTa} 0.0000 optimal\n", "2 {ACKr} 0.8739 optimal\n", "3 {ACt2r, ACKr} 0.8739 optimal\n", "4 {ADK1, ACONTb} 0.0000 optimal\n", "5 {ACONTa, ACONTb} 0.0000 optimal\n", "6 {ACKr, ACONTa} 0.0000 optimal\n", "7 {ACKr, ACONTb} 0.0000 optimal\n", "8 {ACt2r, ADK1} 0.8739 optimal\n", "9 {ACt2r, ACONTb} 0.0000 optimal\n", "10 {ADK1, ACKr} 0.8739 optimal\n", "11 {ACONTb} 0.0000 optimal\n", "12 {ADK1, ACONTa} 0.0000 optimal\n", "13 {ACt2r} 0.8739 optimal\n", "14 {ADK1} 0.8739 optimal" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "double_reaction_deletion(\n", " cobra_model, cobra_model.reactions[2:7]).round(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing individual deletion results\n", "\n", "Note that the indices for deletions are python [set](https://docs.python.org/3.8/library/stdtypes.html#set-types-set-frozenset) objects. This is the appropriate type since the order of deletions does not matter. Deleting reaction 1 and reaction 2 will have the same effect as deleting reaction 2 and reaction 1. \n", "\n", "To make it easier to access results all DataFrames returned by COBRAPpy deletion functions have a `knockout` indexer that makes that a bit simpler. Each entry in the indexer is treated as a single deletion entry. So you need to pass sets for double deletions." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ids growth status\n", "12 {ATPM} 0.916647 optimal\n", " ids growth status\n", "859 {ATPM, TKT1} 0.90584 optimal\n" ] } ], "source": [ "single = single_reaction_deletion(cobra_model)\n", "double = double_reaction_deletion(cobra_model)\n", "\n", "print(single.knockout[\"ATPM\"])\n", "print(double.knockout[{\"ATPM\", \"TKT1\"}])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This can be used to get several deletions at once and will also work for Reaction or Gene objects (depending on what you deleted) directly." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ids growth status\n", "12 {ATPM} 0.916647 optimal\n", "44 {TKT1} 0.864759 optimal\n", "71 {PFK} 0.704037 optimal\n", " ids growth status\n", "425 {ATPM, PFK} 0.704037 optimal\n", "859 {ATPM, TKT1} 0.905840 optimal\n", "4125 {ATPM} 0.916647 optimal\n" ] } ], "source": [ "atpm = cobra_model.reactions.ATPM\n", "tkt1 = cobra_model.reactions.TKT1\n", "pfk = cobra_model.reactions.PFK\n", "\n", "print(single.knockout[atpm, tkt1, pfk])\n", "print(double.knockout[{atpm, tkt1}, {atpm, pfk}, {atpm}])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 } cobrapy-0.29.1/documentation_builder/dfba.ipynb000066400000000000000000000735661467301562300216100ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dynamic Flux Balance Analysis (dFBA) in COBRApy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following notebook shows a simple, but slow example of implementing dFBA using COBRApy and [scipy.integrate.solve_ivp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html). This notebook shows a static optimization approach (SOA) implementation and should not be considered production ready.\n", "\n", "The model considers only basic Michaelis-Menten limited growth on glucose." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from tqdm import tqdm\n", "\n", "from scipy.integrate import solve_ivp\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create or load a cobrapy model. Here, we use the 'textbook' e-coli core model." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling...\n", " A: min|aij| = 1.000e+00 max|aij| = 1.000e+00 ratio = 1.000e+00\n", "Problem data seem to be well scaled\n" ] } ], "source": [ "import cobra\n", "from cobra.io import load_model\n", "model = load_model('textbook')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up the dynamic system" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dynamic flux balance analysis couples a dynamic system in external cellular concentrations to a pseudo-steady state metabolic model.\n", "\n", "In this notebook, we define the function `add_dynamic_bounds(model, y)` to convert the external metabolite concentrations into bounds on the boundary fluxes in the metabolic model." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def add_dynamic_bounds(model, y):\n", " \"\"\"Use external concentrations to bound the uptake flux of glucose.\"\"\"\n", " biomass, glucose = y # expand the boundary species\n", " glucose_max_import = -10 * glucose / (5 + glucose)\n", " model.reactions.EX_glc__D_e.lower_bound = glucose_max_import\n", " \n", "\n", "def dynamic_system(t, y):\n", " \"\"\"Calculate the time derivative of external species.\"\"\"\n", "\n", " biomass, glucose = y # expand the boundary species\n", " \n", " # Calculate the specific exchanges fluxes at the given external concentrations.\n", " with model:\n", " add_dynamic_bounds(model, y)\n", " \n", " cobra.util.add_lp_feasibility(model)\n", " feasibility = cobra.util.fix_objective_as_constraint(model)\n", " lex_constraints = cobra.util.add_lexicographic_constraints(\n", " model, ['Biomass_Ecoli_core', 'EX_glc__D_e'], ['max', 'max'])\n", " \n", " # Since the calculated fluxes are specific rates, we multiply them by the\n", " # biomass concentration to get the bulk exchange rates.\n", " fluxes = lex_constraints.values\n", " fluxes *= biomass\n", " \n", " # This implementation is **not** efficient, so I display the current\n", " # simulation time using a progress bar.\n", " if dynamic_system.pbar is not None:\n", " dynamic_system.pbar.update(1)\n", " dynamic_system.pbar.set_description('t = {:.3f}'.format(t))\n", " \n", " return fluxes\n", "\n", "dynamic_system.pbar = None\n", "\n", "\n", "def infeasible_event(t, y):\n", " \"\"\"\n", " Determine solution feasibility.\n", " \n", " Avoiding infeasible solutions is handled by solve_ivp's built-in event detection.\n", " This function re-solves the LP to determine whether or not the solution is feasible\n", " (and if not, how far it is from feasibility). When the sign of this function changes\n", " from -epsilon to positive, we know the solution is no longer feasible.\n", " \n", " \"\"\"\n", " \n", " with model:\n", " \n", " add_dynamic_bounds(model, y)\n", " \n", " cobra.util.add_lp_feasibility(model)\n", " feasibility = cobra.util.fix_objective_as_constraint(model)\n", " \n", " return feasibility - infeasible_event.epsilon\n", "\n", "infeasible_event.epsilon = 1E-6\n", "infeasible_event.direction = 1\n", "infeasible_event.terminal = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the dynamic FBA simulation" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "t = 5.804: : 185it [00:05, 34.89it/s]\n" ] } ], "source": [ "ts = np.linspace(0, 15, 100) # Desired integration resolution and interval\n", "y0 = [0.1, 10]\n", "\n", "with tqdm() as pbar:\n", " dynamic_system.pbar = pbar\n", "\n", " sol = solve_ivp(\n", " fun=dynamic_system,\n", " events=[infeasible_event],\n", " t_span=(ts.min(), ts.max()),\n", " y0=y0,\n", " t_eval=ts,\n", " rtol=1e-6,\n", " atol=1e-8,\n", " method='BDF'\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because the culture runs out of glucose, the simulation terminates early. The exact time of this 'cell death' is recorded in `sol.t_events`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " message: 'A termination event occurred.'\n", " nfev: 179\n", " njev: 2\n", " nlu: 14\n", " sol: None\n", " status: 1\n", " success: True\n", " t: array([0. , 0.15151515, 0.3030303 , 0.45454545, 0.60606061,\n", " 0.75757576, 0.90909091, 1.06060606, 1.21212121, 1.36363636,\n", " 1.51515152, 1.66666667, 1.81818182, 1.96969697, 2.12121212,\n", " 2.27272727, 2.42424242, 2.57575758, 2.72727273, 2.87878788,\n", " 3.03030303, 3.18181818, 3.33333333, 3.48484848, 3.63636364,\n", " 3.78787879, 3.93939394, 4.09090909, 4.24242424, 4.39393939,\n", " 4.54545455, 4.6969697 , 4.84848485, 5. , 5.15151515,\n", " 5.3030303 , 5.45454545, 5.60606061, 5.75757576])\n", " t_events: [array([5.80191035])]\n", " y: array([[ 0.1 , 0.10897602, 0.11871674, 0.12927916, 0.14072254,\n", " 0.15310825, 0.16649936, 0.18095988, 0.19655403, 0.21334507,\n", " 0.23139394, 0.25075753, 0.27148649, 0.29362257, 0.31719545,\n", " 0.34221886, 0.36868605, 0.3965646 , 0.42579062, 0.4562623 ,\n", " 0.48783322, 0.52030582, 0.55342574, 0.58687742, 0.62028461,\n", " 0.65321433, 0.685188 , 0.71570065, 0.74425054, 0.77037369,\n", " 0.79368263, 0.81390289, 0.83089676, 0.84467165, 0.85535715,\n", " 0.8631722 , 0.86843813, 0.8715096 , 0.8727423 ],\n", " [10. , 9.8947027 , 9.78040248, 9.65642157, 9.52205334,\n", " 9.37656372, 9.21919615, 9.04917892, 8.86573366, 8.6680879 ,\n", " 8.45549026, 8.22722915, 7.98265735, 7.72122137, 7.442497 ,\n", " 7.14623236, 6.83239879, 6.50124888, 6.15338213, 5.78981735,\n", " 5.41206877, 5.02222068, 4.62299297, 4.21779303, 3.81071525,\n", " 3.40650104, 3.01042208, 2.6280723 , 2.26504645, 1.92656158,\n", " 1.61703023, 1.33965598, 1.09616507, 0.88670502, 0.70995892,\n", " 0.56344028, 0.44387781, 0.34762375, 0.27100065]])\n", " y_events: [array([[0.87280538, 0.25178492]])]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sol" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot timelines of biomass and glucose" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Glucose')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = plt.subplot(111)\n", "ax.plot(sol.t, sol.y.T[:, 0])\n", "ax2 = plt.twinx(ax)\n", "ax2.plot(sol.t, sol.y.T[:, 1], color='r')\n", "\n", "ax.set_ylabel('Biomass', color='b')\n", "ax2.set_ylabel('Glucose', color='r')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Table of Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 } cobrapy-0.29.1/documentation_builder/faq.ipynb000066400000000000000000000203271467301562300214460ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# FAQ" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This document will address frequently asked questions not addressed in other pages of the documentation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How do I install cobrapy?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please see the [INSTALL.rst](https://github.com/opencobra/cobrapy/blob/stable/INSTALL.rst) file." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How do I cite cobrapy?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please cite the 2013 publication: [10.1186/1752-0509-7-74](http://dx.doi.org/doi:10.1186/1752-0509-7-74)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How do I rename reactions or metabolites?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TL;DR Use `Model.repair` afterwards\n", "\n", "When renaming metabolites or reactions, there are issues because cobra indexes based off of ID's, which can cause errors. For example:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling...\n", " A: min|aij| = 1.000e+00 max|aij| = 1.000e+00 ratio = 1.000e+00\n", "Problem data seem to be well scaled\n" ] } ], "source": [ "from cobra.io import load_model\n", "model = load_model(\"iYS1720\")\n", "\n", "for metabolite in model.metabolites:\n", " metabolite.id = f\"test_{metabolite.id}\"\n", "\n", "try:\n", " model.metabolites.get_by_id(model.metabolites[0].id)\n", "except KeyError as e:\n", " print(repr(e))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Model.repair function will rebuild the necessary indexes" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Metabolite identifiertest_10fthf_c
Name10-Formyltetrahydrofolate
Memory address0x7f9c91ec0a90
FormulaC20H21N7O7
Compartmentc
In 9 reaction(s)\n", " FTHFD, ULA4NFT, GARFT, AICART, TDPFRMT, MTHFC, BIOMASS_Ec_iAF1260_core_59p81M, BIOMASS_invivo, FMETTRS\n", "
" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.repair()\n", "model.metabolites.get_by_id(model.metabolites[0].id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How do I delete a gene?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That depends on what precisely you mean by delete a gene.\n", "\n", "If you want to simulate the model with a gene knockout, use the `cobra.manipulation.knock_out_model_genes` function within a context. The effects of this function are reversed when exiting a context." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bounds before knockout: (-1000.0, 1000.0)\n", "bounds after knockouts (0.0, 0.0)\n" ] } ], "source": [ "model = load_model(\"iYS1720\")\n", "PGI = model.reactions.get_by_id(\"PGI\")\n", "print(\"bounds before knockout:\", (PGI.lower_bound, PGI.upper_bound))\n", "from cobra.manipulation import knock_out_model_genes\n", "knock_out_model_genes(model, [\"STM4221\"])\n", "print(\"bounds after knockouts\", (PGI.lower_bound, PGI.upper_bound))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to actually remove all traces of a gene from a model, this is more difficult because this will require changing all the `gene_reaction_rule` strings for reactions involving the gene." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How do I change the reversibility of a Reaction?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Reaction.reversibility` is a property in cobra which is computed when it is requested from the lower and upper bounds." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = load_model(\"iYS1720\")\n", "model.reactions.get_by_id(\"PGI\").reversibility" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Trying to set it directly will result in an error or warning: " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra/core/reaction.py:810: UserWarning: Setting reaction reversibility is ignored\n", " warn(\"Setting reaction reversibility is ignored\")\n" ] } ], "source": [ "try:\n", " model.reactions.get_by_id(\"PGI\").reversibility = False\n", "except Exception as e:\n", " print(repr(e))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The way to change the reversibility is to change the bounds to make the reaction irreversible." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.reactions.get_by_id(\"PGI\").lower_bound = 10\n", "model.reactions.get_by_id(\"PGI\").reversibility" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How do I generate an LP file from a COBRA model?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### For optlang based solvers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With optlang solvers, the LP formulation of a model is obtained by its string representation. All solvers behave the same way." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "with open('test.lp', 'w') as out:\n", " out.write(str(model.solver))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How do I visualize my flux solutions?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please browse the [visualization packages](https://opencobra.github.io/cobrapy/tags/visualization/) on our website for the most recent list of tools." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 } cobrapy-0.29.1/documentation_builder/gapfilling.ipynb000066400000000000000000000137121467301562300230130ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Gapfillling\n", "\n", "Model gap filling is the task of figuring out which reactions have to be added to a model to make it feasible. Several such algorithms have been reported e.g. [Kumar et al. 2009](http://dx.doi.org/10.1371/journal.pcbi.1000308) and [Reed et al. 2006](http://www.pnas.org/content/103/46/17480.short). Cobrapy has a gap filling implementation that is very similar to that of Reed et al. where we use a mixed-integer linear program to figure out the smallest number of reactions that need to be added for a user-defined collection of reactions, i.e. a universal model. Briefly, the problem that we try to solve is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Minimize: $$\\sum_i c_i * z_i$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "subject to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$Sv = 0$$\n", "$$v^\\star \\geq t$$\n", "$$l_i\\leq v_i \\leq u_i$$\n", "$$v_i = 0 \\textrm{ if } z_i = 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Where *l*, *u* are lower and upper bounds for reaction *i* and *z* is an indicator variable that is zero if the reaction is not used and otherwise 1, *c* is a user-defined cost associated with using the *i*th reaction, $v^\\star$ is the flux of the objective and *t* a lower bound for that objective. To demonstrate, let's take a model and remove some essential reactions from it." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from cobra.io import load_model\n", "from cobra.flux_analysis import gapfill\n", "model = load_model(\"iYS1720\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this model D-Fructose-6-phosphate is an essential metabolite. We will remove all the reactions using it, and at them to a separate model." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import cobra\n", "universal = cobra.Model(\"universal_reactions\")\n", "for i in [i.id for i in model.metabolites.f6p_c.reactions]:\n", " reaction = model.reactions.get_by_id(i)\n", " universal.add_reactions([reaction.copy()])\n", " model.remove_reactions([reaction])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, because of these gaps, the model won't grow." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.optimize().objective_value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use can use the model's original objective, growth, to figure out which of the removed reactions are required for the model be feasible again. This is very similar to making the 'no-growth but growth (NGG)' predictions of [Kumar et al. 2009](http://dx.doi.org/10.1371/journal.pcbi.1000308)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TALA\n", "GF6PTA\n" ] } ], "source": [ "solution = gapfill(model, universal, demand_reactions=False)\n", "for reaction in solution[0]:\n", " print(reaction.id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can obtain multiple possible reaction sets by having the algorithm go through multiple iterations." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---- Run 1 ----\n", "TALA\n", "GF6PTA\n", "---- Run 2 ----\n", "TALA\n", "GF6PTA\n", "---- Run 3 ----\n", "TKT2\n", "GF6PTA\n", "PGI\n", "---- Run 4 ----\n", "TALA\n", "GF6PTA\n" ] } ], "source": [ "result = gapfill(model, universal, demand_reactions=False, iterations=4)\n", "for i, entries in enumerate(result):\n", " print(\"---- Run %d ----\" % (i + 1))\n", " for e in entries:\n", " print(e.id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also instead of using the original objective, specify a given metabolite that we want the model to be able to produce." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PGI\n" ] } ], "source": [ "with model:\n", " model.objective = model.add_boundary(model.metabolites.f6p_c, type='demand')\n", " solution = gapfill(model, universal)\n", " for reaction in solution[0]:\n", " print(reaction.id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, note that using mixed-integer linear programming is computationally quite expensive and for larger models you may want to consider alternative [gap filling methods](http://opencobra.github.io/cobrapy/tags/gapfilling/) and [reconstruction methods](http://opencobra.github.io/cobrapy/tags/reconstruction/)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.8" } }, "nbformat": 4, "nbformat_minor": 1 } cobrapy-0.29.1/documentation_builder/getting_started.ipynb000066400000000000000000001072761467301562300240770ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting Started" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading a model and inspecting it" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To begin with, cobrapy comes with bundled models for _Salmonella_ and _E. coli_, as well as a \"textbook\" model of _E. coli_ core metabolism. To load a test model, type" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling...\n", " A: min|aij| = 1.000e+00 max|aij| = 1.000e+00 ratio = 1.000e+00\n", "Problem data seem to be well scaled\n" ] } ], "source": [ "import cobra\n", "from cobra.io import load_model\n", "\n", "# \"iJO1366\" and \"salmonella\" are also valid arguments\n", "model = load_model(\"textbook\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The reactions, metabolites, and genes attributes of the cobrapy model are a special type of list called a `cobra.DictList`, and each one is made up of `cobra.Reaction`, `cobra.Metabolite` and `cobra.Gene` objects respectively." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "95\n", "72\n", "137\n" ] } ], "source": [ "print(len(model.reactions))\n", "print(len(model.metabolites))\n", "print(len(model.genes))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When using [Jupyter notebook](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/) this type of information is rendered as a table." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Namee_coli_core
Memory address0x07feb1af21f28
Number of metabolites72
Number of reactions95
Number of groups0
Objective expression1.0*Biomass_Ecoli_core - 1.0*Biomass_Ecoli_core_reverse_2cdba
Compartmentscytosol, extracellular
" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like a regular list, objects in the `DictList` can be retrieved by index. For example, to get the 30th reaction in the model (at index 29 because of [0-indexing](https://en.wikipedia.org/wiki/Zero-based_numbering)):" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierEX_glu__L_e
NameL-Glutamate exchange
Memory address0x7feb1b012eb8
Stoichiometry\n", "

glu__L_e -->

\n", "

L-Glutamate -->

\n", "
GPR
Lower bound0.0
Upper bound1000.0
\n", " " ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.reactions[29]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Additionally, items can be retrieved by their `id` using the `DictList.get_by_id()` function. For example, to get the cytosolic atp metabolite object (the id is \"atp_c\"), we can do the following:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Metabolite identifieratp_c
NameATP
Memory address0x7feb1af41550
FormulaC10H12N5O13P3
Compartmentc
In 13 reaction(s)\n", " Biomass_Ecoli_core, ATPS4r, PGK, ACKr, ADK1, GLNS, ATPM, PFK, PPS, GLNabc, PYK, PPCK, SUCOAS\n", "
" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.metabolites.get_by_id(\"atp_c\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an added bonus, users with an interactive shell such as IPython will be able to tab-complete to list elements inside a list. While this is not recommended behavior for most code because of the possibility for characters like \"-\" inside ids, this is very useful while in an interactive prompt:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-10.0, 1000.0)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.reactions.EX_glc__D_e.bounds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reactions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will consider the reaction glucose 6-phosphate isomerase, which interconverts glucose 6-phosphate and fructose 6-phosphate. The reaction id for this reaction in our test model is PGI." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierPGI
Nameglucose-6-phosphate isomerase
Memory address0x7feb1b09c2e8
Stoichiometry\n", "

g6p_c <=> f6p_c

\n", "

D-Glucose 6-phosphate <=> D-Fructose 6-phosphate

\n", "
GPRb4025
Lower bound-1000.0
Upper bound1000.0
\n", " " ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pgi = model.reactions.get_by_id(\"PGI\")\n", "pgi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can view the full name and reaction catalyzed as strings" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "glucose-6-phosphate isomerase\n", "g6p_c <=> f6p_c\n" ] } ], "source": [ "print(pgi.name)\n", "print(pgi.reaction)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also view reaction upper and lower bounds. Because the `pgi.lower_bound` < 0, and `pgi.upper_bound` > 0, `pgi` is reversible." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-1000.0 < pgi < 1000.0\n", "True\n" ] } ], "source": [ "print(pgi.lower_bound, \"< pgi <\", pgi.upper_bound)\n", "print(pgi.reversibility)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The lower and upper bound of reactions can also be modified, and the reversibility attribute will automatically be updated. The preferred method for manipulating bounds is using `reaction.bounds`, e.g." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 < pgi < 1000.0\n", "Reversibility after modification: False\n", "Reversibility after resetting: True\n" ] } ], "source": [ "old_bounds = pgi.bounds\n", "pgi.bounds = (0, 1000.0)\n", "print(pgi.lower_bound, \"< pgi <\", pgi.upper_bound)\n", "print(\"Reversibility after modification:\", pgi.reversibility)\n", "pgi.bounds = old_bounds\n", "print(\"Reversibility after resetting:\", pgi.reversibility)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bounds can also be modified one-at-a-time using `reaction.lower_bound` or `reaction.upper_bound`. This approach is less desirable than setting both bounds simultaneously with `reaction.bounds` because a user might accidently set a lower bound *higher* than an upper bound (or vice versa). If a lower bound is *higher* than upper bound (or vice versa), this will lead to an error." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Upper bound prior to setting new lower bound: 1000.0\n" ] }, { "ename": "ValueError", "evalue": "The lower bound must be less than or equal to the upper bound (1100 <= 1000.0).", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/var/folders/v7/99h1ft7s0mzg1_83fmbc8mnm0000gn/T/ipykernel_23255/4168182522.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mold_bounds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpgi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Upper bound prior to setting new lower bound:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpgi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupper_bound\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpgi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower_bound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Upper bound after setting new lower bound:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpgi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupper_bound\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpgi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbounds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mold_bounds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/PycharmProjects/cobrapy/src/cobra/util/context.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, new_value)\u001b[0m\n\u001b[1;32m 109\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mold_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/PycharmProjects/cobrapy/src/cobra/core/reaction.py\u001b[0m in \u001b[0;36mlower_bound\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m 368\u001b[0m \"\"\"\n\u001b[1;32m 369\u001b[0m \u001b[0;31m# Validate bounds before setting them.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 370\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_bounds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_upper_bound\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 371\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lower_bound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 372\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_variable_bounds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/PycharmProjects/cobrapy/src/cobra/core/reaction.py\u001b[0m in \u001b[0;36m_check_bounds\u001b[0;34m(lb, ub)\u001b[0m\n\u001b[1;32m 292\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlb\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mub\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 293\u001b[0m raise ValueError(\n\u001b[0;32m--> 294\u001b[0;31m \u001b[0;34mf\"The lower bound must be less than or equal to the upper bound \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 295\u001b[0m \u001b[0;34mf\"({lb} <= {ub}).\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 296\u001b[0m )\n", "\u001b[0;31mValueError\u001b[0m: The lower bound must be less than or equal to the upper bound (1100 <= 1000.0)." ] } ], "source": [ "old_bounds = pgi.bounds\n", "print('Upper bound prior to setting new lower bound:', pgi.upper_bound)\n", "pgi.lower_bound = 1100\n", "print('Upper bound after setting new lower bound:', pgi.upper_bound)\n", "pgi.bounds = old_bounds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also ensure the reaction is mass balanced. This function will return elements which violate mass balance. If it comes back empty, then the reaction is mass balanced." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pgi.check_mass_balance()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to add a metabolite, we pass in a `dict` with the metabolite object and its coefficient" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'g6p_c + h_c <=> f6p_c'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pgi.add_metabolites({model.metabolites.get_by_id(\"h_c\"): -1})\n", "pgi.reaction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The reaction is no longer mass balanced" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'charge': -1.0, 'H': -1.0}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pgi.check_mass_balance()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can remove the metabolite, and the reaction will be balanced once again." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "g6p_c <=> f6p_c\n", "{}\n" ] } ], "source": [ "pgi.subtract_metabolites({model.metabolites.get_by_id(\"h_c\"): -1})\n", "print(pgi.reaction)\n", "print(pgi.check_mass_balance())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to build the reaction from a string. However, care must be taken when doing this to ensure reaction id's match those in the model. The direction of the arrow is also used to update the upper and lower bounds." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "unknown metabolite 'green_eggs' created\n", "unknown metabolite 'ham' created\n" ] } ], "source": [ "pgi.reaction = \"g6p_c --> f6p_c + h_c + green_eggs + ham\"" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'g6p_c --> f6p_c + green_eggs + h_c + ham'" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pgi.reaction" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'g6p_c <=> f6p_c'" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pgi.reaction = \"g6p_c <=> f6p_c\"\n", "pgi.reaction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Metabolites" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will consider cytosolic atp as our metabolite, which has the id `\"atp_c\"` in our test model." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Metabolite identifieratp_c
NameATP
Memory address0x7feb1af41550
FormulaC10H12N5O13P3
Compartmentc
In 13 reaction(s)\n", " Biomass_Ecoli_core, ATPS4r, PGK, ACKr, ADK1, GLNS, ATPM, PFK, PPS, GLNabc, PYK, PPCK, SUCOAS\n", "
" ], "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "atp = model.metabolites.get_by_id(\"atp_c\")\n", "atp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can print out the metabolite name and compartment (cytosol in this case) directly as string." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ATP\n", "c\n" ] } ], "source": [ "print(atp.name)\n", "print(atp.compartment)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that ATP is a charged molecule in our model." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-4" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "atp.charge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see the chemical formula for the metabolite as well." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C10H12N5O13P3\n" ] } ], "source": [ "print(atp.formula)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The reactions attribute gives a `frozenset` of all reactions using the given metabolite. We can use this to count the number of reactions which use atp." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(atp.reactions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A metabolite like glucose 6-phosphate will participate in fewer reactions." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "frozenset({,\n", " ,\n", " ,\n", " })" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.metabolites.get_by_id(\"g6p_c\").reactions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Genes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `gene_reaction_rule` is a boolean representation of the gene requirements for this reaction to be active as described in [Schellenberger et al 2011 Nature Protocols 6(9):1290-307](http://dx.doi.org/doi:10.1038/nprot.2011.308).\n", "\n", "The GPR is stored as the GPR class in the gpr for a Reaction. A string representation of it is stored as the gene_reaction_rule for a Reaction object." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b4025\n", "b4025\n" ] } ], "source": [ "gpr = pgi.gpr\n", "print(gpr)\n", "gpr_string = pgi.gene_reaction_rule\n", "print(gpr_string)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Corresponding gene objects also exist. These objects are tracked by the reactions itself, as well as by the model" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "frozenset({})" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pgi.genes" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Gene identifierb4025
Namepgi
Memory address0x7feb1afb0f98
FunctionalTrue
In 1 reaction(s)\n", " PGI\n", "
" ], "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pgi_gene = model.genes.get_by_id(\"b4025\")\n", "pgi_gene" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each gene keeps track of the reactions it catalyzes" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "frozenset({})" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pgi_gene.reactions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Altering the gene_reaction_rule will create new gene objects if necessary and update all relationships." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "frozenset({, })" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pgi.gene_reaction_rule = \"(spam or eggs)\"\n", "pgi.genes" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "frozenset()" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pgi_gene.reactions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Newly created genes are also added to the model" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Gene identifierspam
Name
Memory address0x7feb1b0aacf8
FunctionalTrue
In 1 reaction(s)\n", " PGI\n", "
" ], "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.genes.get_by_id(\"spam\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `knock_out_model_genes` function will evaluate the GPR and set the upper and lower bounds to 0 if the reaction is knocked out." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "after 1 KO: -1000 < flux_PGI < 1000\n", "after 2 KO: 0 < flux_PGI < 0\n" ] } ], "source": [ "cobra.manipulation.knock_out_model_genes(\n", " model, [\"spam\"])\n", "print(\"after 1 KO: %4d < flux_PGI < %4d\" % (pgi.lower_bound, pgi.upper_bound))\n", "\n", "cobra.manipulation.knock_out_model_genes(\n", " model, [\"eggs\"])\n", "print(\"after 2 KO: %4d < flux_PGI < %4d\" % (pgi.lower_bound, pgi.upper_bound))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When knocking out model genes in a context, it is reversed when leaving the context." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quite often, one wants to make small changes to a model and evaluate the impacts of these. For example, we may want to knock-out all reactions sequentially, and see what the impact of this is on the objective function. One way of doing this would be to create a new copy of the model before each knock-out with `model.copy()`. However, even with small models, this is a very slow approach as models are quite complex objects. Better then would be to do the knock-out, optimizing and then manually resetting the reaction bounds before proceeding with the next reaction. Since this is such a common scenario however, cobrapy allows us to use the model as a context, to have changes reverted automatically." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ACALD blocked (bounds: (0, 0)), new growth rate 0.873922\n", "ACALDt blocked (bounds: (0, 0)), new growth rate 0.873922\n", "ACKr blocked (bounds: (0, 0)), new growth rate 0.873922\n", "ACONTa blocked (bounds: (0, 0)), new growth rate -0.000000\n", "ACONTb blocked (bounds: (0, 0)), new growth rate -0.000000\n" ] } ], "source": [ "model = load_model('textbook')\n", "for reaction in model.reactions[:5]:\n", " with model as model:\n", " reaction.knock_out()\n", " model.optimize()\n", " print('%s blocked (bounds: %s), new growth rate %f' %\n", " (reaction.id, str(reaction.bounds), model.objective.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we look at those knocked reactions, see that their bounds have all been reverted." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(-1000.0, 1000.0),\n", " (-1000.0, 1000.0),\n", " (-1000.0, 1000.0),\n", " (-1000.0, 1000.0),\n", " (-1000.0, 1000.0)]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[reaction.bounds for reaction in model.reactions[:5]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nested contexts are also supported" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "original objective: 1.0*Biomass_Ecoli_core - 1.0*Biomass_Ecoli_core_reverse_2cdba\n", "print objective in first context: 1.0*ATPM - 1.0*ATPM_reverse_5b752\n", "print objective in second context: 1.0*ACALD - 1.0*ACALD_reverse_fda2b\n", "objective after exiting second context: 1.0*ATPM - 1.0*ATPM_reverse_5b752\n", "back to original objective: 1.0*Biomass_Ecoli_core - 1.0*Biomass_Ecoli_core_reverse_2cdba\n" ] } ], "source": [ "print('original objective: ', model.objective.expression)\n", "with model:\n", " model.objective = 'ATPM'\n", " print('print objective in first context:', model.objective.expression)\n", " with model:\n", " model.objective = 'ACALD'\n", " print('print objective in second context:', model.objective.expression)\n", " print('objective after exiting second context:',\n", " model.objective.expression)\n", "print('back to original objective:', model.objective.expression)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most methods that modify the model are supported like this including adding and removing reactions and metabolites and setting the objective. Supported methods and functions mention this in the corresponding documentation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While it does not have any actual effect, for syntactic convenience it is also possible to refer to the model by a different name than outside the context. Such as" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "with model as inner:\n", " inner.reactions.PFK.knock_out" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 } cobrapy-0.29.1/documentation_builder/index.rst000066400000000000000000000014351467301562300214740ustar00rootroot00000000000000Documentation for COBRApy ========================= For installation instructions, please see `INSTALL.rst `_. Many of the examples below are viewable as IPython notebooks, which can be viewed at `nbviewer `_. .. toctree:: :numbered: 3 :maxdepth: 2 getting_started configuration building_model io simulating deletions phenotype_phase_plane sampling loopless consistency gapfilling media solvers constraints_objectives dfba pymatbridge faq API Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` cobrapy-0.29.1/documentation_builder/io.ipynb000066400000000000000000000414051467301562300213060ustar00rootroot00000000000000 { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Reading and Writing Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cobrapy supports reading and writing models in SBML (with and without FBC), JSON, YAML, MAT, and pickle formats. Generally, SBML with FBC version 2 is the preferred format for general use. The JSON format may be more useful for cobrapy-specific functionality.\n", "\n", "The package also ships with test models in various formats for testing purposes." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mini test files: \n", "/Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra/data/mini_fbc2.xml, /Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra/data/mini.json, /Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra/data/mini_fbc1.xml, /Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra/data/mini_fbc2.xml.gz, /Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra/data/mini.yml, /Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra/data/mini_cobra.xml, /Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra/data/mini.mat, /Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra/data/mini_fbc2.xml.bz2, /Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra/data/mini.pickle\n" ] } ], "source": [ "from pathlib import Path\n", "from cobra.io import load_json_model, save_json_model, load_matlab_model, save_matlab_model, read_sbml_model, write_sbml_model\n", "import logging\n", "\n", "data_dir = Path(\".\") / \"..\" / \"src\" / \"cobra\" / \"data\"\n", "data_dir = data_dir.resolve()\n", "\n", "print(\"mini test files: \")\n", "print(\", \".join(str(i) for i in data_dir.glob('mini*.*')))\n", "\n", "textbook_model = load_model(\"textbook\")\n", "ecoli_model = load_model(\"iJO1366\")\n", "logging.getLogger(\"cobra.io.sbml\").setLevel(logging.ERROR)", "salmonella_model = load_model(\"salmonella\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SBML" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [Systems Biology Markup Language](http://sbml.org) is an XML-based standard format for distributing models which has support for COBRA models through the [FBC extension](http://sbml.org/Documents/Specifications/SBML_Level_3/Packages/Flux_Balance_Constraints_%28flux%29) version 2.\n", "\n", "Cobrapy has native support for reading and writing SBML with FBCv2. Please note that all id's in the model must conform to the SBML SID requirements in order to generate a valid SBML file." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Namemini_textbook
Memory address0x07fbe29d69cf8
Number of metabolites23
Number of reactions18
Number of groups0
Objective expression1.0*ATPM - 1.0*ATPM_reverse_5b752 + 1.0*PFK - 1.0*PFK_reverse_d24a6
Compartmentscytosol, extracellular
" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mini_fbc2_path = data_dir / \"mini_fbc2.xml\" \n", "read_sbml_model(str(mini_fbc2_path.resolve()))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "write_sbml_model(textbook_model, \"test_fbc2.xml\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are other dialects of SBML prior to FBC 2 which have previously been use to encode COBRA models. The primary ones is the \"COBRA\" dialect which used the \"notes\" fields in SBML files.\n", "\n", "Cobrapy can use [libsbml](http://sbml.org/Software/libSBML), which must be installed separately (see installation instructions) to read and write these files. When reading in a model, it will automatically detect whether FBC was used or not. When writing a model, the use_fbc_package flag can be used can be used to write files in this legacy \"cobra\" format.\n", "\n", "Consider having the [lxml](http://lxml.de/) package installed as it can speed up parsing considerably." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Namemini_textbook
Memory address0x07fbe29d693c8
Number of metabolites23
Number of reactions18
Number of groups0
Objective expression1.0*ATPM - 1.0*ATPM_reverse_5b752 + 1.0*PFK - 1.0*PFK_reverse_d24a6
Compartmentscytosol, extracellular
" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mini_cobra_path = data_dir / \"mini_cobra.xml\" \n", "read_sbml_model(str(mini_cobra_path.resolve()))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "write_sbml_model(textbook_model, \"test_cobra.xml\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## JSON" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cobrapy models have a [JSON](https://en.wikipedia.org/wiki/JSON) (JavaScript Object Notation) representation. This format was created for interoperability with [escher](https://escher.github.io)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Namemini_textbook
Memory address0x07fbe29c6ac50
Number of metabolites23
Number of reactions18
Number of groups0
Objective expression1.0*ATPM - 1.0*ATPM_reverse_5b752 + 1.0*PFK - 1.0*PFK_reverse_d24a6
Compartmentscytosol, extracellular
" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mini_json_path = data_dir / \"mini.json\" \n", "load_json_model(str(mini_json_path.resolve()))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "save_json_model(textbook_model, \"test.json\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## YAML" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cobrapy models have a [YAML](https://en.wikipedia.org/wiki/YAML) (YAML Ain't Markup Language) representation. This format was created for more human readable model representations and automatic diffs between models." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Namemini_textbook
Memory address0x07fbe29c6ad30
Number of metabolites23
Number of reactions18
Number of groups0
Objective expression1.0*ATPM - 1.0*ATPM_reverse_5b752 + 1.0*PFK - 1.0*PFK_reverse_d24a6
Compartmentscytosol, extracellular
" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mini_yml_path = data_dir / \"mini.yml\"\n", "load_yaml_model(str(mini_yml_path.resolve()))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "save_yaml_model(textbook_model, \"test.yml\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MATLAB" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Often, models may be imported and exported solely for the purposes of working with the same models in cobrapy and the [MATLAB cobra toolbox](http://opencobra.github.io/cobratoolbox/). MATLAB has its own \".mat\" format for storing variables. Reading and writing to these mat files from python requires scipy.\n", "\n", "A mat file can contain multiple MATLAB variables. Therefore, the variable name of the model in the MATLAB file can be passed into the reading function:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Namemini_textbook
Memory address0x07fbe29c5dd30
Number of metabolites23
Number of reactions18
Number of groups0
Objective expression1.0*ATPM - 1.0*ATPM_reverse_5b752 + 1.0*PFK - 1.0*PFK_reverse_d24a6
Compartmentsc, e
" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mini_mat_path = data_dir / \"mini.mat\"\n", "load_matlab_model(\n", " str(mini_mat_path.resolve()),\n", " variable_name=\"mini_textbook\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the mat file contains only a single model, cobra can figure out which variable to read from, and the variable_name parameter is unnecessary." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Namemini_textbook
Memory address0x07fbe299af940
Number of metabolites23
Number of reactions18
Number of groups0
Objective expression1.0*ATPM - 1.0*ATPM_reverse_5b752 + 1.0*PFK - 1.0*PFK_reverse_d24a6
Compartmentsc, e
" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mini_mat_path = data_dir / \"mini.mat\"\n", "load_matlab_model(str(mini_mat_path.resolve()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Saving models to mat files is also relatively straightforward" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "save_matlab_model(textbook_model, \"test.mat\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pickle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cobra models can be serialized using the python serialization format, [pickle](https://docs.python.org/2/library/pickle.html).\n", "\n", "Please note that use of the pickle format is generally not recommended for most use cases. JSON, SBML, and MAT are generally the preferred formats." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 } cobrapy-0.29.1/documentation_builder/loopless.ipynb000066400000000000000000002305051467301562300225400ustar00rootroot00000000000000{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Loopless FBA" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The goal of this procedure is identification of a thermodynamically consistent flux state without loops, as implied by the name. You can find a more detailed description in the [method](#Method) section at the end of the notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import plot_helper\n", "\n", "from cobra.io import load_model\n", "from cobra import Reaction, Metabolite, Model\n", "from cobra.flux_analysis.loopless import add_loopless, loopless_solution\n", "from cobra.flux_analysis import pfba" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Loopless solution" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Classical loopless approaches as described below are computationally expensive to solve due to the added mixed-integer constraints. A much faster, and pragmatic approach is instead to post-process flux distributions to simply set fluxes to zero wherever they can be zero without changing the fluxes of any exchange reactions in the model. [CycleFreeFlux](http://dx.doi.org/10.1093/bioinformatics/btv096) is an algorithm that can be used to achieve this and in cobrapy it is implemented in the `cobra.flux_analysis.loopless_solution` function. `loopless_solution` will identify the closest flux distribution (using only loopless elementary flux modes) to the original one. Note that this will not remove loops which you explicitly requested, for instance by forcing a loop reaction to carry non-zero flux. \n", "\n", "Using a larger model than the simple example above, this can be demonstrated as follows" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "salmonella = load_model('salmonella')\n", "nominal = salmonella.optimize()\n", "loopless = loopless_solution(salmonella)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pandas\n", "\n", "df = pandas.DataFrame(dict(loopless=loopless.fluxes, nominal=nominal.fluxes))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/pdf": 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\n", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot.scatter(x='loopless', y='nominal')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "This functionality can also be used in FVA by using the `loopless=True` argument to avoid getting high flux ranges for reactions that essentially only can reach high fluxes if they are allowed to participate in loops (see the simulation notebook) leading to much narrower flux ranges." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Loopless model" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Cobrapy also includes the \"classical\" loopless formulation by [Schellenberger et. al.](https://dx.doi.org/10.1016%2Fj.bpj.2010.12.3707) implemented in `cobra.flux_analysis.add_loopless` modify the model with additional mixed-integer constraints that make thermodynamically infeasible loops impossible. This is much slower than the strategy provided above and should only be used if one of the two following cases applies:\n", "\n", "1. You want to combine a non-linear (e.g. quadratic) objective with the loopless condition\n", "2. You want to force the model to be infeasible in the presence of loops independent of the set reaction bounds.\n", "\n", "We will demonstrate this with a toy model which has a simple loop cycling A $\\rightarrow$ B $\\rightarrow$ C $\\rightarrow$ A, with A allowed to enter the system and C allowed to leave. A graphical view of the system is drawn below:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "application/pdf": 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\n", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_helper.plot_loop()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "model = Model()\n", "model.add_metabolites([Metabolite(i) for i in \"ABC\"])\n", "model.add_reactions([Reaction(i) for i in [\"EX_A\", \"DM_C\", \"v1\", \"v2\", \"v3\"]])\n", "\n", "model.reactions.EX_A.add_metabolites({\"A\": 1})\n", "model.reactions.DM_C.add_metabolites({\"C\": -1})\n", "\n", "model.reactions.v1.add_metabolites({\"A\": -1, \"B\": 1})\n", "model.reactions.v2.add_metabolites({\"B\": -1, \"C\": 1})\n", "model.reactions.v3.add_metabolites({\"C\": -1, \"A\": 1})\n", "\n", "model.objective = 'DM_C'" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "While this model contains a loop, a flux state exists which has no flux through reaction $v_3$, and is identified by loopless FBA." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loopless solution: status = optimal\n", "loopless solution flux: v3 = 0.0\n" ] } ], "source": [ "with model:\n", " add_loopless(model)\n", " solution = model.optimize()\n", "print(\"loopless solution: status = \" + solution.status)\n", "print(\"loopless solution flux: v3 = %.1f\" % solution.fluxes[\"v3\"])" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "If there is no forced flux through a loopless reaction, parsimonious FBA will also have no flux through the loop." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "parsimonious solution: status = optimal\n", "loopless solution flux: v3 = 0.0\n" ] } ], "source": [ "solution = pfba(model)\n", "print(\"parsimonious solution: status = \" + solution.status)\n", "print(\"loopless solution flux: v3 = %.1f\" % solution.fluxes[\"v3\"])" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "However, if flux is forced through $v_3$, then there is no longer a feasible loopless solution, but the parsimonious solution will still exist." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "model is infeasible\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/cdiener/code/cobrapy/src/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'.\n", " warn(f\"Solver status is '{status}'.\", UserWarning)\n" ] } ], "source": [ "model.reactions.v3.lower_bound = 1\n", "with model:\n", " add_loopless(model)\n", " try:\n", " solution = model.optimize()\n", " except:\n", " print('model is infeasible')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "parsimonious solution: status = optimal\n", "loopless solution flux: v3 = 1.0\n" ] } ], "source": [ "solution = pfba(model)\n", "print(\"parsimonious solution: status = \" + solution.status)\n", "print(\"loopless solution flux: v3 = %.1f\" % solution.fluxes[\"v3\"])" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Method" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "`loopless_solution` is based on a given reference flux distribution. It will look for a new flux distribution with the following requirements:\n", "\n", "1. The objective value is the same as in the reference fluxes.\n", "2. All exchange fluxes have the same value as in the reference distribution.\n", "3. All non-exchange fluxes have the same sign (flow in the same direction) as the reference fluxes.\n", "4. The sum of absolute non-exchange fluxes is minimized.\n", "\n", "As proven in the [original publication](http://dx.doi.org/10.1093/bioinformatics/btv096) this will identify the \"least-loopy\" solution closest to the reference fluxes.\n", "\n", "If you are using `add_loopless` this will use the method [described here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3030201/). In summary, it will add $G \\approx \\Delta G$ proxy variables and make loops thermodynamically infeasible. This is achieved by the following formulation.\n", "\n", "$$\n", "\\begin{aligned}\n", "&\\text{maximize } v_{obj} \\\\\n", "s.t. & Sv = 0\\\\\n", "& lb_j \\leq v_j \\leq ub_j \\\\\n", "& -M\\cdot (1 - a_i) \\leq v_i \\leq M\\cdot a_i\\\\\n", "& -1000a_i + (1 - a_i) \\leq G_i \\leq -a_i + 1000(1 - a_i)\\\\\n", "& N_{int}G = 0\\\\\n", "& a_i \\in \\{0, 1\\}\n", "\\end{aligned}\n", "$$\n", "\n", "Here the index j runs over all reactions and the index i only over internal ones. $a_i$ are indicator variables which equal one if the reaction flux flows in hte forward direction and 0 otherwise. They are used to force the G proxies to always carry the opposite sign of the flux (as it is the case for the \"real\" $\\Delta G$ values). $N_{int}$ is the nullspace matrix for internal reactions and is used to find thermodinamically \"correct\" values for G. \n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 1 } cobrapy-0.29.1/documentation_builder/make.bat000066400000000000000000000117601467301562300212420ustar00rootroot00000000000000@ECHO OFF REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set BUILDDIR=..\documentation set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . set I18NSPHINXOPTS=%SPHINXOPTS% . if NOT "%PAPER%" == "" ( set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% ) if "%1" == "" goto help if "%1" == "help" ( :help echo.Please use `make ^` where ^ is one of echo. html to make standalone HTML files echo. dirhtml to make HTML files named index.html in directories echo. singlehtml to make a single large HTML file echo. pickle to make pickle files echo. json to make JSON files echo. htmlhelp to make HTML files and a HTML help project echo. qthelp to make HTML files and a qthelp project echo. devhelp to make HTML files and a Devhelp project echo. epub to make an epub echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter echo. text to make text files echo. man to make manual pages echo. texinfo to make Texinfo files echo. gettext to make PO message catalogs echo. changes to make an overview over all changed/added/deprecated items echo. linkcheck to check all external links for integrity echo. doctest to run all doctests embedded in the documentation if enabled goto end ) if "%1" == "clean" ( for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i del /q /s %BUILDDIR%\* goto end ) if "%1" == "html" ( %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html if errorlevel 1 exit /b 1 echo. echo.Build finished. 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The HTML pages are in %BUILDDIR%/singlehtml. goto end ) if "%1" == "pickle" ( %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the pickle files. goto end ) if "%1" == "json" ( %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the JSON files. goto end ) if "%1" == "htmlhelp" ( %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run HTML Help Workshop with the ^ .hhp project file in %BUILDDIR%/htmlhelp. goto end ) if "%1" == "qthelp" ( %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run "qcollectiongenerator" with the ^ .qhcp project file in %BUILDDIR%/qthelp, like this: echo.^> qcollectiongenerator %BUILDDIR%\qthelp\cobra.qhcp echo.To view the help file: echo.^> assistant -collectionFile %BUILDDIR%\qthelp\cobra.ghc goto end ) if "%1" == "devhelp" ( %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp if errorlevel 1 exit /b 1 echo. echo.Build finished. goto end ) if "%1" == "epub" ( %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub if errorlevel 1 exit /b 1 echo. echo.Build finished. The epub file is in %BUILDDIR%/epub. goto end ) if "%1" == "latex" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex if errorlevel 1 exit /b 1 echo. echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. goto end ) if "%1" == "text" ( %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text if errorlevel 1 exit /b 1 echo. echo.Build finished. The text files are in %BUILDDIR%/text. goto end ) if "%1" == "man" ( %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man if errorlevel 1 exit /b 1 echo. echo.Build finished. The manual pages are in %BUILDDIR%/man. goto end ) if "%1" == "texinfo" ( %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo if errorlevel 1 exit /b 1 echo. echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. goto end ) if "%1" == "gettext" ( %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale if errorlevel 1 exit /b 1 echo. echo.Build finished. The message catalogs are in %BUILDDIR%/locale. goto end ) if "%1" == "changes" ( %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes if errorlevel 1 exit /b 1 echo. echo.The overview file is in %BUILDDIR%/changes. goto end ) if "%1" == "linkcheck" ( %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck if errorlevel 1 exit /b 1 echo. echo.Link check complete; look for any errors in the above output ^ or in %BUILDDIR%/linkcheck/output.txt. goto end ) if "%1" == "doctest" ( %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest if errorlevel 1 exit /b 1 echo. echo.Testing of doctests in the sources finished, look at the ^ results in %BUILDDIR%/doctest/output.txt. goto end ) :end cobrapy-0.29.1/documentation_builder/media.ipynb000066400000000000000000000430631467301562300217600ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Growth media\n", "\n", "The availability of nutrients has a major impact on metabolic fluxes and `cobrapy` provides some helpers to manage the exchanges between the external environment and your metabolic model. In experimental settings the \"environment\" is usually constituted by the growth medium, ergo the concentrations of all metabolites and co-factors available to the modeled organism. However, constraint-based metabolic models only consider fluxes. Thus, you can not simply use concentrations since fluxes have the unit `mmol / [gDW h]` (concentration per gram dry weight of cells and hour). \n", "\n", "Also, you are setting an upper bound for the particular import flux and not the flux itself. There are some crude approximations. For instance, if you supply 1 mol of glucose every 24h to 1 gram of bacteria you might set the upper exchange flux for glucose to `1 mol / [1 gDW * 24 h]` since that is the nominal maximum that can be imported. There is no guarantee however that glucose will be consumed with that flux. Thus, the preferred data for exchange fluxes are direct flux measurements as the ones obtained from timecourse exa-metabolome measurements for instance. \n", "\n", "So how does that look in COBRApy? The current growth medium of a model is managed by the `medium` attribute. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling...\n", " A: min|aij| = 1.000e+00 max|aij| = 1.000e+00 ratio = 1.000e+00\n", "Problem data seem to be well scaled\n" ] }, { "data": { "text/plain": [ "{'EX_co2_e': 1000.0,\n", " 'EX_glc__D_e': 10.0,\n", " 'EX_h_e': 1000.0,\n", " 'EX_h2o_e': 1000.0,\n", " 'EX_nh4_e': 1000.0,\n", " 'EX_o2_e': 1000.0,\n", " 'EX_pi_e': 1000.0}" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cobra.io import load_model\n", "\n", "model = load_model(\"textbook\")\n", "model.medium" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This will return a dictionary that contains the upper flux bounds for all active exchange fluxes (the ones having non-zero flux bounds). Right now we see that we have enabled aerobic growth. You can modify a growth medium of a model by assigning a dictionary to `model.medium` that maps exchange reactions to their respective upper import bounds. For now let us enforce anaerobic growth by shutting off the oxygen import." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'EX_co2_e': 1000.0,\n", " 'EX_glc__D_e': 10.0,\n", " 'EX_h_e': 1000.0,\n", " 'EX_h2o_e': 1000.0,\n", " 'EX_nh4_e': 1000.0,\n", " 'EX_pi_e': 1000.0}" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "medium = model.medium\n", "medium[\"EX_o2_e\"] = 0.0\n", "model.medium = medium\n", "\n", "model.medium" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see oxygen import is now removed from the list of active exchanges and we can verify that this also leads to a lower growth rate." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.21166294973530736" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.slim_optimize()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a small trap here. `model.medium` can not be assigned to directly. So the following will not work:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'EX_co2_e': 1000.0,\n", " 'EX_glc__D_e': 10.0,\n", " 'EX_h_e': 1000.0,\n", " 'EX_h2o_e': 1000.0,\n", " 'EX_nh4_e': 1000.0,\n", " 'EX_pi_e': 1000.0}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.medium[\"EX_co2_e\"] = 0.0\n", "model.medium" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see `EX_co2_e` is not set to zero. This is because model.medium is just a copy of the current exchange fluxes. Assigning to it directly with `model.medium[...] = ...` will **not** change the model. You have to assign an entire dictionary with the changed import flux upper bounds:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'EX_glc__D_e': 10.0,\n", " 'EX_h_e': 1000.0,\n", " 'EX_h2o_e': 1000.0,\n", " 'EX_nh4_e': 1000.0,\n", " 'EX_pi_e': 1000.0}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "medium = model.medium\n", "medium[\"EX_co2_e\"] = 0.0\n", "model.medium = medium\n", "\n", "model.medium # now it worked" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting the growth medium also connects to the context manager, so you can set a specific growth medium in a reversible manner." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.21166294973530736\n", "0.8739215069684102\n" ] }, { "data": { "text/plain": [ "{'EX_co2_e': 1000.0,\n", " 'EX_glc__D_e': 10.0,\n", " 'EX_h_e': 1000.0,\n", " 'EX_h2o_e': 1000.0,\n", " 'EX_nh4_e': 1000.0,\n", " 'EX_o2_e': 1000.0,\n", " 'EX_pi_e': 1000.0}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = load_model(\"textbook\")\n", "\n", "with model:\n", " medium = model.medium\n", " medium[\"EX_o2_e\"] = 0.0\n", " model.medium = medium\n", " print(model.slim_optimize())\n", "print(model.slim_optimize())\n", "model.medium" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So the medium change is only applied within the `with` block and reverted automatically." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Minimal media\n", "\n", "In some cases you might be interested in the smallest growth medium that can maintain a specific growth rate, the so called \"minimal medium\". For this we provide the function `minimal_medium` which by default obtains the medium with the lowest total import flux. This function needs two arguments: the model and the minimum growth rate (or other objective) the model has to achieve." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "EX_glc__D_e 10.000000\n", "EX_nh4_e 4.765319\n", "EX_o2_e 21.799493\n", "EX_pi_e 3.214895\n", "dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cobra.medium import minimal_medium\n", "\n", "max_growth = model.slim_optimize()\n", "minimal_medium(model, max_growth)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So we see that growth is actually limited by glucose import.\n", "\n", "Alternatively you might be interested in a minimal medium with the smallest number of active imports. This can be achieved by using the `minimize_components` argument (note that this uses a MIP formulation and will therefore be much slower)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "EX_glc__D_e 10.00000\n", "EX_nh4_e 0.54528\n", "EX_pi_e 0.36787\n", "dtype: float64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "minimal_medium(model, 0.1, minimize_components=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When minimizing the number of import fluxes there may be many alternative solutions. To obtain several of those you can also pass a positive integer to `minimize_components` which will give you at most that many alternative solutions. Let us try that with our model and also use the `open_exchanges` argument which will assign a large upper bound to all import reactions in the model. The return type will be a `pandas.DataFrame`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 1 2 3 4 \\\n", "EX_fru_e 0.000000 308.82944 0.00000 31.58596 0.000000 \n", "EX_glc__D_e 0.000000 0.00000 308.82944 0.00000 31.157104 \n", "EX_gln__L_e 0.000000 0.00000 0.00000 2.18112 0.000000 \n", "EX_glu__L_e 277.588056 6.10840 6.10840 0.00000 0.000000 \n", "EX_mal__L_e 0.000000 0.00000 0.00000 0.00000 0.000000 \n", "EX_nh4_e 0.000000 0.00000 0.00000 0.00000 4.362240 \n", "EX_o2_e 500.000000 0.00000 0.00000 0.00000 0.000000 \n", "EX_pi_e 46.944976 2.94296 2.94296 2.94296 2.942960 \n", "\n", " 5 \n", "EX_fru_e 0.000000 \n", "EX_glc__D_e 0.000000 \n", "EX_gln__L_e 18.848678 \n", "EX_glu__L_e 0.000000 \n", "EX_mal__L_e 1000.000000 \n", "EX_nh4_e 0.000000 \n", "EX_o2_e 0.000000 \n", "EX_pi_e 12.583458 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "minimal_medium(model, 0.8, minimize_components=8, open_exchanges=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So there are 4 alternative solutions in total. One aerobic and three anaerobic ones using different carbon sources." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Boundary reactions\n", "\n", "Apart from exchange reactions there are other types of boundary reactions such as demand or sink reactions. `cobrapy` uses various heuristics to identify those and they can be accessed by using the appropriate attribute.\n", "\n", "For exchange reactions:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[,\n", " ,\n", " ,\n", " ,\n", " ]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ecoli = load_model(\"iJO1366\")\n", "ecoli.exchanges[0:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For demand reactions:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ecoli.demands" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For sink reactions:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ecoli.sinks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All boundary reactions (any reaction that consumes or introduces mass into the system) can be obtained with the `boundary` attribute:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ecoli.boundary[0:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 } cobrapy-0.29.1/documentation_builder/phenotype_phase_plane.ipynb000066400000000000000000000704701467301562300252550ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Production envelopes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Production envelopes (aka phenotype phase planes) will show distinct phases of optimal growth with different use of two different substrates. For more information, see [Edwards et al.](http://dx.doi.org/10.1002/bit.10047)\n", "\n", "Cobrapy supports calculating these production envelopes and they can easily be plotted using your favorite plotting package. Here, we will make one for the \"textbook\" _E. coli_ core model and demonstrate plotting using [matplotlib](http://matplotlib.org/)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling...\n", " A: min|aij| = 1.000e+00 max|aij| = 1.000e+00 ratio = 1.000e+00\n", "Problem data seem to be well scaled\n" ] } ], "source": [ "from cobra.io import load_model\n", "from cobra.flux_analysis import production_envelope\n", "\n", "model = load_model(\"textbook\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to make a phenotype phase plane to evaluate uptakes of Glucose and Oxygen." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "prod_env = production_envelope(model, [\"EX_glc__D_e\", \"EX_o2_e\"])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " carbon_source flux_minimum carbon_yield_minimum mass_yield_minimum \\\n", "0 EX_glc__D_e 0.0 0.0 NaN \n", "1 EX_glc__D_e 0.0 0.0 NaN \n", "2 EX_glc__D_e 0.0 0.0 NaN \n", "3 EX_glc__D_e 0.0 0.0 NaN \n", "4 EX_glc__D_e 0.0 0.0 NaN \n", "\n", " flux_maximum carbon_yield_maximum mass_yield_maximum EX_glc__D_e \\\n", "0 0.000000 5.692701e-14 NaN -10.0 \n", "1 0.072244 1.310050e+00 NaN -10.0 \n", "2 0.144488 2.620100e+00 NaN -10.0 \n", "3 0.216732 3.930150e+00 NaN -10.0 \n", "4 0.288975 5.240200e+00 NaN -10.0 \n", "\n", " EX_o2_e \n", "0 -60.000000 \n", "1 -56.842105 \n", "2 -53.684211 \n", "3 -50.526316 \n", "4 -47.368421 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prod_env.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we specify the carbon source, we can also get the carbon and mass yield. For example, temporarily setting the objective to produce acetate instead we could get production envelope as follows and pandas to quickly plot the results." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "prod_env = production_envelope(\n", " model, [\"EX_o2_e\"], objective=\"EX_ac_e\", carbon_sources=\"EX_glc__D_e\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " carbon_source flux_minimum carbon_yield_minimum mass_yield_minimum \\\n", "0 EX_glc__D_e 0.0 0.0 0.0 \n", "1 EX_glc__D_e 0.0 0.0 0.0 \n", "2 EX_glc__D_e 0.0 0.0 0.0 \n", "3 EX_glc__D_e 0.0 0.0 0.0 \n", "4 EX_glc__D_e 0.0 0.0 0.0 \n", "\n", " flux_maximum carbon_yield_maximum mass_yield_maximum EX_o2_e \n", "0 0.000000 0.000000 0.000000 -60.000000 \n", "1 1.578947 0.052632 0.051748 -56.842105 \n", "2 3.157895 0.105263 0.103496 -53.684211 \n", "3 4.736842 0.157895 0.155245 -50.526316 \n", "4 6.315789 0.210526 0.206993 -47.368421 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prod_env.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "prod_env.plot(\n", " kind='line', x='EX_o2_e', y='carbon_yield_maximum');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Previous versions of cobrapy included more tailored plots for phase planes which have now been dropped in order to improve maintainability and enhance the focus of cobrapy. Plotting for cobra models is intended for another package." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 } cobrapy-0.29.1/documentation_builder/plot_helper.py000066400000000000000000000053651467301562300225300ustar00rootroot00000000000000# -*- coding: utf-8 -*- from matplotlib.pyplot import figure, xlim, ylim, gca, arrow, text, scatter from mpl_toolkits.axisartist.axislines import SubplotZero from numpy import linspace, arange, sqrt, pi, sin, cos, sign from IPython.display import set_matplotlib_formats set_matplotlib_formats('png', 'pdf') # axis style def make_plot_ax(): fig = figure(figsize=(6, 5)) ax = SubplotZero(fig, 111) fig.add_subplot(ax) for direction in ["xzero", "yzero"]: ax.axis[direction].set_axisline_style("-|>") ax.axis[direction].set_visible(True) for direction in ["left", "right", "bottom", "top"]: ax.axis[direction].set_visible(False) xlim(-0.1, 2.1) ylim(xlim()) ticks = [0.5 * i for i in range(1, 5)] labels = [str(i) if i == int(i) else "" for i in ticks] ax.set_xticks(ticks) ax.set_yticks(ticks) ax.set_xticklabels(labels) ax.set_yticklabels(labels) ax.axis["yzero"].set_axis_direction("left") return ax def plot_qp1(): ax = make_plot_ax() ax.plot((0, 2), (2, 0), 'b') ax.plot([1], [1], 'bo') # circular grid for r in sqrt(2.) + 0.125 * arange(-11, 6): t = linspace(0., pi/2., 100) ax.plot(r * cos(t), r * sin(t), '-.', color="gray") def plot_qp2(): ax = make_plot_ax() ax.plot((0, 2), (2, 0), 'b') ax.plot([0.5], [1.5], 'bo') yrange = linspace(1, 2, 11) for r in [y ** 2 / 2. - y for y in yrange]: t = linspace(-sqrt(2 * r + 1) + 0.000001, sqrt(2 * r + 1) - 0.000001, 1000) ax.plot(abs(t), [1 + sqrt(abs(2 * r + 1 - x ** 2)) * sign(x) for x in t], '-.', color="gray") def plot_loop(): figure(figsize=(10.5, 4.5), frameon=False) gca().axis("off") xlim(0.5, 3.5) ylim(0.7, 2.2) arrow_params = {"head_length": 0.08, "head_width": 0.1, "ec": "k", "fc": "k"} text_params = {"fontsize": 25, "horizontalalignment": "center", "verticalalignment": "center"} arrow(0.5, 1, 0.85, 0, **arrow_params) # EX_A arrow(1.5, 1, 0.425, 0.736, **arrow_params) # v1 arrow(2.04, 1.82, 0.42, -0.72, **arrow_params) # v2 arrow(2.4, 1, -0.75, 0, **arrow_params) # v3 arrow(2.6, 1, 0.75, 0, **arrow_params) # reaction labels text(0.9, 1.15, "EX_A", **text_params) text(1.6, 1.5, r"v$_1$", **text_params) text(2.4, 1.5, r"v$_2$", **text_params) text(2, 0.85, r"v$_3$", **text_params) text(2.9, 1.15, "DM_C", **text_params) # metabolite labels scatter(1.5, 1, s=250, color='#c994c7') text(1.5, 0.9, "A", **text_params) scatter(2, 1.84, s=250, color='#c994c7') text(2, 1.95, "B", **text_params) scatter(2.5, 1, s=250, color='#c994c7') text(2.5, 0.9, "C", **text_params) cobrapy-0.29.1/documentation_builder/requirements.txt000066400000000000000000000001341467301562300231120ustar00rootroot00000000000000Sphinx~=5.3 sphinxcontrib-napoleon sphinx-autoapi nbsphinx>=0.2.4 ipykernel matplotlib>=3.6 cobrapy-0.29.1/documentation_builder/sampling.ipynb000066400000000000000000000457621467301562300225230ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Flux sampling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic usage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The easiest way to get started with flux sampling is using the `sample` function in the `flux_analysis` submodule. `sample` takes at least two arguments: a cobra model and the number of samples you want to generate." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling...\n", " A: min|aij| = 1.000e+00 max|aij| = 1.000e+00 ratio = 1.000e+00\n", "Problem data seem to be well scaled\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " ACALD ACALDt ACKr ACONTa ACONTb ACt2r ADK1 \\\n", "0 -0.771296 -0.431093 -2.284246 6.735302 6.735302 -2.284246 2.273930 \n", "1 -2.089680 -1.099843 -0.386453 10.477790 10.477790 -0.386453 3.396770 \n", "2 -1.108346 -0.126460 -1.198639 5.057819 5.057819 -1.198639 7.154043 \n", "3 -1.239111 -0.334024 -1.284023 12.035499 12.035499 -1.284023 19.790232 \n", "4 -1.943290 -1.571257 -0.842072 11.035900 11.035900 -0.842072 15.963163 \n", "\n", " AKGDH AKGt2r ALCD2x ... RPI SUCCt2_2 SUCCt3 \\\n", "0 3.942050 -1.589360 -0.340203 ... -2.314416 6.997002 8.082133 \n", "1 3.163168 -1.592767 -0.989837 ... -1.756359 3.093051 3.415053 \n", "2 0.313155 -0.227554 -0.981886 ... -4.491355 7.873466 8.606818 \n", "3 1.359155 -0.007846 -0.905088 ... -2.063827 9.002800 10.772472 \n", "4 0.288986 -0.861444 -0.372033 ... -1.468256 7.188997 8.085781 \n", "\n", " SUCDi SUCOAS TALA THD2 TKT1 TKT2 TPI \n", "0 335.977726 -3.942050 2.152983 12.850348 2.152983 2.088065 7.542984 \n", "1 540.804734 -3.163168 1.657479 56.649368 1.657479 1.617715 8.029587 \n", "2 558.331088 -0.313155 4.295540 12.141283 4.295540 4.216795 5.181243 \n", "3 647.037371 -1.359155 2.016495 26.609381 2.016495 1.997461 7.712080 \n", "4 368.039875 -0.288986 1.465663 5.730886 1.465663 1.464620 8.298285 \n", "\n", "[5 rows x 95 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cobra.io import load_model\n", "from cobra.sampling import sample\n", "\n", "model = load_model(\"textbook\")\n", "s = sample(model, 100)\n", "s.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default sample uses the `optgp` method based on the [method presented here](http://dx.doi.org/10.1371/journal.pone.0086587) as it is suited for larger models and can run in parallel. By default the sampler uses a single process. This can be changed by using the `processes` argument." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "One process:\n", "CPU times: user 9.8 s, sys: 333 ms, total: 10.1 s\n", "Wall time: 9.21 s\n", "Two processes:\n", "CPU times: user 186 ms, sys: 41.2 ms, total: 227 ms\n", "Wall time: 5.26 s\n" ] } ], "source": [ "print(\"One process:\")\n", "%time s = sample(model, 1000)\n", "print(\"Two processes:\")\n", "%time s = sample(model, 1000, processes=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively you can also user Artificial Centering Hit-and-Run for sampling by setting the method to `achr`. `achr` does not support parallel execution but has good convergence and is almost Markovian." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "s = sample(model, 100, method=\"achr\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general setting up the sampler is expensive since initial search directions are generated by solving many linear programming problems. Thus, we recommend to generate as many samples as possible in one go. However, this might require finer control over the sampling procedure as described in the following section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Advanced usage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sampler objects" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The sampling process can be controlled on a lower level by using the sampler classes directly." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from cobra.sampling import OptGPSampler, ACHRSampler" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both sampler classes have standardized interfaces and take some additional argument. For instance the `thinning` factor. \"Thinning\" means only recording samples every n iterations. A higher thinning factors mean less correlated samples but also larger computation times. By default the samplers use a thinning factor of 100 which creates roughly uncorrelated samples. If you want less samples but better mixing feel free to increase this parameter. If you want to study convergence for your own model you might want to set it to 1 to obtain all iterates." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "achr = ACHRSampler(model, thinning=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`OptGPSampler` has an additional `processes` argument specifying how many processes are used to create parallel sampling chains. This should be in the order of your CPU cores for maximum efficiency. As noted before class initialization can take up to a few minutes due to generation of initial search directions. Sampling on the other hand is quick." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "optgp = OptGPSampler(model, processes=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sampling and validation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both samplers have a sample function that generates samples from the initialized object and act like the `sample` function described above, only that this time it will only accept a single argument, the number of samples. For `OptGPSampler` the number of samples should be a multiple of the number of processes, otherwise it will be increased to the nearest multiple automatically." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "s1 = achr.sample(100)\n", "\n", "s2 = optgp.sample(100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can call `sample` repeatedly and both samplers are optimized to generate large amount of samples without falling into \"numerical traps\". All sampler objects have a `validate` function in order to check if a set of points are feasible and give detailed information about feasibility violations in a form of a short code denoting feasibility. Here the short code is a combination of any of the following letters:\n", "\n", "- \"v\" - valid point\n", "- \"l\" - lower bound violation\n", "- \"u\" - upper bound violation\n", "- \"e\" - equality violation (meaning the point is not a steady state)\n", "\n", "For instance for a random flux distribution (should not be feasible):" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['le'], dtype=' 0.1) for s in optgp.batch(100, 10)]\n", "print(\"Usually {:.2f}% +- {:.2f}% grow...\".format(\n", " np.mean(counts) * 100.0, np.std(counts) * 100.0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adding constraints" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Flux sampling will respect additional contraints defined in the model. For instance we can add a constraint enforcing growth in asimilar manner as the section before." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "co = model.problem.Constraint(model.reactions.Biomass_Ecoli_core.flux_expression, lb=0.1)\n", "model.add_cons_vars([co])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note that this is only for demonstration purposes. usually you could set the lower bound of the reaction directly instead of creating a new constraint.*" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 0.106248\n", "1 0.116061\n", "2 0.113978\n", "3 0.179056\n", "4 0.117057\n", "5 0.111005\n", "6 0.182250\n", "7 0.114853\n", "8 0.128597\n", "9 0.160970\n", "Name: Biomass_Ecoli_core, dtype: float64\n" ] } ], "source": [ "s = sample(model, 10)\n", "print(s.Biomass_Ecoli_core)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see our new constraint was respected." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 4 } cobrapy-0.29.1/documentation_builder/simulating.ipynb000066400000000000000000001250721467301562300230560ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulating with FBA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simulations using flux balance analysis can be solved using `Model.optimize()`. This will maximize or minimize (maximizing is the default) flux through the objective reactions." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scaling...\n", " A: min|aij| = 1.000e+00 max|aij| = 1.000e+00 ratio = 1.000e+00\n", "Problem data seem to be well scaled\n" ] } ], "source": [ "from cobra.io import load_model\n", "model = load_model(\"textbook\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running FBA" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "solution = model.optimize()\n", "print(solution)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Model.optimize() function will return a Solution object. A solution object has several attributes:\n", "\n", " - `objective_value`: the objective value\n", " - `status`: the status from the linear programming solver\n", " - `fluxes`: a pandas series with flux indexed by reaction identifier. The flux for a reaction variable is the difference of the primal values for the forward and reverse reaction variables.\n", " - `shadow_prices`: a pandas series with shadow price indexed by the metabolite identifier." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, after the last call to `model.optimize()`, if the optimization succeeds it's status will be optimal. In case the model is infeasible an error is raised." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8739215069684307" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solution.objective_value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The solvers that can be used with cobrapy are so fast that for many small to mid-size models computing the solution can be even faster than it takes to collect the values from the solver and convert to them python objects. With `model.optimize`, we gather values for all reactions and metabolites and that can take a significant amount of time if done repeatedly. If we are only interested in the flux value of a single reaction or the objective, it is faster to instead use `model.slim_optimize` which only does the optimization and returns the objective value leaving it up to you to fetch other values that you may need." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.45 ms, sys: 8 µs, total: 1.46 ms\n", "Wall time: 1.47 ms\n" ] }, { "data": { "text/plain": [ "0.8739215069684307" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "model.optimize().objective_value" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 86 µs, sys: 0 ns, total: 86 µs\n", "Wall time: 88.9 µs\n" ] }, { "data": { "text/plain": [ "0.8739215069684307" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "model.slim_optimize()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analyzing FBA solutions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Models solved using FBA can be further analyzed by using summary methods, which output printed text to give a quick representation of model behavior. Calling the summary method on the entire model displays information on the input and output behavior of the model, along with the optimized objective." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Objective

1.0 Biomass_Ecoli_core = 0.8739215069684306

Uptake

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MetaboliteReactionFluxC-NumberC-Flux
glc__D_eEX_glc__D_e106100.00%
nh4_eEX_nh4_e4.76500.00%
o2_eEX_o2_e21.800.00%
pi_eEX_pi_e3.21500.00%

Secretion

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MetaboliteReactionFluxC-NumberC-Flux
co2_eEX_co2_e-22.811100.00%
h2o_eEX_h2o_e-29.1800.00%
h_eEX_h_e-17.5300.00%
" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition, the input-output behavior of individual metabolites can also be inspected using summary methods. For instance, the following commands can be used to examine the overall redox balance of the model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

nadh_c

C21H27N7O14P2

Producing Reactions

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PercentFluxReactionDefinition
13.14%5.064AKGDHakg_c + coa_c + nad_c --> co2_c + nadh_c + succoa_c
8.04%3.1Biomass_Ecoli_core1.496 3pg_c + 3.7478 accoa_c + 59.81 atp_c + 0.361 e4p_c + 0.0709 f6p_c + 0.129 g3p_c + 0.205 g6p_c + 0.2557 gln__L_c + 4.9414 glu__L_c + 59.81 h2o_c + 3.547 nad_c + 13.0279 nadph_c + 1.7867 oaa_c + 0.5191 pep_c + 2.8328 pyr_c + 0.8977 r5p_c --> 59.81 adp_c + 4.1182 akg_c + 3.7478 coa_c + 59.81 h_c + 3.547 nadh_c + 13.0279 nadp_c + 59.81 pi_c
41.58%16.02GAPDg3p_c + nad_c + pi_c <=> 13dpg_c + h_c + nadh_c
13.14%5.064MDHmal__L_c + nad_c <=> h_c + nadh_c + oaa_c
24.09%9.283PDHcoa_c + nad_c + pyr_c --> accoa_c + co2_c + nadh_c

Consuming Reactions

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PercentFluxReactionDefinition
100.00%-38.53NADH164.0 h_c + nadh_c + q8_c --> 3.0 h_e + nad_c + q8h2_c
" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.metabolites.nadh_c.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or to get a sense of the main energy production and consumption reactions" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

atp_c

C10H12N5O13P3

Producing Reactions

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PercentFluxReactionDefinition
66.58%45.51ATPS4radp_c + 4.0 h_e + pi_c <=> atp_c + h2o_c + 3.0 h_c
23.44%16.02PGK3pg_c + atp_c <=> 13dpg_c + adp_c
2.57%1.758PYKadp_c + h_c + pep_c --> atp_c + pyr_c
7.41%5.064SUCOASatp_c + coa_c + succ_c <=> adp_c + pi_c + succoa_c

Consuming Reactions

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PercentFluxReactionDefinition
12.27%-8.39ATPMatp_c + h2o_c --> adp_c + h_c + pi_c
76.46%-52.27Biomass_Ecoli_core1.496 3pg_c + 3.7478 accoa_c + 59.81 atp_c + 0.361 e4p_c + 0.0709 f6p_c + 0.129 g3p_c + 0.205 g6p_c + 0.2557 gln__L_c + 4.9414 glu__L_c + 59.81 h2o_c + 3.547 nad_c + 13.0279 nadph_c + 1.7867 oaa_c + 0.5191 pep_c + 2.8328 pyr_c + 0.8977 r5p_c --> 59.81 adp_c + 4.1182 akg_c + 3.7478 coa_c + 59.81 h_c + 3.547 nadh_c + 13.0279 nadp_c + 59.81 pi_c
0.33%-0.2235GLNSatp_c + glu__L_c + nh4_c --> adp_c + gln__L_c + h_c + pi_c
10.94%-7.477PFKatp_c + f6p_c --> adp_c + fdp_c + h_c
" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.metabolites.atp_c.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Changing the Objectives" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The objective function is determined from the objective_coefficient attribute of the objective reaction(s). Generally, a \"biomass\" function which describes the composition of metabolites which make up a cell is used." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "biomass_rxn = model.reactions.get_by_id(\"Biomass_Ecoli_core\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Currently in the model, there is only one reaction in the objective (the biomass reaction), with an linear coefficient of 1." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{: 1.0}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cobra.util.solver import linear_reaction_coefficients\n", "linear_reaction_coefficients(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The objective function can be changed by assigning Model.objective, which can be a reaction object (or just it's name), or a `dict` of `{Reaction: objective_coefficient}`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{: 1.0}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# change the objective to ATPM\n", "model.objective = \"ATPM\"\n", "\n", "# The upper bound should be 1000, so that we get\n", "# the actual optimal value\n", "model.reactions.get_by_id(\"ATPM\").upper_bound = 1000.\n", "linear_reaction_coefficients(model)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "174.99999999999983" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.optimize().objective_value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also have more complicated objectives including quadratic terms." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running FVA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "FBA will not give always give unique solution, because multiple flux states can achieve the same optimum. FVA (or flux variability analysis) finds the ranges of each metabolic flux at the optimum." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from cobra.flux_analysis import flux_variability_analysis" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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minimummaximum
ACALD-2.623542e-140.000000e+00
ACALDt-2.623542e-140.000000e+00
ACKr-4.012477e-140.000000e+00
ACONTa2.000000e+012.000000e+01
ACONTb2.000000e+012.000000e+01
ACt2r-4.012477e-140.000000e+00
ADK10.000000e+001.705303e-13
AKGDH2.000000e+012.000000e+01
AKGt2r-1.451321e-140.000000e+00
ALCD2x-2.273737e-140.000000e+00
\n", "
" ], "text/plain": [ " minimum maximum\n", "ACALD -2.623542e-14 0.000000e+00\n", "ACALDt -2.623542e-14 0.000000e+00\n", "ACKr -4.012477e-14 0.000000e+00\n", "ACONTa 2.000000e+01 2.000000e+01\n", "ACONTb 2.000000e+01 2.000000e+01\n", "ACt2r -4.012477e-14 0.000000e+00\n", "ADK1 0.000000e+00 1.705303e-13\n", "AKGDH 2.000000e+01 2.000000e+01\n", "AKGt2r -1.451321e-14 0.000000e+00\n", "ALCD2x -2.273737e-14 0.000000e+00" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flux_variability_analysis(model, model.reactions[:10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting parameter `fraction_of_optimium=0.90` would give the flux ranges for reactions at 90% optimality." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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minimummaximum
ACALD-2.6923080.0
ACALDt-2.6923080.0
ACKr-4.1176470.0
ACONTa8.46153820.0
ACONTb8.46153820.0
ACt2r-4.1176470.0
ADK10.00000017.5
AKGDH2.50000020.0
AKGt2r-1.4893620.0
ALCD2x-2.3333330.0
\n", "
" ], "text/plain": [ " minimum maximum\n", "ACALD -2.692308 0.0\n", "ACALDt -2.692308 0.0\n", "ACKr -4.117647 0.0\n", "ACONTa 8.461538 20.0\n", "ACONTb 8.461538 20.0\n", "ACt2r -4.117647 0.0\n", "ADK1 0.000000 17.5\n", "AKGDH 2.500000 20.0\n", "AKGt2r -1.489362 0.0\n", "ALCD2x -2.333333 0.0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cobra.flux_analysis.flux_variability_analysis(\n", " model, model.reactions[:10], fraction_of_optimum=0.9)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The standard FVA may contain loops, i.e. high absolute flux values that only can be high if they are allowed to participate in loops (a mathematical artifact that cannot happen in vivo). Use the `loopless` argument to avoid such loops. Below, we can see that FRD7 and SUCDi reactions can participate in loops but that this is avoided when using the looplesss FVA." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
minimummaximum
FRD70.0980.0
SUCDi20.01000.0
\n", "
" ], "text/plain": [ " minimum maximum\n", "FRD7 0.0 980.0\n", "SUCDi 20.0 1000.0" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loop_reactions = [model.reactions.FRD7, model.reactions.SUCDi]\n", "flux_variability_analysis(model, reaction_list=loop_reactions, loopless=False)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
minimummaximum
FRD70.00.0
SUCDi20.020.0
\n", "
" ], "text/plain": [ " minimum maximum\n", "FRD7 0.0 0.0\n", "SUCDi 20.0 20.0" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flux_variability_analysis(model, reaction_list=loop_reactions, loopless=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Running FVA in summary methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Flux variability analysis can also be embedded in calls to summary methods. For instance, the expected variability in substrate consumption and product formation can be quickly found by" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Objective

1.0 ATPM = 175.0

Uptake

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MetaboliteReactionFluxRangeC-NumberC-Flux
glc__D_eEX_glc__D_e10[9.5; 10]6100.00%
o2_eEX_o2_e60[55.88; 60]00.00%

Secretion

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MetaboliteReactionFluxRangeC-NumberC-Flux
ac_eEX_ac_e0[-2.059; 0]20.00%
acald_eEX_acald_e0[-1.346; 0]20.00%
akg_eEX_akg_e0[-0.7447; 0]50.00%
co2_eEX_co2_e-60[-60; -54.17]1100.00%
etoh_eEX_etoh_e0[-1.167; 0]20.00%
for_eEX_for_e0[-5.833; 0]10.00%
glu__L_eEX_glu__L_e0[-0.6731; 0]50.00%
h2o_eEX_h2o_e-60[-60; -54.17]00.00%
h_eEX_h_e0[-5.833; 0]00.00%
lac__D_eEX_lac__D_e0[-1.129; 0]30.00%
nh4_eEX_nh4_e0[0; 0.6731]00.00%
pi_eEX_pi_e0[0; 0.171]00.00%
pyr_eEX_pyr_e0[-1.346; 0]30.00%
succ_eEX_succ_e0[-0.875; 0]40.00%
" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.optimize()\n", "model.summary(fva=0.95)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, variability in metabolite mass balances can also be checked with flux variability analysis." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

pyr_c

C3H3O3

Producing Reactions

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PercentFluxRangeReactionDefinition
50.00%10[9.5; 10]GLCptsglc__D_e + pep_c --> g6p_c + pyr_c
0.00%0[-1.129; 0]LDH_Dlac__D_c + nad_c <=> h_c + nadh_c + pyr_c
0.00%0[0; 8.75]ME1mal__L_c + nad_c --> co2_c + nadh_c + pyr_c
0.00%0[0; 8.75]ME2mal__L_c + nadp_c --> co2_c + nadph_c + pyr_c
50.00%10[1.25; 18.75]PYKadp_c + h_c + pep_c --> atp_c + pyr_c
0.00%0[-1.346; 0]PYRt2h_e + pyr_e <=> h_c + pyr_c

Consuming Reactions

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PercentFluxRangeReactionDefinition
0.00%0[-0.1316; 0]Biomass_Ecoli_core1.496 3pg_c + 3.7478 accoa_c + 59.81 atp_c + 0.361 e4p_c + 0.0709 f6p_c + 0.129 g3p_c + 0.205 g6p_c + 0.2557 gln__L_c + 4.9414 glu__L_c + 59.81 h2o_c + 3.547 nad_c + 13.0279 nadph_c + 1.7867 oaa_c + 0.5191 pep_c + 2.8328 pyr_c + 0.8977 r5p_c --> 59.81 adp_c + 4.1182 akg_c + 3.7478 coa_c + 59.81 h_c + 3.547 nadh_c + 13.0279 nadp_c + 59.81 pi_c
100.00%-20[-28.75; -13]PDHcoa_c + nad_c + pyr_c --> accoa_c + co2_c + nadh_c
0.00%0[-5.833; 0]PFLcoa_c + pyr_c --> accoa_c + for_c
0.00%0[-8.75; 0]PPSatp_c + h2o_c + pyr_c --> amp_c + 2.0 h_c + pep_c + pi_c
" ], "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.metabolites.pyr_c.summary(fva=0.95)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In these summary methods, the values are reported as a the center point +/- the range of the FVA solution, calculated from the maximum and minimum values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running pFBA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parsimonious FBA (often written pFBA) finds a flux distribution which gives the optimal growth rate, but minimizes the total sum of flux. This involves solving two sequential linear programs, but is handled transparently by cobrapy. For more details on pFBA, please see [Lewis et al. (2010)](http://dx.doi.org/10.1038/msb.2010.47)." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "model.objective = 'Biomass_Ecoli_core'\n", "fba_solution = model.optimize()\n", "pfba_solution = cobra.flux_analysis.pfba(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These functions will likely give completely different objective values, because the objective value shown from pFBA is defined as `sum(abs(pfba_solution.fluxes.values))`, while the objective value for standard FBA is defined as the weighted flux through the reactions being optimized for (e.g. `fba_solution.fluxes[\"Biomass_Ecoli_core\"]`).\n", "\n", "Both pFBA and FBA should return identical results within solver tolerances for the objective being optimized. E.g. for a FBA problem which maximizes the reaction `Biomass_Ecoli_core`:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8.881784197001252e-16" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "abs(fba_solution.fluxes[\"Biomass_Ecoli_core\"] - pfba_solution.fluxes[\n", " \"Biomass_Ecoli_core\"])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "np.isclose(\n", " fba_solution.fluxes[\"Biomass_Ecoli_core\"], \n", " pfba_solution.fluxes[\"Biomass_Ecoli_core\"]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running geometric FBA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Geometric FBA finds a unique optimal flux distribution which is central to the range of possible fluxes. For more details on geometric FBA, please see [K Smallbone, E Simeonidis (2009)](http://dx.doi.org/10.1016/j.jtbi.2009.01.027)." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Optimal solution with objective value 0.000
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fluxesreduced_costs
ACALD0.000000e+000.0
ACALDt0.000000e+000.0
ACKr7.454685e-150.0
ACONTa6.007250e+000.0
ACONTb6.007250e+000.0
.........
TALA1.496984e+000.0
THD20.000000e+000.0
TKT11.496984e+000.0
TKT21.181498e+000.0
TPI7.477382e+000.0
\n", "

95 rows × 2 columns

\n", "
" ], "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "geometric_fba_sol = cobra.flux_analysis.geometric_fba(model)\n", "geometric_fba_sol" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 } cobrapy-0.29.1/documentation_builder/solvers.ipynb000066400000000000000000000063101467301562300223700ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Solvers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A constraint-based reconstruction and analysis model for biological systems is actually just an application of a class of discrete optimization problems typically solved with [linear, mixed integer](https://en.wikipedia.org/wiki/Linear_programming) or [quadratic programming](https://en.wikipedia.org/wiki/Quadratic_programming) techniques. Cobrapy does not implement any algorithm to find solutions to such problems but rather creates a biologically motivated abstraction to these techniques to make it easier to think of how metabolic systems work without paying much attention to how that formulates to an optimization problem.\n", "\n", "The actual solving is instead done by tools such as the free software [glpk](https://www.gnu.org/software/glpk/) or commercial tools [gurobi](http://www.gurobi.com/) and [cplex](https://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/) which are all made available as a common programmers interface via the [optlang](https://github.com/biosustain/optlang) package.\n", "\n", "When you have defined your model, you can switch solver backend by simply assigning to the `model.solver` property." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from cobra.io import load_model\n", "model = load_model('textbook')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "model.solver = 'glpk'\n", "# or if you have cplex installed\n", "model.solver = 'cplex'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For information on how to configure and tune the solver, please see the [documentation for optlang project](http://optlang.readthedocs.io) and note that `model.solver` is simply an optlang object of class `Model`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "optlang.cplex_interface.Model" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(model.solver)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Internal solver interfaces" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cobrapy also contains its own solver interfaces but these are now deprecated and will be removed completely in the near future. For documentation of how to use these, please refer to [older documentation](http://cobrapy.readthedocs.io/en/0.5.11/)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 } cobrapy-0.29.1/pyproject.toml000066400000000000000000000007261467301562300161720ustar00rootroot00000000000000[build-system] requires = [ "setuptools>=40.6.0", "wheel" ] build-backend = "setuptools.build_meta" [tool.black] line-length = 88 target-version = ["py38"] include = "\\.pyi?$" exclude = """ ( /( \\.eggs # exclude a few common directories in the | \\.git # root of the project | \\.hg | \\.mypy_cache | \\.tox | \\.venv | _build | buck-out | build | dist | release-notes | benchmarks )/ ) """ cobrapy-0.29.1/release-notes/000077500000000000000000000000001467301562300160175ustar00rootroot00000000000000cobrapy-0.29.1/release-notes/0.10.0.md000066400000000000000000000014371467301562300170620ustar00rootroot00000000000000# Release notes for cobrapy 0.10.0 ## Fixes * The solvers packaged with cobrapy have been removed. * cobra is now a pure Python package and no longer has compiled extensions. * Massively simplified continuous testing and deployment. * The `solver` argument and configuration keyword arguments have been removed. Please set the solver on the model and configure it before calling optimization functions. * The deprecated `ArrayBasedModel` has been removed. Please use the function `create_stoichiometric_matrix` instead. * `scipy` is no longer required for MOMA based methods. * A few deprecated functions have been removed. * Test cases have been adjusted. * Small adjustments to loopless FVA. * The deletion study interface and functions were improved and now use the optlang interface. cobrapy-0.29.1/release-notes/0.10.1.md000066400000000000000000000004661467301562300170640ustar00rootroot00000000000000# Release notes for cobrapy 0.10.1 ## Fixes * `pandas` has dropped support for Python 3.4 and we are doing the same for our automated tests. If you are running Python 3.4 and have a working installation of numpy and pandas, adding cobrapy on top of that is no problem. * Updated some URLs in the links. cobrapy-0.29.1/release-notes/0.11.0.md000066400000000000000000000007011467301562300170540ustar00rootroot00000000000000# Release notes for cobrapy 0.11.0 ## New features * Add an argument `processes` to the functions `find_essential_genes` and `find_essential_reactions`. * Automatic documentation generation should hopefully mean that it's never out-of-date. ## Backwards incompatible changes * Homogenize all function and class arguments using multiprocessing to accept an integer for `processes`. This affects functions from deletion and sampling code. cobrapy-0.29.1/release-notes/0.11.1.md000066400000000000000000000002221467301562300170530ustar00rootroot00000000000000# Release notes for cobrapy 0.11.1 ## Fixes * Catch all `optlang` errors and re-raise them as `OptimizationError` with corresponding message. cobrapy-0.29.1/release-notes/0.11.2.md000066400000000000000000000001731467301562300170610ustar00rootroot00000000000000# Release notes for cobrapy 0.11.2 ## Fixes * Correctly set the constraint for a previous minimization objective in FVA. cobrapy-0.29.1/release-notes/0.11.3.md000066400000000000000000000003131467301562300170560ustar00rootroot00000000000000# Release notes for cobrapy 0.11.3 ## Fixes * Improve convergence and stability of sampling. * Reverse inplace operations after leaving a context. ## Docs * Better description of custom constraints. cobrapy-0.29.1/release-notes/0.12.0.md000066400000000000000000000013521467301562300170600ustar00rootroot00000000000000# Release notes for cobrapy 0.12.0 ## New features * Add geometric FBA as a method. * Add (linear) ROOM as a flux simulation method. * Allow a reference solution to be used with deletion methods when using MOMA or ROOM. ## Fixes * Extend MOMA docstrings and add references. * Vastly improve the time needed for adding MOMA/ROOM constraints. * Copy metabolites added from other models. * Change `.summary` methods to use a fractional threshold. * Fix unexpected behavior in loopless FBA. * Some internal improvements related to dependencies and continuous integration. ## Backwards incompatible changes * The `find_essential_*` functions no longer accept a reference solution. As their method of choice *FBA* doesn't accept one anyway. cobrapy-0.29.1/release-notes/0.12.1.md000066400000000000000000000021131467301562300170550ustar00rootroot00000000000000# Release notes for cobrapy 0.12.1 ## New features * Fix/feature that allows summaries to work properly for models with multiple exchange reactions for the same metabolite. * Require a later optlang version so that Gurobi can now be used for quadratic problems. * cobrapy can now extract primal values from a wider range of solver states. This is particularly useful to investigate problems in non-optimal states. ## Fixes * Test different solvers on more text cases than previously. * Improve the way benchmarks are run. * Restore automatic documentation builds on readthedocs.org. * Correct a problem in how geomtric FBA retrieved solutions. * Update the use of pandas interface of the sampler. * Correct the sanity checks for the new ROOM implementation. ## Deprecated features ## Backwards incompatible changes * cobrapy functions that return solution only raise `OptimizationError`s rather than also potentially raising a `RuntimeError`. * Linear MOMA is the new default method. It used to be MOMA which excludes GLPK as a solver due to being a quadratic optimization problem. cobrapy-0.29.1/release-notes/0.13.0.md000066400000000000000000000010171467301562300170570ustar00rootroot00000000000000# Release notes for cobrapy 0.13.0 ## New features * New properties for the `cobra.Model` that give access to specific boundary reactions. * New functions for handling media and computing minimal media. Please read the new documentation chapter on media. ## Backwards incompatible changes * The model class has gained new properties `exchanges`, `demands`, `sinks`, and `boundary`. `boundary` mirrors the previous behavior of `exchanges` whereas the other properties return the more specific boundary reactions. cobrapy-0.29.1/release-notes/0.13.1.md000066400000000000000000000003461467301562300170640ustar00rootroot00000000000000# Release notes for cobrapy 0.13.1 ## Fixes * Improve type checking of boundary reactions, i.e., avoid errors when there are no such reactions. * Add test cases for those situations. * Correct some typos in the documentation. cobrapy-0.29.1/release-notes/0.13.2.md000066400000000000000000000001411467301562300170560ustar00rootroot00000000000000# Release notes for cobrapy 0.13.2 ## Fixes * Fix the PyPI deployment configuration on Travis. cobrapy-0.29.1/release-notes/0.13.3.md000066400000000000000000000001321467301562300170570ustar00rootroot00000000000000# Release notes for cobrapy 0.13.3 * Make a clean release for PyPI, website, and GitHub. cobrapy-0.29.1/release-notes/0.13.4.md000066400000000000000000000003451467301562300170660ustar00rootroot00000000000000# Release notes for cobrapy 0.13.4 ## Fixes * Internal re-organization of the test suite. * Upgrade the `ruamel.yaml` version making it compatible with Python 3.7. * Fix a bug with a regular expression in the Matlab interface. cobrapy-0.29.1/release-notes/0.14.0.md000066400000000000000000000026621467301562300170670ustar00rootroot00000000000000# Release notes for cobrapy 0.14.0 ## New features * Adding boundary reactions is now much more flexible and accepts user-defined identifiers. * It is now possible to run FVA and loopless FVA in parallel. * Some utilities and an example of how to perform static optimization dynamic FBA. * There now is a global configuration singleton `cobra.Configuration()` that can be used to set default bounds for newly created reactions and set a default solver for all newly created or parsed models. * Setting a time limit on solvers now still allows users to retrieve the flux distribution at the point of time out. ## Fixes * Infinity values are now properly handled and passed down to optlang. * Properly update the model objective on leaving a model context. * New metabolites are now included in the gapfilling process. ## Deprecated features * We are deprecating a special case of lower and upper bound behavior. Please read more about it in #793 and let us know what you think. ## Backwards incompatible changes * The old solver interfaces that were deprecated for a long time have been removed. ## Documentation * Better documentation for the minimal medium functionality. * Additions to the contribution guidelines. ## Internal * Improve indexing for `find_blocked_reactions`. * Adjust the test suite for pytest 4.0 and update the configuration. * The module structure and corresponding tests are broken up and re-organized. cobrapy-0.29.1/release-notes/0.14.1.md000066400000000000000000000001671467301562300170660ustar00rootroot00000000000000# Release notes for cobrapy 0.14.1 ## Fixes * Always create an XML declaration when reading SBML from a byte stream. cobrapy-0.29.1/release-notes/0.14.2.md000066400000000000000000000002711467301562300170630ustar00rootroot00000000000000# Release notes for cobrapy 0.14.2 ## Fixes * Better identification of the external compartment. * Fix the installation of all optional dependencies, i.e., `pip install cobra[all]`. cobrapy-0.29.1/release-notes/0.15.0.md000066400000000000000000000010121467301562300170540ustar00rootroot00000000000000# Release notes for cobrapy 0.15.0 ## New features * An updated libsbml-based SBML parser. * Parsing and writing SBML groups to and from SBML is now possible. Support for JSON is coming soon. * The global configuration now contains a default solver tolerance. This is applied to models automatically. * `cobra.Model` now has a tolerance property that can directly be manipulated. ## Fixes * Better support for SBO terms on SBML elements. ## Backwards incompatible changes * Annotations are now read as triples. cobrapy-0.29.1/release-notes/0.15.1.md000066400000000000000000000001231467301562300170570ustar00rootroot00000000000000# Release notes for cobrapy 0.15.1 ## Fixes * Fix copying of models with groups. cobrapy-0.29.1/release-notes/0.15.2.md000066400000000000000000000012621467301562300170650ustar00rootroot00000000000000# Release notes for cobrapy 0.15.2 ## New features * We introduce a new `tolerance` attribute both on the global `Configuration` object as well as `Model`. The default for this is 1E-07. Many functions that use a zero cut-off will now use the set model tolerance value by default. This value can still be set manually via function arguments but attempting to set a threshold that is lower than the model tolerance now raises an error. ## Fixes * Geometric FBA now has a `processes` argument with a default of 1 which can be used for parallel processing. * Clean up the sampling code and make it respect global defaults. * Many SBML improvements, please see #835 for details. cobrapy-0.29.1/release-notes/0.15.3.md000066400000000000000000000003141467301562300170630ustar00rootroot00000000000000# Release notes for cobrapy 0.15.3 ## Fixes * Correct multiple issues with the new SBML parser (#822, #839, #840, #843, #844). * Function `validate_sbml_model` properly captures errors and warnings. cobrapy-0.29.1/release-notes/0.15.4.md000066400000000000000000000003211467301562300170620ustar00rootroot00000000000000# Release notes for cobrapy 0.15.4 ## Fixes * This release brings a number of bug fixes for SBML I/O (#852). ## Backwards incompatible changes * The deprecated `objective_coefficient` was removed (#871). cobrapy-0.29.1/release-notes/0.16.0.md000066400000000000000000000007641467301562300170720ustar00rootroot00000000000000# Release notes for cobrapy 0.16.0 ## New features * Summaries now return an object with string and HTML representations instead of simply printing output (#855). This is a breaking change but should not affect anyone in a harmful way. ## Fixes * The documentation was repaired (#884). * Further SBML handling improvements (#881). * FASTCC implementation is now iterative (#827). * Change the yaml import and dependency (#894). ## Deprecated features * End of life for Python 2 announced. cobrapy-0.29.1/release-notes/0.17.0.md000066400000000000000000000003071467301562300170640ustar00rootroot00000000000000# Release notes for cobrapy 0.17.0 ## Fixes * Improve handling of and problems with SBML notes. * Log SBML messages to a module logger rather than the root logger. * Repair documentation building. cobrapy-0.29.1/release-notes/0.17.1.md000066400000000000000000000003101467301562300170570ustar00rootroot00000000000000# Release notes for cobrapy 0.17.1 ## Fixes * Prevent an error with infeasible solutions in (loopless) FVA. * Correct a sampling problem with Gurobi as the solver. * Adjust sampling documentation. cobrapy-0.29.1/release-notes/0.18.0.md000066400000000000000000000010551467301562300170660ustar00rootroot00000000000000# Release notes for cobrapy 0.18.0 ## New features * Support writing SBML notes on model objects ## Fixes * Test alternative solver * Handle repeated infeasible optimizations in `minimal_medium` * Allow newline characters in SBML notes ## Backwards incompatible changes * Change the container type of `groups` from `set` to `DictList` * Expect annotation attributes to be of type `dict` ## Internal * Update Travis CI configuration * Add Python 3.8 to PyPI classifiers ## Contributors * @BenjaSanchez * @cdiener * @ecederstrand * @Hemant27031999 cobrapy-0.29.1/release-notes/0.18.1.md000066400000000000000000000002701467301562300170650ustar00rootroot00000000000000# Release notes for cobrapy 0.18.1 ## Fixes * Use the correct SBO term for unbounded (infinite bounds) reactions. ## Other * Pin dependency on python-libsbml-experimental 5.18.1. cobrapy-0.29.1/release-notes/0.19.0.md000066400000000000000000000031371467301562300170720ustar00rootroot00000000000000# Release notes for cobrapy 0.19.0 We now have an official code of conduct and a committee of three people to handle reports! You can contact them at [conduct@opencobra.org](mailto:conduct@opencobra.org). You can also contact them individually at: * [Maureen Carey](mailto:MAC9JC@virginia.edu) * [Svetlana Kutuzova](mailto:svegal@biosustain.dtu.dk) * [Greg Medlock](mailto:GLM5UH@virginia.edu) Many thanks to them for taking on this responsibility. ## New features - Deletion result DataFrames as returned by functions from `cobra.deletions` now have a new `knockout` accessor. See the docs on usage examples. - All summaries are now persistent objects. The same summary can be displayed in multiple ways. The underlying data for the summary can also be accessed. - The model summary now displays elementary (by default carbon) uptake and secretion. - The JSON schema is now available as a stand-alone JSON file. ## Fixes - Remove the frozenset indexing in deletion DataFrames that is now unsupported in pandas. - Summaries for models, metabolites, and reactions are now calculated instantaneously and thus also work with model contexts. - JSON models can now contain infinity and NaN values as bounds. - The Matlab bridge for reading Matlab models should be restored. Any testing or problem reports are greatly appreciated. ## Deprecated features - We are progressively updating our codebase. That means that more and more parts will require Python 3.6+. ## Backwards incompatible changes - Deletion result DataFrames have no frozenset index anymore but now carry the deleted elements in the `ids` column. cobrapy-0.29.1/release-notes/0.20.0.md000066400000000000000000000012211467301562300170520ustar00rootroot00000000000000# Release notes for cobrapy 0.20.0 ## New features * There is a new function inspired by [cameo](https://github.com/biosustain/cameo/) that can load models directly from [BiGG](http://bigg.ucsd.edu/) or [BioModels](https://www.ebi.ac.uk/biomodels/) and by default will cache them locally (#995), for example, ```py from cobra.io import load_model ecoli = load_model("iML1515") ylipo = load_model("MODEL1604280017") ``` ## Fixes * Over a series of pull requests, @synchon has been modernizing parts of the codebase for Python 3.6+ (#993, #999, #1000, #1003) * Upgrade the dependency python-libsbml-experimental to 5.18.3 cobrapy-0.29.1/release-notes/0.21.0.md000066400000000000000000000002071467301562300170560ustar00rootroot00000000000000# Release notes for cobrapy 0.21.0 ## Other * Update libSBML dependency to `python-libsbml==5.19.0`. * Docstring and style changes. cobrapy-0.29.1/release-notes/0.22.0.md000066400000000000000000000021731467301562300170630ustar00rootroot00000000000000# Release notes for cobrapy 0.22.0 ## New features - Add support for the OSQP and COIN-OR CBC solvers (#946) ## Fixes - Fix the `Reaction.compartments` attribute not updating after metabolite changes (#1055) - Lots of syntax, annotation, and docstring fixes (#1058, #1059, #1060, #1063, #1065, #1066, #1076, #1077, #1080) - Multiprocessing on windows now sends data to processes via a pickle file which should speed up any multiprocessing initialization (#1070) - Upgrade optlang and fix some tolerance issues (#1011) - Enable Python 3.9 (#1082) ## Documentation - Document boundary reactions (#1038) - Documentation is now built again on each release (#1047) - Clarify pfba in the documentation (#1049) ## Other - COBRApy source code releases are now deposited on Zenodo which provides a citeable DOI (#1061) - COBRApy creates automatic releases to BioSimulators (#1064) - Fix deployment of releases to the website (#1068) - Improve the `pyproject.toml` config (#1067) ## Backwards incompatible changes - Remove some particular `cobra.Reaction.lower_bound` and `.upper_bound` behaviour that has been deprecated for a long time. cobrapy-0.29.1/release-notes/0.22.1.md000066400000000000000000000005431467301562300170630ustar00rootroot00000000000000# Release notes for cobrapy 0.22.1 ## Fixes - Fix an old bug with double gene deletion that was not knocking-out combinations (#1103). - Lots of syntax, annotation, and docstring fixes (#1081, #1083, #1084, #1085, #1086, #1088, #1090, #1091, #1092, #1093, #1094, #1095, #1096). ## Deprecated features - `find_gene_knockout_reactions` is deprecated. cobrapy-0.29.1/release-notes/0.23.0.md000066400000000000000000000004251467301562300170620ustar00rootroot00000000000000# Release notes for cobrapy 0.23.0 ## Fixes - Make compatible with Python 3.10 - Use the internal semantic GPR parser in the SBML module - Setting a growth medium will not reset export flux bounds anymore ## Other - Clean up code (#1116, #1114, #1113, #1111, #1110, #1097) cobrapy-0.29.1/release-notes/0.24.0.md000066400000000000000000000007251467301562300170660ustar00rootroot00000000000000# Release notes for cobrapy 0.24.0 ## New features * Gene-protein-reaction association rules are now represented by a structured object `GPR`. `Reaction.gene_reaction_rule` still returns a string and a string can be assigned to it that will modify the underlying `GPR` instance. * The `Reaction` class has gained another attribute `Reaction.gpr` which allows direct access to the `GPR` instance (#1143). ## Fixes * Syntax update and type annotations (#1151). cobrapy-0.29.1/release-notes/0.25.0.md000066400000000000000000000027051467301562300170670ustar00rootroot00000000000000# Release notes for cobrapy 0.25.0 ## New features * Add `knock_out_model_genes` to simulate knock-outs, setting reaction bounds of affected reactions to zero and returning a list of reactions that were knocked-out. Replaces `delete_model_genes` and `undelete_model_genes` since it is context sensitive. * Improve reading of matlab models which will include metabolite and reaction annotations. ## Fixes * `model.copy()` will now correctly copy GPRs. * Fix an error where matlab models can not be read if their bounds exceed the configuration default in some cases. * Fixed some bugs in `GPR().from_string()` where it was using the unmodified string, leading to errors with `GPR`s that should work. Made `GPR`s that have empty parenthesis fail more comprehensibly. ## Other * Move tests to a different directory, from `src/cobra/test` to `tests` (#1191). * Add two tests for `GPR` fixes: * `test_gpr_wrong_input()` * `test_gpr_that_needs_two_replacements()` * Clean up Python syntax (#1152, #1153, #1171, #1173, #1182) ## Deprecated features * Deprecate `delete_model_genes`, `undelete_model_genes` ## Backwards incompatible changes * Remove `find_gene_knockout_reactions` from `delete.py` * Remove `_find_gene_knockout_reactions_fast`, `_gene_knockout_computation`, `_get_removed ` from `test_delete.py` * Remove `pymatlib` direct transfer of models to matlab process. Please use `save_matlab_model()` and then read the model in matlab. cobrapy-0.29.1/release-notes/0.26.0.md000066400000000000000000000032111467301562300170610ustar00rootroot00000000000000# Release notes for cobrapy 0.26.0 ## New features * View number of genes in model notebook representation. * added support for Python 3.11 ## Fixes * serializes GPRs to strings to avoid massive storage usage * Reformatted example files (e_coli_core.xml, mini_cobra.xml, mini.json, mini.yml, textbook.xml.gz) to be more compliant with identifiers.org. * ncbigi is not a valid identifiers.org, so it was replaced with ncbiprotein. * make sbml.py subsystem reading add partonomy, which matches the definition of SBO:0000633 (see https://sourceforge.net/p/sbo/term-request/113/) * Correct reading and writing of subsystem in mat. * General cleanup of code in mat.py * fix the pandas deprecation warning in `find_external_compartment` * fix an issue where creating a Formula object would give a recursion error ## Other * Resolve `flake8` issues and add missing type annotations and docstrings in `src/cobra/io` and `tests/test_io` (#1212). * Updated model.py and test_model.py to Python 3.6+, including type annotations and docstrings. * Resolve remaining `flake8` issues and enable the check in GitHub Actions Lint workflow (#1272). ## Deprecated features * dropped support for Python 3.6 ## Backwards incompatible changes * Removed `model.add_reaction()` and replaced remaining usages of it with `model.add_reactions()` * Removed the following tests: test_add_remove_reaction_benchmark, test_add_reaction, test_add_reaction_context, test_add_reaction_from_other_model, test_add_cobra_reaction * Removed `model.__add__` and `model.__iadd__` - use `model.merge` to replace them. * Remove `Model().description()`. * Remove `Model().get_metabolite_compartments()`. cobrapy-0.29.1/release-notes/0.26.1.md000066400000000000000000000006201467301562300170630ustar00rootroot00000000000000# Release notes for cobrapy 0.26.1 ## New features ## Fixes Fix automatic building of the documentation. Fix an issue where SBML models with a "created" date would break lots of the cobrapy functionality. Temporary fix of restricting numpy to <1.24 as that release introduces breaking changes both in cobrapy and dependencies ## Other ## Deprecated features ## Backwards incompatible changes cobrapy-0.29.1/release-notes/0.26.2.md000066400000000000000000000002501467301562300170630ustar00rootroot00000000000000# Release notes for cobrapy x.y.z ## New features ## Fixes Fix the version numbers, my bad - cd ## Other ## Deprecated features ## Backwards incompatible changes cobrapy-0.29.1/release-notes/0.26.3.md000066400000000000000000000011231467301562300170640ustar00rootroot00000000000000# Release notes for cobrapy x.y.z ## New features ## Fixes Fixed an issue where reaction bounds were being overwritten by global model min/max values when writing sbml model to file. See [#1300](https://github.com/opencobra/cobrapy/pull/1312). Fix an issue where [`runfrog`](https://github.com/matthiaskoenig/fbc_curation/issues/98) does not work via github actions or local installation by removing the use of obsolete numpy aliases for `float` and `bool`. Fix a bug where installing httpx>=0.24 would break cobrapy IO. ## Other ## Deprecated features ## Backwards incompatible changes cobrapy-0.29.1/release-notes/0.27.0.md000066400000000000000000000010601467301562300170620ustar00rootroot00000000000000# Release notes for cobrapy 0.27.0 ## New features ## Fixes COBRApy is now compatible with pydantic version 2. COBRApy is now compatible with the latest numpy versions and pin to a lower version has been removed. `loopless_solution` now fixes the objective to its optimum as in the originally published method and returns the objective value in the solution object. Repair a broken test for `fix_objective_as_constraint`. ## Other Backwards compatibility for pickled models has been improved. ## Deprecated features ## Backwards incompatible changes cobrapy-0.29.1/release-notes/0.28.0.md000066400000000000000000000003651467301562300170720ustar00rootroot00000000000000# Release notes for cobrapy 0.28.0 ## Fixes * Allowed for pandas versions >=1.0 and <3.0 (#1364). ## Other * Updated the GitHub Actions used (#1364). ## Backwards incompatible changes * Updated the package `depinfo` to version >2 (#1361). cobrapy-0.29.1/release-notes/0.29.0.md000066400000000000000000000010301467301562300170610ustar00rootroot00000000000000# Release notes for cobrapy 0.29.0 ## New features * Added a new "hybrid" solver that exposes an HIGHS/OSQP combinations for large scale LPs, MILPs, and QPs. ## Fixes Repaired the broken Biomodels Web IO. ## Other ## Deprecated features * The OSQP solver is deprecated in favor of the hybrid solver which also uses OSQP but will not attempt to solve LPs with OSQP anymore. Setting the `model.solver = "osqp"` will now use the hybrid interface and will raise an error in a future release. ## Backwards incompatible changes cobrapy-0.29.1/release-notes/0.29.1.md000066400000000000000000000013031467301562300170650ustar00rootroot00000000000000# Release notes for cobrapy 0.29.1 ## New features ## Fixes * Fixes the incorrect bounds in the CycleFree loop removal. * Fixes reduced costs and shadow prices not available when using non-convex models. * Fixed a bug with SBML group parsing that affects the Debian package. * Fixed a bug where load_matlab_model would not work with numpy 2.0. ## Other * Adding a duplicate boundary reaction (with `add_boundary`) no longer errors, but instead just returns the existing reaction. * Automatic detection of the external compartment now recognizes CARVEME and GAPSEQ ids and will no longer show a warning for models generated with those tools. ## Deprecated features ## Backwards incompatible changes cobrapy-0.29.1/release-notes/0.6.0.md000066400000000000000000000217031467301562300170050ustar00rootroot00000000000000# Release notes for cobrapy 0.6.0 ## Highlights In this release we have made major changes to pretty much all corners of cobrapy and we hope that you will enjoy the new features as much as we do, and that any negative impacts on existing workflows will be limited. The major change is the ongoing move away from cobrapy's internal solver interfaces to those provided by [optlang](https://github.com/biosustain/optlang) which provides a single unified interface to glpk, cplex and gurobi enhanced by the ability to deal with symbolic expressions. This means formulating complex constraints no longer implies defining the necessary matrix algebra, but instead simply writing the expression and assigning that as an objective to the model. We feel that this, and the clarified scope and focus attained by separating the topic of linear programming (optlang) and metabolic flux analysis (cobrapy) to two packages is natural and makes both of these tasks less confusing and more fun. We hope that you after trying, feel the same and that in any case you let us know what you think by [raising an issue](https://github.com/opencobra/cobrapy/issues) or talking directly to us on [gitter](gitter.im/opencobra/cobrapy) or [google groups](https://groups.google.com/forum/#!forum/cobra-pie). ## New features ### The optlang solver interface The main change is the addition of `model.solver` which is the optlang interface to the chosen solver (cplex and glpk are currently well supported, gurobi interface is at the time of writing mostly working but improvements are still expected). The solver interface manages variables, constraints and the objective of the model and the task of turning these into a model formulation to be optimized by the solver. From cobrapy's point-of-view, this means that all aspects concerning generating problems, configuring solvers are handed over to optlang and consequently the whole `cobra.solver` has been deprecated, slated for removal in the next major release of cobrapy. Importantly, configuring the solver by passing `**solver_args` or `solver='solver'` is now instead done by assigning solver to `model.solver` and then configuring via `model.solver.configuration`. Creating new algorithms has been greatly facilitated as it no longer requires formulating objectives and constraints by matrix algebra but instead directly by expressions, e.g. see the implementation of `cobra.flux_analysis.moma.add_moma` and `cobra.flux_analysis.loopless.loopless_solution`. Instead of having only reactions as variables and metabolites as constraints, with optlang, cobrapy now supports arbitrary constraints and variables and these can be added/removed by `model.add_cons_vars` and `model.remove_cons_vars` which take care of adding these to `model.problem` which is the optlang's mathematical model associated with the cobra model. Reactions are now modeled by two variables, forward and reverse, and these can be seen by accessing `reaction.{forward,reverse}_variable` and the combined `reaction.flux_expression`. Objectives can now easily be made quite advanced by simply crafting the right expression and assigning this as usual to `model.objective`, see the [contraints and objectives notebook](http://cobrapy.readthedocs.io/en/latest/constraints_objectives.html). ### Temporary changes to a model Models are large complex objects and copying such objects is inevitably slow. To avoid that, cobrapy has drawn on the experience from the development of cameo to introduce the `HistoryManager` class and the concept of models as contexts. Now, most changes that can be made to a model such as changing the objective, setting reaction bounds, adding and removing reactions, is reversed upon exit when done inside a context, see the updated [getting started notebook](http://cobrapy.readthedocs.io/en/latest/getting_started.html). ### Improved solution handling Previously, cobra models lugged around their latest solution to enable providing `reaction.{flux,reduced_cost}` (formerly `reaction.{x,y}`). This was problematic because if the model had changed since last optimization, then this would effectively give the wrong result. On top of that, it was not easy to make a change, optimize and get values, and then undo that change to the model without having to copy the whole model object. To solve this, and many similar problem, we have completely refactored `cobra.Solution` so that `model.optimize()` now returns a solution and it is the user's responsibility to manage this object. `reaction.flux` gets its values directly from the `model.problem`. To sugar the new solution class, fluxes, reduced costs, and shadow prices are now pandas series! Fluxes and reduced costs can be returned as a data frame directlt with the `to_frame` method. ## Sampling Cobrapy now has flux sampling supported by `cobra.flux_analysis.sampling` see [the sampling notebook](http://cobrapy.readthedocs.io/en/latest/sampling.html). ## Loopless models and solutions Added implementations of [CycleFreeFlux](http://dx.doi.org/10.1093/bioinformatics/btv096) and the loopless model of [Schellenberger et al.](http://dx.doi.org/10.1016/j.bpj.2010.12.3707). See the [notebook on loopless](http://cobrapy.readthedocs.io/en/latest/loopless.html) and [simulating](http://cobrapy.readthedocs.io/en/latest/simulating.html) ## DataFrames as return values `flux_variability_analysis`, `single_{gene,reaction}_deletion`, `cobra.flux_analysis.sampling` and `cobra.util.create_stoichiometric_matrix` now return pandas data frames instead of nested dicts as these are more convenient and fun to work with. Pandas (and numpy) are therefore now hard requirements for cobrapy, which should not be a problem for neither linux, windows or mac users as there are reliable wheels for these packages now. ### Model medium `model.medium` is now a dict and setter for getting boundary feeding reactions and their bounds ### Knocking out genes Addition of `cobra.core.Gene.knock_out` which can be used to evaluate impact of knocking a gene (and all depending reactions). ### Adding boundary reactions The model class has new method `model.add_boundary` which can be used to add sink, exchange or demand reactions with the appropriate bounds and prefixes (DM, SK or EX). ### Gapfilling The `SMILEY` and `growMatch` implementations were refactored to a single new function `cobra.flux_analysis.gapfilling.gapfill` which handles both use-cases. ### New Output Format in YAML Models can now be round tripped to/from YAML documents. YAML is a file format that is even more legible than JSON. In the scope of cobrapy, YAML output is intended for `diff` comparisons between models. ## Fixes - Handle multiple IDs in Matlab models [#345](https://github.com/opencobra/cobrapy/issues) - `DictList.query` behavior changed so that attribute is `None` if the search parameter is not a regex or string, to enable `reactions.query(lambda x: x.boundary)` - Set charge from notes if not defined elsewhere [#352](https://github.com/opencobra/cobrapy/issues) - Warnings are no longer issued on package import if soft requirement scipy, python-libsbml is not available. ## Deprecated features These features are now deprecated and slated for complete removal in the next major cobrapy release. - The whole `cobra.solver` module is now deprecated, see New features. - `ArrayBasedModel` / `Model.to_array_based_model` are deprecated. This formulation makes little sense when handing over the matrix algebra to optlang, for the stoichiometry matrix (aka S), see `cobra.util.array.create_stoichiometric_matrix`. - `Metabolite.y` in favor of `Metabolite.shadow_price` - `Model.add_reaction` in favor of `Model.add_reactions` - `Reaction.x` in favor of `Reaction.flux` - `Reaction.y` in favor of `Reaction.reduced_cost` - `Solution.{x, y, x_dict, y_dict, f}` in favor of `Solution.{fluxes, reduced_costs}`. The setters are also deprecated. - `phenotype_phase_plane` in favor of `production_envelope`. The plotting capabilities are deprecated, to be re-implemented somewhere else. - `convert_to_irreverible`, `revert_to_irreversible`, `canonical_form` deprecated without replacement. - `check_reaction_bounds` deprecated without replacement. - `optimize_minimal_flux` was renamed to `pfba` ## Backwards incompatible changes - optknock was completely removed, users are advised to use cameo for this functionality - dual_problem was removed - `cobra.topology` was removed, possibly to be reintroduced in a different package - flux_variability_analysis results must be transformed to have them work as the previous nested dict, i.e. `flux_variability_analysis(model).T` should give behavior as previously. - In a major linting effort we renamed capitalized modules to lower-case, e.g. `cobra.core.Model` to `cobra.core.model`. Imports from `cobra` are unchanged though. - objective coefficients of reactions can now only be set once the reaction is attached to a model. - `Reaction.{x,y}`, `Metabolite.y` are defunct for legacy solvers. - `SMILEY` and `growMatch` algorithms are defunct in combination with the legacy solvers. cobrapy-0.29.1/release-notes/0.6.1.md000066400000000000000000000004501467301562300170020ustar00rootroot00000000000000# Release notes for cobrapy 0.6.1 ## Fixes - Debug `metabolite.add_metabolites` with `combine=False` [#503](https://github.com/opencobra/cobrapy/pull/503) - Fix broken printing of metabolites ([#502](https://github.com/opencobra/cobrapy/issues/502)), by removing unnecessary `Frozendict` cobrapy-0.29.1/release-notes/0.6.2.md000066400000000000000000000023521467301562300170060ustar00rootroot00000000000000# Release notes for cobrapy 0.6.2 ## Fixes - Fix in the ordering and rounding of FVA summary [#525](https://github.com/opencobra/cobrapy/pull/525) - Shared memory, improve speed during sampling [#521](https://github.com/opencobra/cobrapy/pull/521) - Debug `model.remove_reactions` to properly work with context manager. This lead to the deprecation of `reaction.delete` as this was not compatible with the concept of being able to later revert the change. [#506](https://github.com/opencobra/cobrapy/issues/506), [#508](https://github.com/opencobra/cobrapy/pull/508). - Adding two models (`modela + modelb`) again results in a model with the objective set to the sum of the two models objectives [#505](https://github.com/opencobra/cobrapy/issues/505). - When adding reactions to a model, the reactions with identifiers identical to those in the model are ignored. [#511](https://github.com/opencobra/cobrapy/issues/511) ## New features - `model.merge` can be used to merge two models, more flexibly than the overloaded + and += operators. ## Deprecated features - `reaction.delete` has been deprecated in favor of `reaction.remove_from_model` - overloaded `+` and `+=` for `cobra.Model` are deprecated in favor of `model.merge` cobrapy-0.29.1/release-notes/0.7.0.md000066400000000000000000000015641467301562300170110ustar00rootroot00000000000000# Release notes for cobrapy 0.7.0 ## Fixes - `cobra.flux_analysis.reaction.assess` [was broken](https://github.com/opencobra/cobrapy/issues/537) following the release of 0.6.0 and has now been fixed (and now with unit tests). - `production_envelope` failed when model C-source was formulated as -> x instead of x <-. Fixed added option to guess the C-source by taking the medium reaction with the highest input C flux. - `model_to_pymatbridge` needs scipy and that's correctly handled now. ## New features - `flux_variability_analysis` now has the `pfba_factor` parameter which enables the inclusion of a constraint on the max sum of absolute fluxes when doing FVA. ## Deprecated features - `cobra.flux_analysis.reaction.assess_{precursors,products}` were essentially copies of each other and have been merged to `cobra.flux_analysis.reaction.assess_component` cobrapy-0.29.1/release-notes/0.8.0.md000066400000000000000000000020461467301562300170060ustar00rootroot00000000000000# Release notes for cobrapy 0.8.0 ## Fixes - Tests no longer generates warnings about using deprecated functions. - Gapfilling integrality thresholds now supported for all optlang solvers. ## New features - `Model.slim_optimize()` can be used perform optimization without creating a solution. Can lead to significant speedup compared to `Model.optimize` when repeatedly doing optimizations and only making use of the objective value as avoiding the need to fetch all values from the solver object. - solution, model, metabolite and reaction now have html representation so they give more informative prints in jupyter notebooks. - New convenience functions `cobra.flux_analysis.find_essential_genes` and `cobra.flux_analysis.find_essential_reactions`. - `Model.optimize` has new parameter `raise_error` to enable option to get trigger exception if no feasible solution could be found. - `str(reaction)` now gives the more useful reaction id and the reaction string. ## Deprecated features - `str(reaction)` no longer gives `reaction.id`. cobrapy-0.29.1/release-notes/0.8.1.md000066400000000000000000000007171467301562300170120ustar00rootroot00000000000000# Release notes for cobrapy 0.8.1 ## Fixes - Fix bug related to inhomogeneous sampling [#558](https://github.com/opencobra/cobrapy/issues/558). During flux sampling, store the nullspace rather than the entire projection (N * N.T) which reduces the memory footprint since it uses memory in the order of `2 * n_reaction * n_metabolites` instead of `4 * n_reactions^2` (no change in speed). [PR #556](https://github.com/opencobra/cobrapy/pull/556) cobrapy-0.29.1/release-notes/0.8.2.md000066400000000000000000000021771467301562300170150ustar00rootroot00000000000000# Release notes for cobrapy 0.8.2 ## Fixes - Guarantee that sampler._reproject always returns a feasible point and will not attempt to reproject already feasible points. [#564](https://github.com/opencobra/cobrapy/pull/564) - `Model.summary` no longer fails when calling after the model has changed. Fixed by letting the summary function re-compute a solution (default) or letting user supply a prior computed solution object. [#566](https://github.com/opencobra/cobrapy/pull/566) - Metabolites must now have valid identifiers before being added to a model or `ValueError` is raised. - Fix use of underscores in key/value pairs in legacy sbml notes. [#547](https://github.com/opencobra/cobrapy/issues/547) ## Backwards incompatible changes - the Solution class no longer contains links progenitor model's reactions and metabolites. Removed since it those can change after the solution has been computed making them erroneous. This of course implies that `Solution` constructor changes to: ``` def __init__(self, objective_value, status, fluxes, reduced_costs=None, shadow_prices=None, **kwargs): ``` cobrapy-0.29.1/release-notes/0.9.0.md000066400000000000000000000031211467301562300170020ustar00rootroot00000000000000# Release notes for cobrapy 0.9.0 ## Fixes - `Model.compartment` is now a dynamic property fetching the compartments from all metabolites therefore always up-to-date. Assigning a dictionary to the same property updates the internal dictionary of compartment descriptions. This change removes the need for the check for missing compartments from `validation.check_metabolite_compartment_formula`. - Excessively long output of html representations in jupyter notebooks are now abbreviated [#577](https://github.com/opencobra/cobrapy/pull/577). - Reaction forward and reverse variables are no longer cached with those object. No visible effect but simplifies the code. - Fix bug in summary methods when used with exchange reaction sas objective. [#595](https://github.com/opencobra/cobrapy/pull/595). ## New features - `Model.objective_direction` is a new revertible property to set maximization / minimization using the context manager. - Change output of `production_envelope` to wide data frame format [#587](https://github.com/opencobra/cobrapy/pull/587). Also allow multiple carbon source reactions and better handling of zero-division exceptions. - Enable summing lists of reactions, see [#596](https://github.com/opencobra/cobrapy/pull/596) ## Deprecated features - `Model.get_metabolite_compartments` is deprecated (use `Model.compartments` instead). - `Reaction.get_compartments` is deprecated (use `Reaction.compartments` instead). ## Backwards incompatible changes - The format of the dataframe `production_envelope` changed listing max and min on different columns instead of the same. cobrapy-0.29.1/release-notes/0.9.1.md000066400000000000000000000013521467301562300170070ustar00rootroot00000000000000# Release notes for cobrapy 0.9.1 ## Fixes * All around improvements to cobrapy's continuous integration. * Pin the scipy version to 0.19.1 until we can upgrade our sparse matrix code to scipy 1.0. ## New features * Compatibility with symengine. Symengine can now power optlang and thus make many simulation tasks faster. Please try it out! It's as simple as `pip install symengine`. * When writing to JSON or YAML metabolites, reactions, and genes by default maintain the order of the model. Their annotations are alphabetically sorted. * Duplicate reactions are now properly ignored. They were causing `ValueError`s before. * Automatic GitHub releases upon tagged releases. * Automatic deployment of website upon tagged releases. cobrapy-0.29.1/release-notes/next-release.md000066400000000000000000000002021467301562300207270ustar00rootroot00000000000000# Release notes for cobrapy x.y.z ## New features ## Fixes ## Other ## Deprecated features ## Backwards incompatible changes cobrapy-0.29.1/scripts/000077500000000000000000000000001467301562300147405ustar00rootroot00000000000000cobrapy-0.29.1/scripts/compare-benchmark.py000066400000000000000000000025361467301562300206760ustar00rootroot00000000000000import argparse import json import re from os.path import basename import pandas as pd pd.set_option("display.width", 200) def benchmark_to_df(json_file): with open(json_file) as jf: content = json.load(jf) df = pd.DataFrame(columns=("test", "time [ms] ")) for b in content["benchmarks"]: df = df.append( {"test": b["name"], "time [ms] ": b["stats"]["mean"] * 1000.0}, ignore_index=True, ) return df if __name__ == "__main__": parser = argparse.ArgumentParser( description=""" compare cobrapy benchmarks. Run pytest with pytest --benchmark-save=without-cache --benchmark-min-rounds=20 then compare saved json files with this script. """ ) parser.add_argument("first", help="first json file") parser.add_argument("second", help="second json file") args = parser.parse_args() first = benchmark_to_df(args.first) second = benchmark_to_df(args.second) re_name = "^[0-9]+_(.+).json$" first_name = re.findall(re_name, basename(args.first))[0] second_name = re.findall(re_name, basename(args.second))[0] both = pd.merge( first, second, how="inner", on="test", suffixes=(first_name, second_name) ) both["fraction"] = both.iloc[:, 2] / both.iloc[:, 1] print(both.sort_values(by="fraction")) cobrapy-0.29.1/scripts/deploy_website.sh000077500000000000000000000011651467301562300203200ustar00rootroot00000000000000#!/usr/bin/env bash set -eu target="${HOME}/cobrapy-website" git clone "https://github.com/opencobra/cobrapy-website.git" "${target}" pushd "${target}" git config user.name "COBRApy Bot" git config user.email "opencobra.cobrapy@gmail.com" git remote rm origin git remote add origin "https://user:${WEBSITE_DEPLOY_TOKEN}@github.com/opencobra/cobrapy-website.git" &> /dev/null git checkout master python "${WORKSPACE}/scripts/publish_release.py" "${WORKSPACE}/release-notes" "${target}/content/releases" "${TAG}" git add . git commit -m "feat: publish ${TAG} on $(date --utc --iso-8601=seconds)" git push origin master popd cobrapy-0.29.1/scripts/publish_release.py000066400000000000000000000066201467301562300204640ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- """Insert a TOML header into the latest release note.""" from __future__ import absolute_import, print_function import sys from datetime import date from glob import glob from os.path import basename, join from shutil import copy def insert_break(lines, break_pos=9): """ Insert a tag for larger release notes. Parameters ---------- lines : list of str The content of the release note. break_pos : int Line number before which a break should approximately be inserted. Returns ------- list of str The text with the inserted tag or no modification if it was sufficiently short. """ def line_filter(line): if len(line) == 0: return True return any(line.startswith(c) for c in "-*+") if len(lines) <= break_pos: return lines newlines = [ i for i, line in enumerate(lines[break_pos:], start=break_pos) if line_filter(line.strip()) ] if len(newlines) > 0: break_pos = newlines[0] lines.insert(break_pos, "\n") return lines def build_hugo_md(filename, tag, bump): """ Build the markdown release notes for Hugo. Inserts the required TOML header with specific values and adds a break for long release notes. Parameters ---------- filename : str, path The release notes file. tag : str The tag, following semantic versioning, of the current release. bump : {"major", "minor", "patch", "alpha", "beta"} The type of release. """ header = [ "+++\n", 'date = "{}"\n'.format(date.today().isoformat()), 'title = "{}"\n'.format(tag), 'author = "The COBRApy Team"\n', 'release = "{}"\n'.format(bump), "+++\n", "\n", ] with open(filename, "r") as file_h: content = insert_break(file_h.readlines()) header.extend(content) with open(filename, "w") as file_h: file_h.writelines(header) def intify(filename): """ Turn a release note filename into something sortable. Parameters ---------- filename : str A release note of expected filename format '...md'. Returns ------- tuple A pair of the major and minor versions as integers. """ tmp = filename[:-3].split(".") return int(tmp[0]), int(tmp[1]) def find_bump(target, tag): """Identify the kind of release by comparing to existing ones.""" tmp = tag.split(".") existing = [intify(basename(f)) for f in glob(join(target, "[0-9]*.md"))] latest = max(existing) if int(tmp[0]) > latest[0]: return "major" elif int(tmp[1]) > latest[1]: return "minor" else: return "patch" def main(argv): """ Identify the release type and create a new target file with TOML header. Requires three arguments. """ source, target, tag = argv if "a" in tag: bump = "alpha" if "b" in tag: bump = "beta" else: bump = find_bump(target, tag) filename = "{}.md".format(tag) destination = copy(join(source, filename), target) build_hugo_md(destination, tag, bump) if __name__ == "__main__": if len(sys.argv) != 4: print("Usage:\n{} " "".format(sys.argv[0])) sys.exit(2) sys.exit(main(sys.argv[1:])) cobrapy-0.29.1/setup.cfg000066400000000000000000000047421467301562300151010ustar00rootroot00000000000000[bumpversion] current_version = 0.29.1 commit = True tag = True parse = (?P\d+) \.(?P\d+) \.(?P\d+) (?P[a]*)(?P\d*) serialize = {major}.{minor}.{patch}{release}{num} {major}.{minor}.{patch} tag_name = {new_version} [metadata] name = cobra url = https://opencobra.github.io/cobrapy download_url = https://pypi.org/project/cobra project_urls = Source Code = https://github.com/opencobra/cobrapy Documentation = https://cobrapy.readthedocs.io Bug Tracker = https://github.com/opencobra/cobrapy/issues author = The cobrapy core development team. author_email = cobra-pie@googlegroups.com maintainer = Moritz E. Beber maintainer_email = moritz.beber@gmail.com classifiers = Development Status :: 5 - Production/Stable Intended Audience :: Science/Research License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+) License :: OSI Approved :: GNU Lesser General Public License v2 or later (LGPLv2+) Natural Language :: English Operating System :: OS Independent Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.9 Programming Language :: Python :: 3.10 Programming Language :: Python :: 3.11 Programming Language :: Python :: Implementation :: CPython Topic :: Scientific/Engineering :: Bio-Informatics license = LGPL-2.0-or-later OR GPL-2.0-or-later description = COBRApy is a package for constraint-based modeling of metabolic networks. long_description = file: README.rst, INSTALL.rst long_description_content_type = text/x-rst keywords = metabolism biology constraint-based linear programming mixed-integer optimization flux-balance analysis reconstruction [options] zip_safe = True install_requires = appdirs ~=1.4 depinfo ~=2.2 diskcache ~=5.0 future httpx ~=0.24 importlib_resources numpy >=1.13 optlang ~=1.8 pandas >=1.0,<3.0 pydantic >=1.6 python-libsbml ~=5.19 rich >=8.0 ruamel.yaml ~=0.16 swiglpk tests_require = tox packages = find: package_dir = = src [options.packages.find] where = src [options.package_data] cobra = data/* io/*.json [options.extras_require] array = scipy development = black bumpversion isort tox [bdist_wheel] universal = 1 [bumpversion:part:release] optional_value = placeholder first_value = placeholder values = placeholder a [bumpversion:file:setup.py] search = version="{current_version}" replace = version="{new_version}" [bumpversion:file:src/cobra/__init__.py] search = __version__ = "{current_version}" replace = __version__ = "{new_version}" cobrapy-0.29.1/setup.py000066400000000000000000000007061467301562300147660ustar00rootroot00000000000000#!/usr/bin/env python """Set up the cobra package.""" from sys import version_info from warnings import warn from setuptools import setup if version_info[:2] < (3, 6): warn( "We only explicitly test Python 3.6 and later, however, earlier versions may " "be functional if you can manage the dependencies yourself." ) # All other arguments are defined in `setup.cfg`. if __name__ == "__main__": setup(version="0.29.1") cobrapy-0.29.1/src/000077500000000000000000000000001467301562300140405ustar00rootroot00000000000000cobrapy-0.29.1/src/cobra/000077500000000000000000000000001467301562300151265ustar00rootroot00000000000000cobrapy-0.29.1/src/cobra/__init__.py000066400000000000000000000006111467301562300172350ustar00rootroot00000000000000__author__ = "The cobrapy core development team." __version__ = "0.29.1" from cobra.core import ( Configuration, DictList, Gene, Metabolite, Model, Object, Reaction, Solution, Species, ) from cobra import flux_analysis from cobra import io from cobra import medium from cobra import sampling from cobra import summary from cobra.util import show_versions cobrapy-0.29.1/src/cobra/core/000077500000000000000000000000001467301562300160565ustar00rootroot00000000000000cobrapy-0.29.1/src/cobra/core/__init__.py000066400000000000000000000006411467301562300201700ustar00rootroot00000000000000from cobra.core.configuration import Configuration from cobra.core.dictlist import DictList from cobra.core.gene import Gene, GPR from cobra.core.metabolite import Metabolite from cobra.core.model import Model from cobra.core.object import Object from cobra.core.reaction import Reaction from cobra.core.group import Group from cobra.core.solution import Solution, get_solution from cobra.core.species import Species cobrapy-0.29.1/src/cobra/core/configuration.py000066400000000000000000000211341467301562300213000ustar00rootroot00000000000000"""Provide a global configuration object.""" import logging import pathlib import types from numbers import Number from os import cpu_count from textwrap import dedent from typing import Optional, Tuple, Union import appdirs from ..exceptions import SolverNotFound from ..util.solver import interface_to_str from ..util.solver import solvers as SOLVERS from .singleton import Singleton __all__ = ("Configuration",) logger = logging.getLogger(__name__) class Configuration(metaclass=Singleton): """ Define a global configuration object. The attributes of this singleton object are used as default values by cobra functions. Attributes ---------- solver : {"glpk", "cplex", "gurobi", "glpk_exact"} The default solver for new models. The solver choices are the ones provided by `optlang` and depend on solvers installed in your environment. tolerance : float, optional The default tolerance for the solver being used (default 1E-07). lower_bound : float, optional The standard lower bound for reversible reactions (default -1000). upper_bound : float, optional The standard upper bound for all reactions (default 1000). bounds : tuple of floats The default reaction bounds for newly created reactions. The bounds are in the form of lower_bound, upper_bound (default -1000.0, 1000.0). processes : int > 0 A default number of processes to use where multiprocessing is possible. The default number corresponds to the number of available cores (hyperthreads) minus one. cache_directory : pathlib.Path or str, optional A path where the model cache should reside if caching is desired. The default directory depends on the operating system. max_cache_size : int, optional The allowed maximum size of the model cache in bytes (default 1 GB). cache_expiration : int, optional The expiration time in seconds for the model cache if any (default None). """ def __init__(self, **kwargs) -> None: """Initialize the configuration with its default attribute values.""" super().__init__(**kwargs) self._solver = None self.tolerance = 1e-07 self.lower_bound = None self.upper_bound = None self.processes = None self._cache_directory = None # Set the cache size to a maximum of 100 MB. self.max_cache_size = 100 * (1024**2) self.cache_expiration = None self.bounds = -1000.0, 1000.0 self._set_default_solver() self._set_default_processes() self._set_default_cache_directory() def _set_default_solver(self) -> None: """Set the default solver from a preferred order.""" for name in ["gurobi", "cplex", "glpk"]: try: self.solver = name except SolverNotFound: continue else: break def _set_default_processes(self) -> None: """Set the default number of processes.""" self.processes = cpu_count() if self.processes is None: logger.warning("The number of cores could not be detected - assuming one.") self.processes = 1 if self.processes > 1: self.processes -= 1 def _set_default_cache_directory(self) -> None: """Set the platform-dependent default cache directory.""" self.cache_directory = pathlib.Path( appdirs.user_cache_dir(appname="cobrapy", appauthor="opencobra") ) @property def solver(self) -> types.ModuleType: """Return the optlang solver interface.""" return self._solver @solver.setter def solver(self, value) -> None: """Set the optlang solver interface.""" not_valid_interface = SolverNotFound( f"'{value}' is not a valid solver interface. " f" Please pick one from {', '.join(SOLVERS)}." ) if isinstance(value, str): if value not in SOLVERS: raise not_valid_interface interface = SOLVERS[value] elif isinstance(value, types.ModuleType) and hasattr(value, "Model"): interface = value else: raise not_valid_interface self._solver = interface @property def bounds(self) -> Tuple[Optional[Number], Optional[Number]]: """Return the lower, upper reaction bound pair. Returns ------- tuple of number and number or None and None The lower and upper bounds for new reactions. """ return self.lower_bound, self.upper_bound @bounds.setter def bounds(self, bounds: Tuple[Optional[Number], Optional[Number]]) -> None: """Set the lower, upper reaction bound pair. Parameters ---------- bounds : tuple of number and number or None and None The lower and upper bounds for new reactions. """ if None not in bounds: assert bounds[0] <= bounds[1] self.lower_bound = bounds[0] self.upper_bound = bounds[1] @property def cache_directory(self) -> pathlib.Path: """Return the model cache directory.""" return self._cache_directory @cache_directory.setter def cache_directory(self, path: Union[pathlib.Path, str]) -> None: """ Set the model cache directory. The directory path is created if it doesn't exist yet. Parameters ---------- path : pathlib.Path or str The path to the cache directory. """ self._cache_directory = pathlib.Path(path) if not self._cache_directory.is_dir(): logger.debug(f"Creating cache directory '{str(self._cache_directory)}'.") self._cache_directory.mkdir(parents=True) def __repr__(self) -> str: """Return a string representation of the current configuration values.""" return dedent( f""" solver: {interface_to_str(self.solver)} tolerance: {self.tolerance} lower_bound: {self.lower_bound} upper_bound: {self.upper_bound} processes: {self.processes} cache_directory: {self.cache_directory} max_cache_size: {self.max_cache_size} cache_expiration: {self.cache_expiration} """ ) def _repr_html_(self) -> str: """ Return a rich HTML representation of the current configuration values. Notes ----- This special method is used automatically in Jupyter notebooks to display a result from a cell. """ return dedent( f"""
Attribute Description Value 
solver
 Mathematical optimization solver {interface_to_str(self.solver)} 
tolerance
 General solver tolerance (feasibility, integrality, etc.) {self.tolerance} 
lower_bound
 Default reaction lower bound {self.lower_bound} 
upper_bound
 Default reaction upper bound {self.upper_bound} 
processes
 Number of parallel processes {self.processes} 
cache_directory
 Path for the model cache {self.cache_directory} 
max_cache_size
 Maximum cache size in bytes {self.max_cache_size} 
cache_expiration
 Model cache expiration time in seconds (if any) {self.cache_expiration}
""" ) cobrapy-0.29.1/src/cobra/core/dictlist.py000066400000000000000000000422161467301562300202540ustar00rootroot00000000000000"""Define the DictList class.""" import re from itertools import islice from typing import ( Any, Callable, Iterable, Iterator, List, Optional, Pattern, Tuple, Type, Union, ) from .object import Object class DictList(list): """ Define a combined dict and list. This object behaves like a list, but has the O(1) speed benefits of a dict when looking up elements by their id. """ def __init__(self, *args): """Instantiate a combined dict and list. Parameters ---------- args : iterable iterable as single argument to create new DictList from """ if len(args) > 2: raise TypeError(f"takes at most 1 argument ({len(args):d} given)") super(DictList, self).__init__(self) self._dict = {} if len(args) == 1: other = args[0] if isinstance(other, DictList): list.extend(self, other) self._dict = other._dict.copy() else: self.extend(other) # noinspection PyShadowingBuiltins def has_id(self, id: Union[Object, str]) -> bool: """Check if id is in DictList.""" return id in self._dict # noinspection PyShadowingBuiltins def _check(self, id: Union[Object, str]) -> None: """Make sure duplicate id's are not added. This function is called before adding in elements. """ if id in self._dict: raise ValueError(f"id {str(id)} is already present in list") def _generate_index(self) -> None: """Rebuild the _dict index.""" self._dict = {v.id: k for k, v in enumerate(self)} # noinspection PyShadowingBuiltins def get_by_id(self, id: Union[Object, str]) -> Object: """Return the element with a matching id.""" return list.__getitem__(self, self._dict[id]) def list_attr(self, attribute: str) -> list: """Return a list of the given attribute for every object.""" return [getattr(i, attribute) for i in self] def get_by_any(self, iterable: List[Union[str, Object, int]]) -> list: """Get a list of members using several different ways of indexing. Parameters ---------- iterable : list (if not, turned into single element list) list where each element is either int (referring to an index in in this DictList), string (a id of a member in this DictList) or member of this DictList for pass-through Returns ------- list a list of members """ def get_item(item: Any) -> Any: if isinstance(item, int): return self[item] elif isinstance(item, str): return self.get_by_id(item) elif item in self: return item else: raise TypeError(f"item in iterable cannot be '{type(item)}'") if not isinstance(iterable, list): iterable = [iterable] return [get_item(item) for item in iterable] def query( self, search_function: Union[str, Pattern, Callable], attribute: Union[str, None] = None, ) -> "DictList": """Query the list. Parameters ---------- search_function : a string, regular expression or function Used to find the matching elements in the list. - a regular expression (possibly compiled), in which case the given attribute of the object should match the regular expression. - a function which takes one argument and returns True for desired values attribute : string or None the name attribute of the object to passed as argument to the `search_function`. If this is None, the object itself is used. Returns ------- DictList a new list of objects which match the query Examples -------- >>> from cobra.io import load_model >>> model = load_model('textbook') >>> model.reactions.query(lambda x: x.boundary) >>> import re >>> regex = re.compile('^g', flags=re.IGNORECASE) >>> model.metabolites.query(regex, attribute='name') """ def select_attribute(x: Optional[Any]) -> Any: if attribute is None: return x else: return getattr(x, attribute) try: # if the search_function is a regular expression regex_searcher = re.compile(search_function) if attribute is not None: matches = ( i for i in self if regex_searcher.findall(select_attribute(i)) != [] ) else: # Don't regex on objects matches = (i for i in self if regex_searcher.findall(i.id) != []) except TypeError: matches = (i for i in self if search_function(select_attribute(i))) results = self.__class__() results._extend_nocheck(matches) return results def _replace_on_id(self, new_object: Object) -> None: """Replace an object by another with the same id.""" the_id = new_object.id the_index = self._dict[the_id] list.__setitem__(self, the_index, new_object) # overriding default list functions with new ones def append(self, entity: Object) -> None: """Append object to end.""" the_id = entity.id self._check(the_id) self._dict[the_id] = len(self) list.append(self, entity) def union(self, iterable: Iterable[Object]) -> None: """Add elements with id's not already in the model.""" _dict = self._dict append = self.append for i in iterable: if i.id not in _dict: append(i) def extend(self, iterable: Iterable[Object]) -> None: """Extend list by appending elements from the iterable. Sometimes during initialization from an older pickle, _dict will not have initialized yet, because the initialization class was left unspecified. This is an issue because unpickling calls DictList.extend, which requires the presence of _dict. Therefore, the issue is caught and addressed here. Parameters ---------- iterable : Iterable """ if getattr(self, "_dict", None) is None: self._dict = {} _dict = self._dict current_length = len(self) list.extend(self, iterable) for i, obj in enumerate(islice(self, current_length, None), current_length): the_id = obj.id if the_id not in _dict: _dict[the_id] = i else: # undo the extend and raise an error self = self[:current_length] self._check(the_id) # if the above succeeded, then the id must be present # twice in the list being added raise ValueError( f"id '{str(the_id)}' at index {i :d} is non-unique. " f"Is it present twice?" ) def _extend_nocheck(self, iterable: Iterable[Object]) -> None: """Extend without checking for uniqueness. This function should only be used internally by DictList when it can guarantee elements are already unique (as in when coming from self or other DictList). It will be faster because it skips these checks. Parameters ---------- iterable : Iterable """ current_length = len(self) list.extend(self, iterable) _dict = self._dict if not current_length: self._generate_index() return for i, obj in enumerate(islice(self, current_length, None), current_length): _dict[obj.id] = i def __sub__(self, other: Iterable[Object]) -> "DictList": """Remove a value or values, and returns the new DictList. x.__sub__(y) <==> x - y Parameters ---------- other : iterable other must contain only unique id's present in the list Returns ------- total: DictList new DictList with item(s) removed """ total = DictList() total.extend(self) for item in other: total.remove(item) return total def __isub__(self, other: Iterable[Object]) -> "DictList": """Remove a value or values in place. x.__sub__(y) <==> x -= y Parameters ---------- other : iterable other must contain only unique id's present in the list """ for item in other: self.remove(item) return self def __add__(self, other: Iterable[Object]) -> "DictList": """Add item while returning a new DictList. x.__add__(y) <==> x + y Parameters ---------- other : iterable other must contain only unique id's which do not intersect with self """ total = DictList() total.extend(self) total.extend(other) return total def __iadd__(self, other: Iterable[Object]) -> "DictList": """Add item while returning the same DictList. x.__iadd__(y) <==> x += y Parameters ---------- other : iterable other must contain only unique id's whcih do not intersect with self """ self.extend(other) return self def __reduce__(self) -> Tuple[Type["DictList"], Tuple, dict, Iterator]: """Return a reduced version of DictList. This reduced version details the class, an empty Tuple, a dictionary of the state and an iterator to go over the DictList. """ return self.__class__, (), self.__getstate__(), self.__iter__() def __getstate__(self) -> dict: """Get internal state. This is only provided for backwards compatibility so older versions of cobrapy can load pickles generated with cobrapy. In reality, the "_dict" state is ignored when loading a pickle """ return {"_dict": self._dict} def __setstate__(self, state: dict) -> None: """Pretend to set internal state. Actually recalculates. Ignore the passed in state and recalculate it. This is only for compatibility with older pickles which did not correctly specify the initialization class """ self._generate_index() # noinspection PyShadowingBuiltins def index(self, id: Union[str, Object], *args) -> int: """Determine the position in the list. Parameters ---------- id: A string or a :class:`~cobra.core.Object.Object` """ # because values are unique, start and stop are not relevant if isinstance(id, str): try: return self._dict[id] except KeyError: raise ValueError(f"{id} not found") try: i = self._dict[id.id] if self[i] is not id: raise ValueError( f"Another object with the identical id ({id.id}) found" ) return i except KeyError: raise ValueError(f"{str(id)} not found") def __contains__(self, entity: Union[str, Object]) -> bool: """Ask if the DictList contain an entity. DictList.__contains__(entity) <==> entity in DictList Parameters ---------- entity: str or :class:`~cobra.core.Object.Object` """ if hasattr(entity, "id"): the_id = entity.id # allow to check with the object itself in addition to the id else: the_id = entity return the_id in self._dict def __copy__(self) -> "DictList": """Copy the DictList into a new one.""" the_copy = DictList() list.extend(the_copy, self) the_copy._dict = self._dict.copy() return the_copy def insert(self, index: int, entity: Object) -> None: """Insert entity before index.""" self._check(entity.id) list.insert(self, index, entity) # all subsequent entries now have been shifted up by 1 _dict = self._dict for i, j in _dict.items(): if j >= index: _dict[i] = j + 1 _dict[entity.id] = index def pop(self, *args) -> Object: """Remove and return item at index (default last).""" value = list.pop(self, *args) index = self._dict.pop(value.id) # If the pop occurred from a location other than the end of the list, # we will need to subtract 1 from every entry afterwards if len(args) == 0 or args == [-1]: # removing from the end of the list return value _dict = self._dict for i, j in _dict.items(): if j > index: _dict[i] = j - 1 return value def add(self, x: Object) -> None: """Opposite of `remove`. Mirrors set.add.""" self.extend([x]) def remove(self, x: Union[str, Object]) -> None: """.. warning :: Internal use only. Each item is unique in the list which allows this It is much faster to do a dict lookup than n string comparisons """ self.pop(self.index(x)) # these functions are slower because they rebuild the _dict every time def reverse(self) -> None: """Reverse *IN PLACE*.""" list.reverse(self) self._generate_index() def sort( self, cmp: Callable = None, key: Callable = None, reverse: bool = False ) -> None: """Stable sort *IN PLACE*. cmp(x, y) -> -1, 0, 1 """ if key is None: def key(i): return i.id list.sort(self, key=key, reverse=reverse) self._generate_index() def __getitem__( self, i: Union[int, slice, Iterable, Object, "DictList"] ) -> Union["DictList", Object]: """Get item from DictList.""" if isinstance(i, int): return list.__getitem__(self, i) elif isinstance(i, slice): selection = self.__class__() selection._extend_nocheck(list.__getitem__(self, i)) return selection elif hasattr(i, "__len__"): if len(i) == len(self) and isinstance(i[0], (bool, bool)): selection = self.__class__() result = (o for j, o in enumerate(self) if i[j]) selection._extend_nocheck(result) return selection else: return self.__class__(list.__getitem__(self, i)) else: return list.__getitem__(self, i) def __setitem__(self, i: Union[slice, int], y: Union[list, Object]) -> None: """Set an item via index or slice. Parameters ---------- i : slice, int i can be slice or int. If i is a slice, y needs to be a list y: list, Object Object to set as """ if isinstance(i, slice): # In this case, y needs to be a list. We will ensure all # the id's are unique for obj in y: # need to be setting to a list self._check(obj.id) # Insert a temporary placeholder so we catch the presence # of a duplicate in the items being added self._dict[obj.id] = None list.__setitem__(self, i, y) self._generate_index() return # in case a rename has occurred if self._dict.get(self[i].id) == i: self._dict.pop(self[i].id) the_id = y.id self._check(the_id) list.__setitem__(self, i, y) self._dict[the_id] = i def __delitem__(self, index: int) -> None: """Remove item from DictList.""" removed = self[index] list.__delitem__(self, index) if isinstance(removed, list): self._generate_index() return _dict = self._dict _dict.pop(removed.id) for i, j in _dict.items(): if j > index: _dict[i] = j - 1 def __getslice__(self, i: int, j: int) -> "DictList": """Get a slice from it to j of DictList.""" return self.__getitem__(slice(i, j)) def __setslice__(self, i: int, j: int, y: Union[list, Object]) -> None: """Set slice, where y is an iterable.""" self.__setitem__(slice(i, j), y) def __delslice__(self, i: int, j: int) -> None: """Remove slice.""" self.__delitem__(slice(i, j)) def __getattr__(self, attr: Any) -> Any: """Get an attribute by id.""" try: return DictList.get_by_id(self, attr) except KeyError: raise AttributeError(f"DictList has no attribute or entry {attr}") def __dir__(self) -> list: """Directory of the DictList. Override this to allow tab complete of items by their id. Returns ------- attributes: list A list of attributes/entities. """ attributes = dir(self.__class__) attributes.append("_dict") attributes.extend(self._dict.keys()) return attributes cobrapy-0.29.1/src/cobra/core/formula.py000066400000000000000000000131621467301562300201000ustar00rootroot00000000000000"""Provide a class representing a chemical formula.""" import re from typing import Optional, Union from warnings import warn from .object import Object # Numbers are not required because of the |(?=[A-Z])? block. See the # discussion in https://github.com/opencobra/cobrapy/issues/128 for # more details. element_re = re.compile("([A-Z][a-z]?)([0-9.]+[0-9.]?|(?=[A-Z])?)") class Formula(Object): """Describe a chemical formula. Parameters --------- formula : string A legal formula string contains only letters and numbers. """ def __init__(self, formula: Optional[str] = None, **kwargs) -> None: """Initialize a formula. Parameters ---------- formula: str, optional An string that will be parsed as a formula. """ super().__init__(id=formula, **kwargs) self.formula = formula self.elements = {} if self.formula is not None: self.parse_composition() def __add__(self, other_formula: Union["Formula", str]) -> "Formula": """Combine two molecular formulas. Parameters ---------- other_formula : Formula, str string for a chemical formula Returns ------- Formula: Formula The combined formula """ return Formula(self.formula + other_formula.formula) def parse_composition(self) -> None: """Break the chemical formula down by element.""" tmp_formula = self.formula # commonly occuring characters in incorrectly constructed formulas if "*" in tmp_formula: warn(f"invalid character '*' found in formula '{self.formula}'") tmp_formula = self.formula.replace("*", "") if "(" in tmp_formula or ")" in tmp_formula: warn(f"parenthesis found in formula '{self.formula}'") return composition = {} parsed = element_re.findall(tmp_formula) for element, count in parsed: if count == "": count = 1 else: try: count = float(count) int_count = int(count) if count == int_count: count = int_count else: warn(f"{count} is not an integer (in formula {self.formula})") except ValueError: warn(f"failed to parse {count} (in formula {self.formula})") self.elements = {} return if element in composition: composition[element] += count else: composition[element] = count self.elements = composition @property def weight(self) -> float: """Calculate the mol mass of the compound. Returns ------- float the mol mass """ try: return sum( [ count * elements_and_molecular_weights[element] for element, count in self.elements.items() ] ) except KeyError as e: warn(f"The element {e} does not appear in the periodic table") elements_and_molecular_weights = { "H": 1.007940, "He": 4.002602, "Li": 6.941000, "Be": 9.012182, "B": 10.811000, "C": 12.010700, "N": 14.006700, "O": 15.999400, "F": 18.998403, "Ne": 20.179700, "Na": 22.989770, "Mg": 24.305000, "Al": 26.981538, "Si": 28.085500, "P": 30.973761, "S": 32.065000, "Cl": 35.453000, "Ar": 39.948000, "K": 39.098300, "Ca": 40.078000, "Sc": 44.955910, "Ti": 47.867000, "V": 50.941500, "Cr": 51.996100, "Mn": 54.938049, "Fe": 55.845000, "Co": 58.933200, "Ni": 58.693400, "Cu": 63.546000, "Zn": 65.409000, "Ga": 69.723000, "Ge": 72.640000, "As": 74.921600, "Se": 78.960000, "Br": 79.904000, "Kr": 83.798000, "Rb": 85.467800, "Sr": 87.620000, "Y": 88.905850, "Zr": 91.224000, "Nb": 92.906380, "Mo": 95.940000, "Tc": 98.000000, "Ru": 101.070000, "Rh": 102.905500, "Pd": 106.420000, "Ag": 107.868200, "Cd": 112.411000, "In": 114.818000, "Sn": 118.710000, "Sb": 121.760000, "Te": 127.600000, "I": 126.904470, "Xe": 131.293000, "Cs": 132.905450, "Ba": 137.327000, "La": 138.905500, "Ce": 140.116000, "Pr": 140.907650, "Nd": 144.240000, "Pm": 145.000000, "Sm": 150.360000, "Eu": 151.964000, "Gd": 157.250000, "Tb": 158.925340, "Dy": 162.500000, "Ho": 164.930320, "Er": 167.259000, "Tm": 168.934210, "Yb": 173.040000, "Lu": 174.967000, "Hf": 178.490000, "Ta": 180.947900, "W": 183.840000, "Re": 186.207000, "Os": 190.230000, "Ir": 192.217000, "Pt": 195.078000, "Au": 196.966550, "Hg": 200.590000, "Tl": 204.383300, "Pb": 207.200000, "Bi": 208.980380, "Po": 209.000000, "At": 210.000000, "Rn": 222.000000, "Fr": 223.000000, "Ra": 226.000000, "Ac": 227.000000, "Th": 232.038100, "Pa": 231.035880, "U": 238.028910, "Np": 237.000000, "Pu": 244.000000, "Am": 243.000000, "Cm": 247.000000, "Bk": 247.000000, "Cf": 251.000000, "Es": 252.000000, "Fm": 257.000000, "Md": 258.000000, "No": 259.000000, "Lr": 262.000000, "Rf": 261.000000, "Db": 262.000000, "Sg": 266.000000, "Bh": 264.000000, "Hs": 277.000000, "Mt": 268.000000, "Ds": 281.000000, "Rg": 272.000000, "Cn": 285.000000, "Uuq": 289.000000, "Uuh": 292.000000, } cobrapy-0.29.1/src/cobra/core/gene.py000066400000000000000000000613431467301562300173550ustar00rootroot00000000000000"""Provide functions for dealing with genes and gene product rules (GPR).""" import logging import re from ast import ( AST, And, BitAnd, BitOr, BoolOp, Expression, Module, Name, NodeTransformer, NodeVisitor, Or, ) from ast import parse as ast_parse from copy import deepcopy from keyword import kwlist from typing import FrozenSet, Iterable, Optional, Set, Tuple, Union from warnings import warn import sympy.logic.boolalg as spl from sympy import Symbol from ..util import resettable from ..util.util import format_long_string from .dictlist import DictList from .species import Species # TODO - When https://github.com/symengine/symengine.py/issues/334 is resolved, # change sympy.Symbol (above in imports) to optlang.symbolics.Symbol logger = logging.getLogger(__name__) keywords = list(kwlist) keywords.remove("and") keywords.remove("or") keywords.extend(("True", "False")) keyword_re = re.compile(rf"(?=\b({'|'.join(keywords)})\b)") number_start_re = re.compile(r"(?=\b[0-9])") replacements = ( (".", "__COBRA_DOT__"), ("'", "__COBRA_SQUOTE__"), ('"', "__COBRA_DQUOTE__"), (":", "__COBRA_COLON__"), ("/", "__COBRA_FSLASH__"), ("\\", "__COBRA_BSLASH"), ("-", "__COBRA_DASH__"), ("=", "__COBRA_EQ__"), ) class GPRWalker(NodeVisitor): """Identifies genes in an AST/GPR tree. Walks over the tree, and identifies the id of each Name node """ def __init__(self, **kwargs) -> None: """Initialize a new object. Other Parameters ---------------- **kwargs: Further keyword arguments are passed on to the parent class. """ super().__init__(**kwargs) self.gene_set = set() def visit_Name(self, node: Name) -> None: """Visit a Gene node and add the id to the gene_set. Parameters ---------- node: ast.Name The node to visit """ self.gene_set.add(node.id) def visit_BoolOp(self, node: BoolOp) -> None: """Visit a BoolOp node (AND/OR) and visit the children to add them to gene_set. Parameters ---------- node: ast.BoolOp The node to visit """ self.generic_visit(node) for val in node.values: self.visit(val) class GPRCleaner(NodeTransformer): """Parses compiled ast of a gene_reaction_rule and identifies genes. Parts of the tree are rewritten to allow periods in gene ID's and bitwise boolean operations """ def __init__(self, **kwargs) -> None: """Initialize a new object. Other Parameters ---------------- **kwargs: Further keyword arguments are passed on to the parent class. """ super().__init__(**kwargs) self.gene_set = set() def visit_Name(self, node: Name) -> Name: """Visit a Gene node and add the id to the gene_set. The gene id will be cleaned used __cobra_escape__ and replacements dictionary (see above). Parameters ---------- node: ast.Name The node to visit Returns ------- node: ast.Name The transformed node (with the id changed). """ if node.id.startswith("__cobra_escape__"): node.id = node.id[16:] for char, escaped in replacements: if escaped in node.id: node.id = node.id.replace(escaped, char) self.gene_set.add(node.id) return node def visit_BinOp(self, node: BoolOp) -> None: """Visit a BoolOp node (AND/OR) and visit the children (genes) to process them. Parameters ---------- node: ast.BoolOp The node to visit. Nodes other than And() and Or() will cause an error. Returns ------- node: ast.BoolOp The node with the children transformed. """ self.generic_visit(node) if isinstance(node.op, BitAnd): return BoolOp(And(), (node.left, node.right)) elif isinstance(node.op, BitOr): return BoolOp(Or(), (node.left, node.right)) else: raise TypeError(f"unsupported operation '{node.op.__class__.__name__}'") def parse_gpr(str_expr: str) -> Tuple: """Parse GPR into AST. Parameters ---------- str_expr : string string with the gene reaction rule to parse Returns ------- tuple elements ast_tree and gene_ids as a set .. deprecated :: Use GPR(string_gpr=str_expr) in the future. Because of the GPR() class, this function will be removed. """ warn( "parse_gpr() will be removed soon." "Use GPR(string_gpr=str_expr) in the future", DeprecationWarning, ) gpr_tree = GPR.from_string(str_expr) return gpr_tree, gpr_tree.genes class Gene(Species): """A Gene in a cobra model. Parameters ---------- id : string The identifier to associate the gene with name: string A longer human readable name for the gene functional: bool Indicates whether the gene is functional. If it is not functional then it cannot be used in an enzyme complex nor can its products be used. """ # noinspection PyShadowingBuiltins def __init__(self, id: str = None, name: str = "", functional: bool = True) -> None: """Initialize a gene. Parameters ---------- id: str A string that will identify the gene. name: str A (longer) string that will identify the gene. Can have more special characters. functional: bool A flag whether or not the gene is functional """ super().__init__(id=id, name=name) self._functional = functional @property def functional(self) -> bool: """Flag indicating if the gene is functional. Changing the flag is reverted upon exit if executed within the model as context. """ return self._functional @functional.setter @resettable def functional(self, value: bool) -> None: if not isinstance(value, bool): raise ValueError("expected boolean") self._functional = value def knock_out(self) -> None: """Knockout gene by marking it as non-functional. Knockout gene by marking it as non-functional and setting all associated reactions bounds to zero. The change is reverted upon exit if executed within the model as context. """ self.functional = False for reaction in self.reactions: if not reaction.functional: reaction.bounds = (0, 0) def _repr_html_(self): return f"""
Gene identifier{self.id}
Name{self.name}
Memory address {id(self):#x}
Functional{self.functional}
In {len(self.reactions)} reaction(s) {format_long_string(", ".join(r.id for r in self.reactions), 200)}
""" class GPR(Module): """A Gene Reaction rule in a cobra model, using AST as base class. Parameters ---------- gpr_from : Expression or Module or AST A GPR in AST format """ def __init__(self, gpr_from: Union[Expression, Module, AST] = None, **kwargs): """Initialize a gene. Parameters ---------- gpr_from: Expression, Module, AST An AST expression that will be parsed to GPR. **kwargs: Further keyword arguments are passed on to the parent class. """ super().__init__(**kwargs) self._genes = set() self.body: Optional[list] = None if gpr_from: if isinstance(gpr_from, str): self.from_string(gpr_from) raise TypeError( f"GPR accepts AST, not string. " f'Next time, use GPR().from_string("{gpr_from}")' ) elif isinstance(gpr_from, (Expression, Module)): cleaner = GPRCleaner() cleaner.visit(gpr_from) self._genes = deepcopy(cleaner.gene_set) self.body = deepcopy(gpr_from.body) self.eval() else: raise TypeError("GPR requires AST Expression or Module") @classmethod def from_string(cls, string_gpr: str) -> "GPR": """Construct a GPR from a string. Parameters ---------- string_gpr: str a string that describes the gene rules, in a format like A & B Returns ------- GPR: returns a new GPR while setting self.body as Parsed AST tree that has the gene rules This function also sets self._genes with the gene ids in the AST """ if not isinstance(string_gpr, str): raise TypeError( f"{cls.__name__}.from_string " f"requires a str argument, not {type(string_gpr)}." ) gpr = cls() uppercase_AND = re.compile(r"\bAND\b") uppercase_OR = re.compile(r"\bOR\b") str_expr = string_gpr.strip() if len(str_expr) == 0: gpr.body = None return gpr for char, escaped in replacements: if char in str_expr: str_expr = str_expr.replace(char, escaped) escaped_str = keyword_re.sub("__cobra_escape__", str_expr) escaped_str = number_start_re.sub("__cobra_escape__", escaped_str) # Some mat models have () in gr_rules which leads to a complicated error later escaped_str = escaped_str.replace("()", "") try: tree = ast_parse(escaped_str, "", "eval") except (SyntaxError, TypeError) as e: if "AND" in escaped_str or "OR" in escaped_str: # noinspection PyTypeChecker logger.warning( f"Uppercase AND/OR found in rule '{string_gpr}'.", ) logger.warning(e.msg) warn( "Uppercase AND/OR found in rule '{}'.".format(string_gpr), SyntaxWarning, ) escaped_str = uppercase_AND.sub("and", escaped_str) escaped_str = uppercase_OR.sub("or", escaped_str) try: tree = ast_parse(escaped_str, "", "eval") except SyntaxError: # noinspection PyTypeChecker logger.warning( f"Malformed gene_reaction_rule '{escaped_str}' for {string_gpr}", exc_info=1, ) logger.warning("GPR will be empty") warn( "Malformed gene_reaction_rule '{}'".format(escaped_str), SyntaxWarning, ) return gpr gpr = cls(tree) gpr.update_genes() return gpr @property def genes(self) -> FrozenSet: """To check the genes. This property updates the genes before returning them, in case the GPR was changed and the genes weren't. Returns ------- genes: frozenset All the genes in a frozen set. Do not try to change them with this property. """ self.update_genes() return frozenset(self._genes) def update_genes(self) -> None: """Update genes, used after changes in GPR. Walks along the AST tree of the GPR class, and modifies self._genes """ if self.body: walker = GPRWalker() walker.visit(self) self._genes = deepcopy(walker.gene_set) def _eval_gpr( self, expr: Union[Expression, list, BoolOp, Name], knockouts: Union[DictList, set], ) -> bool: """Evaluate compiled ast of gene_reaction_rule with knockouts. Parameters ---------- expr : Expression or GPR or list or BoolOp or Name The ast of the gene reaction rule knockouts : DictList, set Set of genes that are knocked out Returns ------- bool True if the gene reaction rule is true with the given knockouts otherwise false """ # just always call the recursions as self._eval_gpr(a, b) if isinstance(expr, (Expression, GPR)): if not expr.body: return True return self._eval_gpr(expr.body, knockouts) elif isinstance(expr, Name): return expr.id not in knockouts elif isinstance(expr, BoolOp): op = expr.op if isinstance(op, Or): # noinspection PyTypeChecker return any(self._eval_gpr(i, knockouts) for i in expr.values) elif isinstance(op, And): # noinspection PyTypeChecker return all(self._eval_gpr(i, knockouts) for i in expr.values) else: raise TypeError(f"Unsupported operation: {op.__class__.__name__}") elif expr is None: return True else: raise TypeError(f"Unsupported operation: {repr(expr)}") def eval(self, knockouts: Union[DictList, Set, str, Iterable] = None) -> bool: """Evaluate compiled ast of gene_reaction_rule with knockouts. This function calls _eval_gpr, but allows more flexibility in input, including name, and list. Parameters ---------- knockouts Which gene or genes to knoc out Returns ------- bool True if the gene reaction rule is true with the given knockouts otherwise false """ if knockouts is None: knockouts = set() if knockouts is str: knockouts = list(knockouts) if self.body: return self._eval_gpr(self.body, knockouts=knockouts) else: return True def _ast2str( self, expr: Union["GPR", Expression, BoolOp, Name, list], level: int = 0, names: dict = None, ) -> str: """Convert compiled ast to gene_reaction_rule str. Parameters ---------- expr : AST or GPR or list or Name or BoolOp string for a gene reaction rule, e.g "a and b" level : int internal use only names : dict Dict where each element id a gene identifier and the value is the gene name. Use this to get a rule str which uses names instead. This should be done for display purposes only. All gene_reaction_rule strings which are computed with should use the id. Returns ------ string The gene reaction rule """ if isinstance(expr, (Expression, GPR)): return self._ast2str(expr.body, 0, names) if expr.body else "" elif isinstance(expr, Name): return names.get(expr.id, expr.id) if names else expr.id elif isinstance(expr, BoolOp): op = expr.op if isinstance(op, Or): # noinspection PyTypeChecker str_exp = " or ".join( self._ast2str(i, level + 1, names) for i in expr.values ) elif isinstance(op, And): # noinspection PyTypeChecker str_exp = " and ".join( self._ast2str(i, level + 1, names) for i in expr.values ) else: # noinspection PyTypeChecker raise TypeError(f"Unsupported operation: {op.__class__.__name}") return f"({str_exp})" if level else str_exp elif expr is None or (isinstance(expr, list) and len(expr) == 0): return "" else: raise TypeError(f"Unsupported operation: {repr(expr)}") def to_string(self, names: dict = None) -> str: """Convert compiled ast to gene_reaction_rule str. Parameters ---------- self : GPR compiled ast Module describing GPR names: dict dictionary of gene ids to gene names. If this is empty, returns gene ids Returns ------ string The gene reaction rule Notes ----- Calls _aststr() """ return self._ast2str(self, names=names) def copy(self): """Copy a GPR.""" return deepcopy(self) def __copy__(self) -> "GPR": """Ensure a correct shallow copy.""" return self.copy() def __repr__(self) -> str: """Return the GPR with module, class, and code to recreate it.""" return ( f"{self.__class__.__module__}.{self.__class__.__qualname__}" f"({self.to_string()!r})" ) def __str__(self) -> str: """Convert compiled ast to gene_reaction_rule str. Parameters ---------- self : GPR compiled ast Module describing GPR Returns ------ string The gene reaction rule """ return self.to_string(names={}) def _repr_html_(self) -> str: return f"""

GPR

{format_long_string(self.to_string(), 100)}

""" def as_symbolic( self, names: dict = None, ) -> Union[spl.Or, spl.And, Symbol]: """Convert compiled ast to sympy expression. Parameters ---------- self : GPR compiled ast Module describing GPR names: dict dictionary of gene ids to gene names. If this is empty, returns sympy expression using gene ids Returns ------ Symbol or BooleanFunction SYMPY expression (Symbol or And or Or). Symbol("") if the GPR is empty Notes ----- Calls _symbolic_gpr() """ # noinspection PyTypeChecker if names: GPRGene_dict = {gid: Symbol(names[gid]) for gid in self.genes} else: GPRGene_dict = None return self._symbolic_gpr(self, GPRGene_dict=GPRGene_dict) def _symbolic_gpr( self, expr: Union["GPR", Expression, BoolOp, Name, list] = None, GPRGene_dict: dict = None, ) -> Union[spl.Or, spl.And, Symbol]: """Parse gpr into SYMPY using ast similar to _ast2str(). Parameters ---------- expr : AST or GPR or list or Name or BoolOp compiled GPR GPRGene_dict: dict dictionary from gene id to GPRGeneSymbol Returns ------- Symbol or BooleanFunction SYMPY expression (Symbol or And or Or). Symbol("") if the GPR is empty """ if GPRGene_dict is None: GPRGene_dict = {gid: Symbol(name=gid) for gid in expr.genes} if isinstance(expr, (Expression, GPR)): return ( self._symbolic_gpr(expr.body, GPRGene_dict) if expr.body else Symbol("") ) else: if isinstance(expr, Name): return GPRGene_dict.get(expr.id) elif isinstance(expr, BoolOp): op = expr.op if isinstance(op, Or): # noinspection PyTypeChecker sym_exp = spl.Or( *[self._symbolic_gpr(i, GPRGene_dict) for i in expr.values] ) elif isinstance(op, And): # noinspection PyTypeChecker sym_exp = spl.And( *[self._symbolic_gpr(i, GPRGene_dict) for i in expr.values] ) else: raise TypeError("Unsupported operation " + op.__class__.__name) return sym_exp elif not expr: return Symbol("") else: raise TypeError("Unsupported Expression " + repr(expr)) @classmethod def from_symbolic(cls, sympy_gpr: Union[spl.BooleanFunction, Symbol]) -> "GPR": """Construct a GPR from a sympy expression. Parameters ---------- sympy_gpr: sympy a sympy that describes the gene rules, being a Symbol for single genes or a BooleanFunction for AND/OR relationships Returns ------- GPR: returns a new GPR while setting self.body as Parsed AST tree that has the gene rules This function also sets self._genes with the gene ids in the AST """ def _sympy_to_ast( sympy_expr: Union[spl.BooleanFunction, Symbol] ) -> Union[BoolOp, Name]: if sympy_expr.func is spl.Or: return BoolOp( op=Or(), values=[_sympy_to_ast(i) for i in sympy_expr.args] ) elif sympy_expr.func is spl.And: return BoolOp( op=And(), values=[_sympy_to_ast(i) for i in sympy_expr.args] ) elif not sympy_expr.args: return Name(id=sympy_expr.name) else: raise TypeError(f"Unsupported operation: {sympy_expr.func}") if not isinstance(sympy_gpr, (spl.BooleanFunction, Symbol)): raise TypeError( f"{cls.__name__}.from_symbolic " f"requires a sympy BooleanFunction or " f"Symbol argument, not {type(sympy_gpr)}." ) gpr = cls() if sympy_gpr == Symbol(""): gpr.body = None return gpr try: tree = Expression(_sympy_to_ast(sympy_gpr)) except SyntaxError as e: logger.warning( f"Problem with sympy expression '{sympy_gpr}' for {repr(gpr)}", ) logger.warning("GPR will be empty") logger.warning(e.msg) return gpr gpr = cls(tree) gpr.update_genes() return gpr def __eq__(self, other) -> bool: """Check equality of GPR via symbolic equality.""" if not self.body and not other.body: return True elif not self.body or not other.body: return False else: self_symb = self.as_symbolic() other_symb = other.as_symbolic() if isinstance(self_symb, Symbol) and isinstance(other_symb, Symbol): return self_symb == other_symb if isinstance(self_symb, Symbol) or isinstance(other_symb, Symbol): return False return self_symb.equals(other_symb) def eval_gpr(expr: Union[Expression, GPR], knockouts: Union[DictList, set]) -> bool: """Evaluate compiled ast of gene_reaction_rule with knockouts. .. deprecated :: Use GPR().eval() in the future. Because of the GPR() class, this function will be removed. Parameters ---------- expr : Expression or GPR The ast of the gene reaction rule knockouts : DictList, set Set of genes that are knocked out Returns ------- bool True if the gene reaction rule is true with the given knockouts otherwise false """ warn( "eval_gpr() will be removed soon." "Use GPR().eval(knockouts) in the future", DeprecationWarning, ) if isinstance(expr, GPR): return expr.eval(knockouts=knockouts) else: return GPR(expr).eval(knockouts=knockouts) # functions for gene reaction rules def ast2str(expr: Union[Expression, GPR], level: int = 0, names: dict = None) -> str: """Convert compiled ast to gene_reaction_rule str. Parameters ---------- expr : AST or GPR AST or GPR level : int internal use only. Ignored because of GPR() class, kept only for interface consistency with code still using ast2str. names : dict Dict where each element id a gene identifier and the value is the gene name. Use this to get a rule str which uses names instead. This should be done for display purposes only. All gene_reaction_rule strings which are computed with should use the id. Returns ------ string The gene reaction rule .. deprecated :: Use GPR.to_string(names=) in the future. Because of the GPR() class, this function will be removed. """ warn( "ast2satr() will be removed soon. Use gpr.to_string(names=names) in the future", DeprecationWarning, ) if isinstance(expr, GPR): return expr.to_string(names=names) else: return GPR(expr).to_string(names=names) cobrapy-0.29.1/src/cobra/core/group.py000066400000000000000000000113041467301562300175630ustar00rootroot00000000000000"""Define the group class.""" from typing import Iterable, Optional, Set from warnings import warn from .object import Object class Group(Object): """ Manage groups via this implementation of the SBML group specification. `Group` is a class for holding information regarding a pathways, subsystems, or other custom groupings of objects within a cobra.Model object. Parameters ---------- id : str The identifier to associate with this group name : str, optional A human readable name for the group members : iterable, optional A DictList containing references to cobra.Model-associated objects that belong to the group. kind : {"collection", "classification", "partonomy"}, optional The kind of group, as specified for the Groups feature in the SBML level 3 package specification. Can be any of "classification", "partonomy", or "collection". The default is "collection". Please consult the SBML level 3 package specification to ensure you are using the proper value for kind. In short, members of a "classification" group should have an "is-a" relationship to the group (e.g. member is-a polar compound, or member is-a transporter). Members of a "partonomy" group should have a "part-of" relationship (e.g. member is part-of glycolysis). Members of a "collection" group do not have an implied relationship between the members, so use this value for kind when in doubt (e.g. member is a gap-filled reaction, or member is involved in a disease phenotype). """ KIND_TYPES = ("collection", "classification", "partonomy") def __init__( self, id: str, name: str = "", members: Optional[Iterable] = None, kind: Optional[str] = None, ): """Initialize the group object. id : str The identifier to associate with this group name : str, optional A human readable name for the group members : iterable, optional A DictList containing references to cobra.Model-associated objects that belong to the group. kind : {"collection", "classification", "partonomy"}, optional The kind of group, as specified for the Groups feature in the SBML level 3 package specification. """ Object.__init__(self, id, name) self._members = set() if members is None else set(members) self._kind = None self.kind = "collection" if kind is None else kind # self.model is None or refers to the cobra.Model that # contains self self._model = None def __len__(self) -> int: """Get length of group. Returns ------- int An int with the length of the group. """ return len(self._members) # read-only @property def members(self) -> Set: """Get members of the group. Returns ------- Set A Set containing the members of the group. """ return self._members @property def kind(self) -> str: """Return the group kind. Returns ------- str The group kind. Should be one of the three types allowed in SBML. """ return self._kind @kind.setter def kind(self, kind: str) -> None: """Set the group kind. Parameters ---------- kind: str Must be one of the allowed kind types "collection", "classification", "partonomy". If kind is not one of these types, a ValueError Exception is raised. """ kind = kind.lower() if kind in self.KIND_TYPES: self._kind = kind else: raise ValueError(f"Kind can only by one of: {', '.join(self.KIND_TYPES)}.") def add_members(self, new_members: list) -> None: """Add objects to the group. Parameters ---------- new_members : list A list of cobrapy objects to add to the group. """ if isinstance(new_members, str) or hasattr(new_members, "id"): warn("need to pass in a list") new_members = [new_members] self._members.update(new_members) def remove_members(self, to_remove: list) -> None: """Remove objects from the group. Parameters ---------- to_remove : list A list of cobra objects to remove from the group """ if isinstance(to_remove, str) or hasattr(to_remove, "id"): warn("need to pass in a list") to_remove = [to_remove] self._members.difference_update(to_remove) cobrapy-0.29.1/src/cobra/core/metabolite.py000066400000000000000000000263361467301562300205670ustar00rootroot00000000000000"""Define the Metabolite class.""" import re from typing import TYPE_CHECKING, Dict, Optional, Union from warnings import warn from ..exceptions import OptimizationError from ..util.solver import check_solver_status from ..util.util import format_long_string from .formula import elements_and_molecular_weights from .species import Species if TYPE_CHECKING: from optlang.interface import Container from pandas import DataFrame from cobra.core import Solution from cobra.summary.metabolite_summary import MetaboliteSummary # Numbers are not required because of the |(?=[A-Z])? block. See the # discussion in https://github.com/opencobra/cobrapy/issues/128 for # more details. element_re = re.compile("([A-Z][a-z]?)([0-9.]+[0-9.]?|(?=[A-Z])?)") class Metabolite(Species): """Class for information about metabolite in cobra.Reaction. Metabolite is a class for holding information regarding a metabolite in a cobra.Reaction object. Parameters ---------- id : str the identifier to associate with the metabolite formula : str Chemical formula (e.g. H2O) name : str A human readable name. charge : float The charge number of the metabolite compartment: str or None Compartment of the metabolite. """ # noinspection PyShadowingBuiltins def __init__( self, id: Optional[str] = None, formula: Optional[str] = None, name: Optional[str] = "", charge: Optional[float] = None, compartment: Optional[str] = None, ) -> None: """Initialize Metaboblite cobra Species. Parameters ---------- id : str the identifier to associate with the metabolite formula : str Chemical formula (e.g. H2O) name : str A human readable name. charge : float The charge number of the metabolite compartment: str or None Compartment of the metabolite. """ super().__init__(id=id, name=name) self.formula = formula # because in a Model a metabolite may participate in multiple Reactions self.compartment = compartment self.charge = charge self._bound = 0.0 def _set_id_with_model(self, value: str) -> None: """Set id with value. Parameters ---------- value: str """ if value in self.model.metabolites: raise ValueError( f"The model already contains a metabolite with the id:" f" {value}" ) self.model.constraints[self.id].name = value self._id = value self.model.metabolites._generate_index() @property def constraint(self) -> "Container": """Get the constraints associated with this metabolite from the solver. Returns ------- optlang..Containter the optlang constraint for this metabolite """ if self.model is not None: return self.model.constraints[self.id] @property def elements(self) -> Optional[Dict[str, Union[int, float]]]: """Get dicitonary of elements and counts. Dictionary of elements as keys and their count in the metabolite as integer. When set, the `formula` property is updated accordingly. Returns ------- composition: None or Dict A dictionary of elements and counts, where count is int unless it is needed to be a float. Returns None in case of error. """ tmp_formula = self.formula if tmp_formula is None: return {} # necessary for some old pickles which use the deprecated # Formula class tmp_formula = str(self.formula) # commonly occurring characters in incorrectly constructed formulas if "*" in tmp_formula: warn(f"invalid character '*' found in formula '{self.formula}'") tmp_formula = tmp_formula.replace("*", "") if "(" in tmp_formula or ")" in tmp_formula: warn(f"invalid formula (has parenthesis) in '{self.formula}'") return None composition = {} parsed = element_re.findall(tmp_formula) for element, count in parsed: if count == "": count = 1 else: try: count = float(count) int_count = int(count) if count == int_count: count = int_count else: warn(f"{count} is not an integer (in formula {self.formula})") except ValueError: warn(f"failed to parse {count} (in formula {self.formula})") return None if element in composition: composition[element] += count else: composition[element] = count return composition @elements.setter def elements(self, elements_dict: Dict[str, Union[int, float]]) -> None: """Update formula based on elements dictionary. Parameters ---------- elements_dict: dict A dicitonary of elements as keys, count as items. """ def stringify(element, number): return element if number == 1 else element + str(number) self.formula = "".join( stringify(e, n) for e, n in sorted(elements_dict.items()) ) @property def formula_weight(self) -> Union[int, float]: """Calculate the formula weight. Returns ------ float, int Weight of formula, based on the weight and count of elements. Can be int if the formula weight is a whole number, but unlikely. """ try: return sum( [ count * elements_and_molecular_weights[element] for element, count in self.elements.items() ] ) except KeyError as e: warn(f"The element {e} does not appear in the periodic table") @property def y(self) -> float: """Return the shadow price for the metabolite in the most recent solution. Shadow prices are computed from the dual values of the bounds in the solution. .. deprecated :: Use metabolite.shadow_price instead. Returns ------- float Float representing the shadow price. """ warn("Please use metabolite.shadow_price instead.", DeprecationWarning) return self.shadow_price @property def shadow_price(self) -> float: """Return the shadow price for the metabolite in the most recent solution. Shadow price is the dual value of the corresponding constraint in the model. Returns ------- shadow_price: float Warnings -------- * Accessing shadow prices through a `Solution` object is the safer, preferred, and only guaranteed to be correct way. You can see how to do so easily in the examples. * Shadow price is retrieved from the currently defined `self._model.solver`. The solver status is checked but there are no guarantees that the current solver state is the one you are looking for. * If you modify the underlying model after an optimization, you will retrieve the old optimization values. Raises ------ RuntimeError If the underlying model was never optimized beforehand or the metabolite is not part of a model. OptimizationError If the solver status is anything other than 'optimal'. Examples -------- >>> from cobra.io import load_model >>> model = load_model("textbook") >>> solution = model.optimize() >>> model.metabolites.glc__D_e.shadow_price -0.09166474637510488 >>> solution.shadow_prices.glc__D_e -0.091664746375104883 """ try: check_solver_status(self._model.solver.status) return self._model.constraints[self.id].dual except AttributeError: raise RuntimeError(f"metabolite '{self.id}' is not part of a model") # Due to below all-catch, which sucks, need to reraise these. except (RuntimeError, OptimizationError) as err: raise err.with_traceback() # Would love to catch CplexSolverError and GurobiError here. except Exception as err: raise OptimizationError( f"Likely no solution exists. Original solver message: {str(err)}." ) from err def remove_from_model(self, destructive: bool = False) -> None: """Remove the association from self.model. The change is reverted upon exit when using the model as a context. Parameters ---------- destructive : bool, default False If False then the metabolite is removed from all associated reactions. If True then all associated reactions are removed from the Model. """ self._model.remove_metabolites(self, destructive) def summary( self, solution: Optional["Solution"] = None, fva: Optional[Union[float, "DataFrame"]] = None, ) -> "MetaboliteSummary": """Create a summary of the producing and consuming fluxes. Parameters ---------- solution : cobra.Solution, optional A previous model solution to use for generating the summary. If ``None``, the summary method will generate a parsimonious flux distribution (default None). fva : pandas.DataFrame or float, optional Whether or not to include flux variability analysis in the output. If given, `fva` should either be a previous FVA solution matching the model or a float between 0 and 1 representing the fraction of the optimum objective to be searched (default None). Returns ------- cobra.summary.MetaboliteSummary See Also -------- Reaction.summary Model.summary """ from cobra.summary import MetaboliteSummary return MetaboliteSummary( metabolite=self, model=self._model, solution=solution, fva=fva, ) def _repr_html_(self) -> str: """Return the metabolite as an HTML string.""" return f"""
Metabolite identifier{self.id}
Name{format_long_string(self.name)}
Memory address {id(self):#x}
Formula{self.formula}
Compartment{self.compartment}
In {len(self.reactions)} reaction(s) {format_long_string(", ".join(r.id for r in self.reactions), 200)}
""" cobrapy-0.29.1/src/cobra/core/model.py000066400000000000000000001545131467301562300175410ustar00rootroot00000000000000"""Define the Model class.""" import logging from copy import copy, deepcopy from functools import partial from types import ModuleType from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Tuple, Union from warnings import warn import optlang from optlang.symbolics import Basic, Zero from ..medium import find_boundary_types, find_external_compartment, sbo_terms from ..util.context import HistoryManager, get_context, resettable from ..util.solver import ( add_cons_vars_to_problem, assert_optimal, check_solver, interface_to_str, remove_cons_vars_from_problem, set_objective, ) from ..util.util import AutoVivification, format_long_string from .configuration import Configuration from .dictlist import DictList from .gene import Gene from .group import Group from .metabolite import Metabolite from .object import Object from .reaction import Reaction from .solution import get_solution if TYPE_CHECKING: import pandas as pd from optlang.container import Container from cobra import Solution from cobra.summary import ModelSummary from cobra.util.solver import CONS_VARS logger = logging.getLogger(__name__) configuration = Configuration() class Model(Object): """Class representation for a cobra model. Parameters ---------- id_or_model: str or Model, optional String to use as model id, or actual model to base new model one. If string, it is used as id. If model, a new model object is instantiated with the same properties as the original model (default None). name: str, optional Human readable string to be model description (default None). Attributes ---------- reactions : DictList A DictList where the key is the reaction identifier and the value a Reaction metabolites : DictList A DictList where the key is the metabolite identifier and the value a Metabolite genes : DictList A DictList where the key is the gene identifier and the value a Gene groups : DictList A DictList where the key is the group identifier and the value a Group """ def __init__( self, id_or_model: Union[str, "Model", None] = None, name: Optional[str] = None ) -> None: """Initialize the Model.""" if isinstance(id_or_model, Model): Object.__init__(self, name=name) self.__setstate__(id_or_model.__dict__) self._solver = id_or_model.solver else: Object.__init__(self, id_or_model, name=name) self.genes = DictList() self.reactions = DictList() # A list of cobra.Reactions self.metabolites = DictList() # A list of cobra.Metabolites self.groups = DictList() # A list of cobra.Groups # genes based on their ids {Gene.id: Gene} self._compartments = {} self._contexts = [] # from cameo ... # if not hasattr(self, '_solver'): # backwards compatibility # with older cobrapy pickles? interface = check_solver(configuration.solver) self._solver = interface.Model() self._solver.objective = interface.Objective(Zero) self._populate_solver(self.reactions, self.metabolites) self._tolerance = None self.tolerance = configuration.tolerance def __setstate__(self, state: Dict) -> None: """Make sure all cobra.Objects in the model point to the model. Parameters ---------- state: dict """ self.__dict__.update(state) for y in ["reactions", "genes", "metabolites"]: for x in getattr(self, y): x._model = self if not hasattr(self, "name"): self.name = None def __getstate__(self) -> Dict: """Get state for serialization. Ensures that the context stack is cleared prior to serialization, since partial functions cannot be pickled reliably. Returns ------- odict: Dict A dictionary of state, based on self.__dict__. """ odict = self.__dict__.copy() odict["_contexts"] = [] return odict @property def solver(self) -> "optlang.interface.Model": """Get the attached solver instance. The associated the solver object, which manages the interaction with the associated solver, e.g. glpk. This property is useful for accessing the optimization problem directly and to define additional non-metabolic constraints. Examples -------- >>> from cobra.io import load_model >>> model = load_model("textbook") >>> new = model.problem.Constraint(model.objective.expression, lb=0.99) >>> model.solver.add(new) """ return self._solver @solver.setter @resettable def solver(self, value: Union[str, ModuleType]) -> None: """Set the attached solver instance. The associated the solver object, which manages the interaction with the associated solver, e.g. glpk. This property is useful for accessing the optimization problem directly and to define additional non-metabolic constraints. Parameters ---------- value: ModuleType or str The solver to set, which will be checked by `check_solver()`. """ interface = check_solver(value) # Do nothing if the solver did not change if self.problem == interface: return self._solver = interface.Model.clone(self._solver) @property def tolerance(self) -> float: """Get the tolerance. Returns ------- float The tolerance of the mdoel. """ return self._tolerance @tolerance.setter def tolerance(self, value: float) -> None: """Set the tolerance. Parameters ---------- value: float Value to set tolerance. """ solver_tolerances = self.solver.configuration.tolerances try: solver_tolerances.feasibility = value except AttributeError: logger.info( f"The current solver interface {interface_to_str(self.problem)} " f"doesn't support setting the feasibility tolerance." ) try: solver_tolerances.optimality = value except AttributeError: logger.info( f"The current solver interface {interface_to_str(self.problem)} " f"doesn't support setting the optimality tolerance." ) try: solver_tolerances.integrality = value except AttributeError: logger.info( f"The current solver interface {interface_to_str(self.problem)} " f"doesn't support setting the integrality tolerance." ) self._tolerance = value @property def compartments(self) -> Dict: """Return all metabolites' compartments. Returns ------- dict A dictionary of metabolite compartments, where the keys are the short version (one letter version) of the compartmetns, and the values are the full names (if they exist). """ return { met.compartment: self._compartments.get(met.compartment, "") for met in self.metabolites if met.compartment is not None } @compartments.setter def compartments(self, value: Dict) -> None: """Get or set the dictionary of current compartment descriptions. Assigning a dictionary to this property updates the model's dictionary of compartment descriptions with the new values. Parameters ---------- value : dict Dictionary mapping compartments abbreviations to full names. Examples -------- >>> from cobra.io import load_model >>> model = load_model("textbook") >>> model.compartments = {'c': 'the cytosol'} >>> model.compartments {'c': 'the cytosol', 'e': 'extracellular'} """ self._compartments.update(value) @property def medium(self) -> Dict[str, float]: """Get the constraints on the model exchanges. `model.medium` returns a dictionary of the bounds for each of the boundary reactions, in the form of `{rxn_id: bound}`, where `bound` specifies the absolute value of the bound in direction of metabolite creation (i.e., lower_bound for `met <--`, upper_bound for `met -->`) Returns ------- Dict[str, float] A dictionary with rxn.id (str) as key, bound (float) as value. """ def is_active(reaction: Reaction) -> bool: """Determine if boundary reaction permits flux towards creating metabolites. Parameters ---------- reaction: cobra.Reaction Returns ------- bool True if reaction produces metaoblites and has upper_bound above 0 or if reaction consumes metabolites and has lower_bound below 0 (so could be reversed). """ return (bool(reaction.products) and (reaction.upper_bound > 0)) or ( bool(reaction.reactants) and (reaction.lower_bound < 0) ) def get_active_bound(reaction: Reaction) -> float: """For an active boundary reaction, return the relevant bound. Parameters ---------- reaction: cobra.Reaction Returns ------- float: upper or minus lower bound, depenending if the reaction produces or consumes metaoblties. """ if reaction.reactants: return -reaction.lower_bound elif reaction.products: return reaction.upper_bound return { rxn.id: get_active_bound(rxn) for rxn in self.exchanges if is_active(rxn) } @medium.setter def medium(self, medium: Dict[str, float]) -> None: """Set the constraints on the model exchanges. `model.medium` returns a dictionary of the bounds for each of the boundary reactions, in the form of `{rxn_id: rxn_bound}`, where `rxn_bound` specifies the absolute value of the bound in direction of metabolite creation (i.e., lower_bound for `met <--`, upper_bound for `met -->`) Parameters ---------- medium: dict The medium to initialize. medium should be a dictionary defining `{rxn_id: bound}` pairs. """ def set_active_bound(reaction: Reaction, bound: float) -> None: """Set active bound. Parameters ---------- reaction: cobra.Reaction Reaction to set bound: float Value to set bound to. The bound is reversed and set as lower bound if reaction has reactants (metabolites that are consumed). If reaction has reactants, it seems the upper bound won't be set. """ if reaction.reactants: reaction.lower_bound = -bound elif reaction.products: reaction.upper_bound = bound # Set the given media bounds media_rxns = [] exchange_rxns = frozenset(self.exchanges) for rxn_id, rxn_bound in medium.items(): rxn = self.reactions.get_by_id(rxn_id) if rxn not in exchange_rxns: logger.warning( f"{rxn.id} does not seem to be an an exchange reaction. " f"Applying bounds anyway." ) media_rxns.append(rxn) # noinspection PyTypeChecker set_active_bound(rxn, rxn_bound) frozen_media_rxns = frozenset(media_rxns) # Turn off reactions not present in media for rxn in exchange_rxns - frozen_media_rxns: is_export = rxn.reactants and not rxn.products set_active_bound( rxn, min(0.0, -rxn.lower_bound if is_export else rxn.upper_bound) ) def copy(self) -> "Model": """Provide a partial 'deepcopy' of the Model. All the Metabolite, Gene, and Reaction objects are created anew but in a faster fashion than deepcopy. Returns ------- cobra.Model: new model copy """ new = self.__class__() do_not_copy_by_ref = { "metabolites", "reactions", "genes", "notes", "annotation", "groups", } for attr in self.__dict__: if attr not in do_not_copy_by_ref: new.__dict__[attr] = self.__dict__[attr] new.notes = deepcopy(self.notes) new.annotation = deepcopy(self.annotation) new.metabolites = DictList() do_not_copy_by_ref = {"_reaction", "_model"} for metabolite in self.metabolites: new_met = metabolite.__class__() for attr, value in metabolite.__dict__.items(): if attr not in do_not_copy_by_ref: new_met.__dict__[attr] = copy(value) if attr == "formula" else value new_met._model = new new.metabolites.append(new_met) new.genes = DictList() for gene in self.genes: new_gene = gene.__class__(None) for attr, value in gene.__dict__.items(): if attr not in do_not_copy_by_ref: new_gene.__dict__[attr] = ( copy(value) if attr == "formula" else value ) new_gene._model = new new.genes.append(new_gene) new.reactions = DictList() do_not_copy_by_ref = {"_model", "_metabolites", "_genes"} for reaction in self.reactions: new_reaction = reaction.__class__() for attr, value in reaction.__dict__.items(): if attr not in do_not_copy_by_ref: new_reaction.__dict__[attr] = copy(value) new_reaction._model = new new.reactions.append(new_reaction) # update awareness for metabolite, stoic in reaction._metabolites.items(): new_met = new.metabolites.get_by_id(metabolite.id) new_reaction._metabolites[new_met] = stoic new_met._reaction.add(new_reaction) new_reaction.update_genes_from_gpr() new.groups = DictList() do_not_copy_by_ref = {"_model", "_members"} # Groups can be members of other groups. We initialize them first and # then update their members. for group in self.groups: new_group: Group = group.__class__(group.id) for attr, value in group.__dict__.items(): if attr not in do_not_copy_by_ref: new_group.__dict__[attr] = copy(value) new_group._model = new new.groups.append(new_group) for group in self.groups: new_group = new.groups.get_by_id(group.id) # update awareness, as in the reaction copies new_objects = [] for member in group.members: if isinstance(member, Metabolite): new_object = new.metabolites.get_by_id(member.id) elif isinstance(member, Reaction): new_object = new.reactions.get_by_id(member.id) elif isinstance(member, Gene): new_object = new.genes.get_by_id(member.id) elif isinstance(member, Group): new_object = new.groups.get_by_id(member.id) else: raise TypeError( f"The group member {member!r} is unexpectedly not a " f"metabolite, reaction, gene, nor another group." ) new_objects.append(new_object) new_group.add_members(new_objects) try: new._solver = deepcopy(self.solver) # Cplex has an issue with deep copies except Exception: # pragma: no cover new._solver = copy(self.solver) # pragma: no cover # it doesn't make sense to retain the context of a copied model so # assign a new empty context new._contexts = [] return new def add_metabolites(self, metabolite_list: Union[List, Metabolite]) -> None: """Add new metabolites to a model. Will add a list of metabolites to the model object and add new constraints accordingly. The change is reverted upon exit when using the model as a context. Parameters ---------- metabolite_list : list or Metabolite. A list of `cobra.core.Metabolite` objects. If it isn't an iterable container, the metabolite will be placed into a list. """ if not hasattr(metabolite_list, "__iter__"): metabolite_list = [metabolite_list] if len(metabolite_list) == 0: return None # First check whether the metabolites exist in the model metabolite_list = [x for x in metabolite_list if x.id not in self.metabolites] bad_ids = [ m for m in metabolite_list if not isinstance(m.id, str) or len(m.id) < 1 ] if len(bad_ids) != 0: raise ValueError(f"invalid identifiers in {repr(bad_ids)}") for x in metabolite_list: x._model = self self.metabolites += metabolite_list # from cameo ... to_add = [] for met in metabolite_list: if met.id not in self.constraints: constraint = self.problem.Constraint(Zero, name=met.id, lb=0, ub=0) to_add += [constraint] self.add_cons_vars(to_add) context = get_context(self) if context: context(partial(self.metabolites.__isub__, metabolite_list)) for x in metabolite_list: # Do we care? context(partial(setattr, x, "_model", None)) def remove_metabolites( self, metabolite_list: Union[List, Metabolite], destructive: bool = False ) -> None: """Remove a list of metabolites from the the object. The change is reverted upon exit when using the model as a context. Parameters ---------- metabolite_list : list or Metaoblite A list of `cobra.core.Metabolite` objects. If it isn't an iterable container, the metabolite will be placed into a list. destructive : bool, optional If False then the metabolite is removed from all associated reactions. If True then all associated reactions are removed from the Model (default False). """ if not hasattr(metabolite_list, "__iter__"): metabolite_list = [metabolite_list] # Make sure metabolites exist in model metabolite_list = [x for x in metabolite_list if x.id in self.metabolites] for x in metabolite_list: x._model = None # remove reference to the metabolite in all groups associated_groups = self.get_associated_groups(x) for group in associated_groups: group.remove_members(x) if not destructive: for the_reaction in list(x._reaction): # noqa W0212 the_coefficient = the_reaction._metabolites[x] # noqa W0212 the_reaction.subtract_metabolites({x: the_coefficient}) else: for x2 in list(x._reaction): # noqa W0212 x2.remove_from_model() self.metabolites -= metabolite_list to_remove = [self.solver.constraints[m.id] for m in metabolite_list] self.remove_cons_vars(to_remove) context = get_context(self) if context: context(partial(self.metabolites.__iadd__, metabolite_list)) for x in metabolite_list: context(partial(setattr, x, "_model", self)) def add_boundary( self, metabolite: Metabolite, type: str = "exchange", reaction_id: Optional[str] = None, lb: Optional[float] = None, ub: Optional[float] = None, sbo_term: Optional[str] = None, ) -> Reaction: """ Add a boundary reaction for a given metabolite. There are three different types of pre-defined boundary reactions: exchange, demand, and sink reactions. An exchange reaction is a reversible, unbalanced reaction that adds to or removes an extracellular metabolite from the extracellular compartment. A demand reaction is an irreversible reaction that consumes an intracellular metabolite. A sink is similar to an exchange but specifically for intracellular metabolites, i.e., a reversible reaction that adds or removes an intracellular metabolite. If you set the reaction `type` to something else, you must specify the desired identifier of the created reaction along with its upper and lower bound. The name will be given by the metabolite name and the given `type`. The change is reverted upon exit when using the model as a context. Parameters ---------- metabolite : cobra.Metabolite Any given metabolite. The compartment is not checked but you are encouraged to stick to the definition of exchanges and sinks. type : {"exchange", "demand", "sink"} Using one of the pre-defined reaction types is easiest. If you want to create your own kind of boundary reaction choose any other string, e.g., 'my-boundary' (default "exchange"). reaction_id : str, optional The ID of the resulting reaction. This takes precedence over the auto-generated identifiers but beware that it might make boundary reactions harder to identify afterwards when using `model.boundary` or specifically `model.exchanges` etc. (default None). lb : float, optional The lower bound of the resulting reaction (default None). ub : float, optional The upper bound of the resulting reaction (default None). sbo_term : str, optional A correct SBO term is set for the available types. If a custom type is chosen, a suitable SBO term should also be set (default None). Returns ------- cobra.Reaction The created boundary reaction. Examples -------- >>> from cobra.io load_model >>> model = load_model("textbook") >>> demand = model.add_boundary(model.metabolites.atp_c, type="demand") >>> demand.id 'DM_atp_c' >>> demand.name 'ATP demand' >>> demand.bounds (0, 1000.0) >>> demand.build_reaction_string() 'atp_c --> ' """ ub = configuration.upper_bound if ub is None else ub lb = configuration.lower_bound if lb is None else lb types = { "exchange": ("EX", lb, ub, sbo_terms["exchange"]), "demand": ("DM", 0, ub, sbo_terms["demand"]), "sink": ("SK", lb, ub, sbo_terms["sink"]), } if type == "exchange": external = find_external_compartment(self) if metabolite.compartment != external: raise ValueError( f"The metabolite is not an external metabolite (compartment is " f"`{metabolite.compartment}` but should be `{external}`). " f"Did you mean to add a demand or sink? If not, either change" f" its compartment or rename the model compartments to fix this." ) if type in types: prefix, lb, ub, default_term = types[type] if reaction_id is None: reaction_id = f"{prefix}_{metabolite.id}" if sbo_term is None: sbo_term = default_term if reaction_id is None: raise ValueError( "Custom types of boundary reactions require a custom " "identifier. Please set the `reaction_id`." ) if reaction_id in self.reactions: # It already exists so just retrieve it. logger.info(f"Boundary reaction '{reaction_id}' already exists.") return self.reactions.get_by_id(reaction_id) name = f"{metabolite.name} {type}" rxn = Reaction(id=reaction_id, name=name, lower_bound=lb, upper_bound=ub) rxn.add_metabolites({metabolite: -1}) if sbo_term: rxn.annotation["sbo"] = sbo_term self.add_reactions([rxn]) return rxn def add_reactions(self, reaction_list: Iterable[Reaction]) -> None: """Add reactions to the model. Reactions with identifiers identical to a reaction already in the model are ignored. The change is reverted upon exit when using the model as a context. Parameters ---------- reaction_list : list A list of `cobra.Reaction` objects """ def existing_filter(rxn: Reaction) -> bool: """Check if the reaction does not exists in the model. Parameters ---------- rxn: cobra.Reaction Returns ------- bool False if reaction exists, True if it doesn't. If the reaction exists, will log a warning. """ if rxn.id in self.reactions: logger.warning(f"Ignoring reaction '{rxn.id}' since it already exists.") return False return True # First check whether the reactions exist in the model. pruned = DictList(filter(existing_filter, reaction_list)) context = get_context(self) # Add reactions. Also take care of genes and metabolites in the loop. for reaction in pruned: reaction._model = self if context: context(partial(setattr, reaction, "_model", None)) # Build a `list()` because the dict will be modified in the loop. for metabolite in list(reaction.metabolites): # TODO: Maybe this can happen with # Reaction.add_metabolites(combine=False) # TODO: Should we add a copy of the metabolite instead? if metabolite not in self.metabolites: self.add_metabolites(metabolite) # A copy of the metabolite exists in the model, the reaction # needs to point to the metabolite in the model. else: # FIXME: Modifying 'private' attributes is horrible. stoichiometry = reaction._metabolites.pop(metabolite) model_metabolite = self.metabolites.get_by_id(metabolite.id) reaction._metabolites[model_metabolite] = stoichiometry model_metabolite._reaction.add(reaction) if context: context(partial(model_metabolite._reaction.remove, reaction)) reaction.update_genes_from_gpr() self.reactions += pruned if context: context(partial(self.reactions.__isub__, pruned)) # from cameo ... self._populate_solver(pruned) def remove_reactions( self, reactions: Union[str, Reaction, List[Union[str, Reaction]]], remove_orphans: bool = False, ) -> None: """Remove reactions from the model. The change is reverted upon exit when using the model as a context. Parameters ---------- reactions : list or reaction or str A list with reactions (`cobra.Reaction`), or their id's, to remove. Reaction will be placed in a list. Str will be placed in a list and used to find the reaction in the model. remove_orphans : bool, optional Remove orphaned genes and metabolites from the model as well (default False). """ if isinstance(reactions, str) or hasattr(reactions, "id"): warn("need to pass in a list") reactions = [reactions] context = get_context(self) for reaction in reactions: # Make sure the reaction is in the model try: reaction = self.reactions[self.reactions.index(reaction)] except ValueError: warn(f"{reaction} not in {self}") else: forward = reaction.forward_variable reverse = reaction.reverse_variable if context: obj_coef = reaction.objective_coefficient if obj_coef != 0: context( partial( self.solver.objective.set_linear_coefficients, {forward: obj_coef, reverse: -obj_coef}, ) ) context(partial(self._populate_solver, [reaction])) context(partial(setattr, reaction, "_model", self)) context(partial(self.reactions.add, reaction)) self.remove_cons_vars([forward, reverse]) self.reactions.remove(reaction) reaction._model = None for met in reaction._metabolites: if reaction in met._reaction: met._reaction.remove(reaction) if context: context(partial(met._reaction.add, reaction)) if remove_orphans and len(met._reaction) == 0: self.remove_metabolites(met) for gene in reaction._genes: if reaction in gene._reaction: gene._reaction.remove(reaction) if context: context(partial(gene._reaction.add, reaction)) if remove_orphans and len(gene._reaction) == 0: self.genes.remove(gene) if context: context(partial(self.genes.add, gene)) # remove reference to the reaction in all groups associated_groups = self.get_associated_groups(reaction) for group in associated_groups: group.remove_members(reaction) def add_groups(self, group_list: Union[str, Group, List[Group]]) -> None: """Add groups to the model. Groups with identifiers identical to a group already in the model are ignored. If any group contains members that are not in the model, these members are added to the model as well. Only metabolites, reactions, and genes can have groups. Parameters ---------- group_list : list or str or Group A list of `cobra.Group` objects to add to the model. Can also be a single group or a string representing group id. If the input is not a list, a warning is raised. """ def existing_filter(new_group: Group) -> bool: """Check if the group does not exist. Parameters ---------- new_group: cobra.Group Group to check. Returns ------- bool False if the group already exists, True if it doesn't. """ if new_group.id in self.groups: logger.warning( f"Ignoring group '{new_group.id}'" f" since it already exists." ) return False return True if isinstance(group_list, str) or hasattr(group_list, "id"): warn("need to pass in a list") group_list = [group_list] pruned = DictList(filter(existing_filter, group_list)) for group in pruned: group._model = self for member in group.members: # If the member is not associated with the model, add it if isinstance(member, Metabolite) and member not in self.metabolites: self.add_metabolites([member]) if isinstance(member, Reaction) and member not in self.reactions: self.add_reactions([member]) # TODO(midnighter): `add_genes` method does not exist. # if isinstance(member, Gene): # if member not in self.genes: # self.add_genes([member]) self.groups += [group] def remove_groups(self, group_list: Union[str, Group, List[Group]]) -> None: """Remove groups from the model. Members of each group are not removed from the model (i.e. metabolites, reactions, and genes in the group stay in the model after any groups containing them are removed). Parameters ---------- group_list : list or str or Group A list of `cobra.Group` objects to remove from the model. Can also be a single group or a string representing group id. If the input is not a list, a warning is raised. """ if isinstance(group_list, str) or hasattr(group_list, "id"): warn("need to pass in a list") group_list = [group_list] for group in group_list: # make sure the group is in the model if group.id not in self.groups: logger.warning(f"{group!r} not in {self!r}. Ignored.") else: self.groups.remove(group) group._model = None def get_associated_groups( self, element: Union[Reaction, Gene, Metabolite] ) -> List[Group]: """Get list of groups for element. Returns a list of groups that an element (reaction, metabolite, gene) is associated with. Parameters ---------- element: `cobra.Reaction`, `cobra.Metabolite`, or `cobra.Gene` Returns ------- list of `cobra.Group` All groups that the provided object is a member of """ # check whether the element is associated with the model return [g for g in self.groups if element in g.members] def add_cons_vars( self, what: Union[List["CONS_VARS"], Tuple["CONS_VARS"]], **kwargs ) -> None: """Add constraints and variables to the model's mathematical problem. Useful for variables and constraints that can not be expressed with reactions and simple lower and upper bounds. Additions are reversed upon exit if the model itself is used as context. Parameters ---------- what : list or tuple of optlang variables or constraints. The variables or constraints to add to the model. Must be of class `optlang.interface.Variable` or `optlang.interface.Constraint`. **kwargs : keyword arguments Passed to solver.add() """ add_cons_vars_to_problem(self, what, **kwargs) def remove_cons_vars( self, what: Union[List["CONS_VARS"], Tuple["CONS_VARS"]] ) -> None: """Remove variables and constraints from problem. Remove variables and constraints from the model's mathematical problem. Remove variables and constraints that were added directly to the model's underlying mathematical problem. Removals are reversed upon exit if the model itself is used as context. Parameters ---------- what : list or tuple of optlang variables or constraints. The variables or constraints to add to the model. Must be of class `optlang.interface.Variable` or `optlang.interface.Constraint`. """ remove_cons_vars_from_problem(self, what) @property def problem(self) -> "optlang.interface": """Get the interface to the model's underlying mathematical problem. Solutions to cobra models are obtained by formulating a mathematical problem and solving it. Cobrapy uses the optlang package to accomplish that and with this property you can get access to the problem interface directly. Returns ------- optlang.interface The problem interface that defines methods for interacting with the problem and associated solver directly. """ return self.solver.interface @property def variables(self) -> "Container": """Get the mathematical variables in the cobra model. In a cobra model, most variables are reactions. However, for specific use cases, it may also be useful to have other types of variables. This property defines all variables currently associated with the model's problem. Returns ------- optlang.container.Container A container with all associated variables. """ return self.solver.variables @property def constraints(self) -> "Container": """Get the constraints in the cobra model. In a cobra model, most constraints are metabolites and their stoichiometries. However, for specific use cases, it may also be useful to have other types of constraints. This property defines all constraints currently associated with the model's problem. Returns ------- optlang.container.Container A container with all associated constraints. """ return self.solver.constraints @property def boundary(self) -> List[Reaction]: """Boundary reactions in the model. Reactions that either have no substrate or product. Returns ------- list A list of reactions that either have no substrate or product and only one metabolite overall. """ return [rxn for rxn in self.reactions if rxn.boundary] @property def exchanges(self) -> List[Reaction]: """Exchange reactions in model. Reactions that exchange mass with the exterior. Uses annotations and heuristics to exclude non-exchanges such as sink reactions. Returns ------- list A list of reactions that satisfy the conditions for exchange reactions. See Also -------- cobra.medium.find_boundary_types """ return find_boundary_types(self, "exchange", None) @property def demands(self) -> List[Reaction]: """Demand reactions in model. Irreversible reactions that accumulate or consume a metabolite in the inside of the model. Returns ------- list A list of reactions that are demand reactions (reactions that accumulate/consume a metabolite irreversibly). See Also -------- cobra.medium.find_boundary_types """ return find_boundary_types(self, "demand", None) @property def sinks(self) -> List[Reaction]: """Sink reactions in model. Reversible reactions that accumulate or consume a metabolite in the inside of the model. Returns ------- list A list of reactions that are demand reactions (reactions that accumulate/consume a metabolite reversibly). See Also -------- cobra.medium.find_boundary_types """ return find_boundary_types(self, "sink", None) def _populate_solver( self, reaction_list: List[Reaction], metabolite_list: Optional[List[Metabolite]] = None, ) -> None: """Populate attached solver with constraints and variables. Populate attached solver with constraints and variables that model the provided reactions. Parameters ---------- reaction_list: list A list of cobra.Reaction to add to the solver. This list will be constrained. metabolite_list: list, optional A list of cobra.Metabolite to add to the solver. This list will be constrained (default None). """ constraint_terms = AutoVivification() to_add = [] if metabolite_list is not None: for met in metabolite_list: to_add += [self.problem.Constraint(Zero, name=met.id, lb=0, ub=0)] self.add_cons_vars(to_add) for reaction in reaction_list: if reaction.id not in self.variables: forward_variable = self.problem.Variable(reaction.id) reverse_variable = self.problem.Variable(reaction.reverse_id) self.add_cons_vars([forward_variable, reverse_variable]) else: reaction = self.reactions.get_by_id(reaction.id) forward_variable = reaction.forward_variable reverse_variable = reaction.reverse_variable for metabolite, coeff in reaction.metabolites.items(): if metabolite.id in self.constraints: constraint = self.constraints[metabolite.id] else: constraint = self.problem.Constraint( Zero, name=metabolite.id, lb=0, ub=0 ) self.add_cons_vars(constraint, sloppy=True) constraint_terms[constraint][forward_variable] = coeff constraint_terms[constraint][reverse_variable] = -coeff self.solver.update() for reaction in reaction_list: reaction = self.reactions.get_by_id(reaction.id) reaction.update_variable_bounds() for constraint, terms in constraint_terms.items(): constraint.set_linear_coefficients(terms) def slim_optimize( self, error_value: Optional[float] = float("nan"), message: Optional[str] = None ) -> float: """Optimize model without creating a solution object. Creating a full solution object implies fetching shadow prices and flux values for all reactions and metabolites from the solver object. This necessarily takes some time and in cases where only one or two values are of interest, it is recommended to instead use this function which does not create a solution object returning only the value of the objective. Note however that the `optimize()` function uses efficient means to fetch values so if you need fluxes/shadow prices for more than say 4 reactions/metabolites, then the total speed increase of `slim_optimize` versus `optimize` is expected to be small or even negative depending on how you fetch the values after optimization. Parameters ---------- error_value : float, None The value to return if optimization failed due to e.g. infeasibility. If None, raise `OptimizationError` if the optimization fails (default float("nan")). message : str, optional Error message to use if the model optimization did not succeed (default None). Returns ------- float The objective value. Returns the error value if optimization failed and error_value was not None. Raises ------ OptimizationError If error_value was set as None and the optimization fails. """ self.solver.optimize() if self.solver.status == optlang.interface.OPTIMAL: return self.solver.objective.value elif error_value is not None: return error_value else: assert_optimal(self, message) def optimize( self, objective_sense: Optional[str] = None, raise_error: bool = False ) -> "Solution": """ Optimize the model using flux balance analysis. Parameters ---------- objective_sense : {None, 'maximize' 'minimize'}, optional Whether fluxes should be maximized or minimized. In case of None, the previous direction is used (default None). raise_error : bool If true, raise an OptimizationError if solver status is not optimal (default False). Returns ------- Solution Notes ----- Only the most commonly used parameters are presented here. Additional parameters for cobra.solvers may be available and specified with the appropriate keyword argument. """ original_direction = self.objective.direction self.objective.direction = {"maximize": "max", "minimize": "min"}.get( objective_sense, original_direction ) self.slim_optimize() solution = get_solution(self, raise_error=raise_error) self.objective.direction = original_direction return solution def repair( self, rebuild_index: bool = True, rebuild_relationships: bool = True ) -> None: """Update all indexes and pointers in a model. Parameters ---------- rebuild_index : bool rebuild the indices kept in reactions, metabolites and genes rebuild_relationships : bool reset all associations between genes, metabolites, model and then re-add them. """ if rebuild_index: # DictList indexes self.reactions._generate_index() self.metabolites._generate_index() self.genes._generate_index() self.groups._generate_index() if rebuild_relationships: for met in self.metabolites: met._reaction.clear() for gene in self.genes: gene._reaction.clear() for rxn in self.reactions: rxn.update_genes_from_gpr() for met in rxn._metabolites: met._reaction.add(rxn) # point _model to self for dict_list in (self.reactions, self.genes, self.metabolites, self.groups): for entity in dict_list: entity._model = self @property def objective(self) -> Union[optlang.Objective]: """Get the solver objective. With optlang, the objective is not limited to a simple linear summation of individual reaction fluxes, making the return value ambiguous. Henceforth, use `cobra.util.solver.linear_reaction_coefficients` to get a dictionary of reactions with their linear coefficients (empty if there are none). """ return self.solver.objective @objective.setter def objective( self, value: Union[ Dict[Reaction, "Container"], str, int, Reaction, "optlang.interface.Objective", Basic, ], ) -> None: """Set the solver objective. Parameters ---------- value: dict or str or int or Reaction or optlang.interface.Container, Reaction or Basic The set value can be dictionary (reactions as keys, linear coefficients as values), string (reaction identifier), int (reaction index), Reaction or problem.Objective or sympy expression directly interpreted as objectives. When using in a context, this attribute can be set temporarily. """ if isinstance(value, Basic): value = self.problem.Objective(value, sloppy=False) if not isinstance(value, (dict, optlang.interface.Objective)): try: reactions = self.reactions.get_by_any(value) except KeyError: raise ValueError("invalid objective") value = {rxn: 1 for rxn in reactions} # TODO - check that it is reset with context. set_objective(self, value, additive=False) @property def objective_direction(self) -> str: """Get the objective direction. Returns ------- str Objective direction as string. Should be "max" or "min". """ return self.solver.objective.direction @objective_direction.setter @resettable def objective_direction(self, value: str) -> None: """Set the objective direction. When used in a context, this attribute is set temporarily. Parameters ---------- value: {"max", "min"} String of "max" or "min" for direction. Raises ------ ValueError If given direction isn't max or min. """ value = value.lower() if value.startswith("max"): self.solver.objective.direction = "max" elif value.startswith("min"): self.solver.objective.direction = "min" else: raise ValueError(f"Unknown objective direction '{value}'.") def summary( self, solution: Optional["Solution"] = None, fva: Union["pd.DataFrame", float, None] = None, ) -> "ModelSummary": """ Create a summary of the exchange fluxes of the model. Parameters ---------- solution : cobra.Solution, optional A previous model solution to use for generating the summary. If ``None``, the summary method will generate a parsimonious flux distribution (default None). fva : pd.DataFrame or float, optional Whether or not to include flux variability analysis in the output. If given, `fva` should either be a previous FVA solution matching the model or a float between 0 and 1 representing the fraction of the optimum objective to be searched (default None). Returns ------- cobra.ModelSummary See Also -------- Reaction.summary Metabolite.summary """ from cobra.summary import ModelSummary return ModelSummary(model=self, solution=solution, fva=fva) def __enter__(self) -> "Model": """Record future changes to the model. Record all future changes to the model, undoing them when a call to __exit__ is received. Creates a new context and adds it to the stack. Returns ------- cobra.Model Returns the model with context added. """ try: self._contexts.append(HistoryManager()) except AttributeError: self._contexts = [HistoryManager()] return self def __exit__(self, type, value, traceback) -> None: """Pop the top context manager and trigger the undo functions.""" context = self._contexts.pop() context.reset() def merge( self, right: "Model", prefix_existing: Optional[str] = None, inplace: bool = True, objective: str = "left", ) -> "Model": """Merge two models to create a model with the reactions from both models. Custom constraints and variables from right models are also copied to left model, however note that, constraints and variables are assumed to be the same if they have the same name. Parameters ---------- right : cobra.Model The model to add reactions from prefix_existing : string or optional Prefix the reaction identifier in the right that already exist in the left model with this string (default None). inplace : bool Add reactions from right directly to left model object. Otherwise, create a new model leaving the left model untouched. When done within the model as context, changes to the models are reverted upon exit (default True). objective : {"left", "right", "sum"} One of "left", "right" or "sum" for setting the objective of the resulting model to that of the corresponding model or the sum of both (default "left"). Returns ------- cobra.Model The merged model. """ if inplace: new_model = self else: new_model = self.copy() new_model.id = f"{self.id}_{right.id}" new_reactions = deepcopy(right.reactions) if prefix_existing is not None: existing = new_reactions.query(lambda rxn: rxn.id in self.reactions) for reaction in existing: reaction.id = f"{prefix_existing}{reaction.id}" new_model.add_reactions(new_reactions) interface = new_model.problem new_vars = [ interface.Variable.clone(v) for v in right.variables if v.name not in new_model.variables ] new_model.add_cons_vars(new_vars) new_cons = [ interface.Constraint.clone(c, model=new_model.solver) for c in right.constraints if c.name not in new_model.constraints ] new_model.add_cons_vars(new_cons, sloppy=True) new_model.objective = dict( left=self.objective, right=right.objective, sum=self.objective.expression + right.objective.expression, )[objective] return new_model def _repr_html_(self) -> str: """Get HTML represenation of the model. Returns ------- str Model representation as HTML string. """ return f"""
Name {self.id}
Memory address {f"{id(self):x}"}
Number of metabolites {len(self.metabolites)}
Number of reactions {len(self.reactions)}
Number of genes {len(self.genes)}
Number of groups {len(self.groups)}
Objective expression {format_long_string(str(self.objective.expression), 100)}
Compartments {", ".join(v if v else k for k, v in self.compartments.items())}
""" cobrapy-0.29.1/src/cobra/core/object.py000066400000000000000000000064161467301562300177050ustar00rootroot00000000000000"""Define base Object class in Cobra.""" from typing import Optional class Object: """Defines common behavior of object in cobra.core.""" def __init__(self, id: Optional[str] = None, name: str = "") -> None: """Initialize a simple object with an identifier. Parameters ---------- id: string, optional the identifier to associate with the object name: string, optional The name to associate with the object. Default "". Objects will have notes and _annotation as dicitionaries, initialized as empty dictionaries. """ self._id = id self.name = name self.notes = {} self._annotation = {} @property def id(self) -> str: """Get the Object id. Returns ------- id: str """ return getattr(self, "_id", None) @id.setter def id(self, value) -> None: """Set the id to value. Parameters ---------- value: str The string to set the id to. Raises ------ TypeError if value is not a string. """ if value == self.id: pass elif not isinstance(value, str): raise TypeError("ID must be a string") elif getattr(self, "_model", None) is not None: self._set_id_with_model(value) else: self._id = value def _set_id_with_model(self, value) -> None: """Set id with model. This appears to be a stub so it can be modified in dependant classes. Parameters ---------- value: str The string to set the id to. """ self._id = value @property def annotation(self) -> dict: """Get annotation dictionary. Returns ------- _annotation: dict Returns _annotation as a dictionary. """ return self._annotation @annotation.setter def annotation(self, annotation): """Set annotation. Parameters ---------- annotation: dict Annotation dictionary to set _annotation to. Will raise error if not dict. Raises ------ TypeError if annotation not a dict. """ if not isinstance(annotation, dict): raise TypeError("Annotation must be a dict") else: self._annotation = annotation def __getstate__(self) -> dict: """Get state of annotation. To prevent excessive replication during deepcopy, ignores _model in state. Returns ------- state: dict Dictionary of state, excluding _model. """ state = self.__dict__.copy() if "_model" in state: state["_model"] = None return state def __repr__(self) -> str: """Return string representation of Object, with class. Returns ------- str Composed of class.name, id and hexadecimal of id. """ return f"<{self.__class__.__name__} {self.id} at {id(self):#x}>" def __str__(self) -> str: """Return string representation of object. Returns ------- str Object.id as string. """ return str(self.id) cobrapy-0.29.1/src/cobra/core/reaction.py000066400000000000000000001560041467301562300202420ustar00rootroot00000000000000"""Define the Reaction class.""" import hashlib import re from collections import defaultdict from copy import copy, deepcopy from functools import partial from math import isinf from operator import attrgetter from typing import ( TYPE_CHECKING, AnyStr, Dict, FrozenSet, Iterable, Iterator, List, Optional, Sequence, Set, Tuple, Union, ) from warnings import warn if TYPE_CHECKING: from optlang.interface import Variable from cobra import Model, Solution from cobra.summary import ReactionSummary import pandas as pd from ..exceptions import OptimizationError from ..manipulation import remove_genes from ..util.context import get_context, resettable from ..util.solver import ( check_solver_status, linear_reaction_coefficients, set_objective, ) from ..util.util import format_long_string from .configuration import Configuration from .gene import GPR, Gene from .metabolite import Metabolite from .object import Object config = Configuration() # This regular expression finds any single letter compartment enclosed in # square brackets at the beginning of the string. For example [c] : foo --> bar compartment_finder = re.compile(r"^\s*(\[[A-Za-z]\])\s*:*") # Regular expressions to match the arrows _reversible_arrow_finder = re.compile("<(-+|=+)>") _forward_arrow_finder = re.compile("(-+|=+)>") _reverse_arrow_finder = re.compile("<(-+|=+)") class Reaction(Object): """Define the cobra.Reaction class. Reaction is a class for holding information regarding a biochemical reaction in a cobra.Model object. Reactions are by default irreversible with bounds `(0.0, cobra.Configuration().upper_bound)` if no bounds are provided on creation. To create an irreversible reaction use `lower_bound=None`, resulting in reaction bounds of `(cobra.Configuration().lower_bound, cobra.Configuration().upper_bound)`. Parameters ---------- id : str, optional The identifier to associate with this reaction (default None). name : str, optional A human readable name for the reaction (default ""). subsystem : str, optional Subsystem where the reaction is meant to occur (default ""). lower_bound : float The lower flux bound (default 0.0). upper_bound : float, optional The upper flux bound (default None). **kwargs: Further keyword arguments are passed on to the parent class. """ # noinspection PyShadowingBuiltins def __init__( self, id: Optional[str] = None, name: str = "", subsystem: str = "", lower_bound: float = 0.0, upper_bound: Optional[float] = None, **kwargs, ) -> None: """Initialize the Reaction class.""" super().__init__(id, name, **kwargs) self._gpr = GPR() self.subsystem = subsystem # The cobra.Genes that are used to catalyze the reaction self._genes = set() # A dictionary of metabolites and their stoichiometric coefficients in # this reaction. self._metabolites = {} # self.model is None or refers to the cobra.Model that # contains self self._model = None # from cameo ... self._lower_bound = ( lower_bound if lower_bound is not None else config.lower_bound ) self._upper_bound = ( upper_bound if upper_bound is not None else config.upper_bound ) def _set_id_with_model(self, value: str) -> None: """Set Reaction id in model, check that it doesn't already exist. The function will rebuild the model reaction index. Parameters ---------- value: str A string that represents the id. Raises ------ ValueError If the model already contains a reaction with the id value. """ if value in self.model.reactions: raise ValueError( f"The model already contains a reaction with the id: {value}" ) forward_variable = self.forward_variable reverse_variable = self.reverse_variable self._id = value self.model.reactions._generate_index() forward_variable.name = self.id reverse_variable.name = self.reverse_id @property def reverse_id(self) -> str: """Generate the id of reverse_variable from the reaction's id. Returns ------- str The original id, joined to the word reverse and a partial hash of the utf-8 encoded id. """ return "_".join( (self.id, "reverse", hashlib.md5(self.id.encode("utf-8")).hexdigest()[0:5]) ) @property def flux_expression(self) -> Optional["Variable"]: """Get Forward flux expression. Returns ------- flux_expression: optlang.interface.Variable, optional The expression representing the the forward flux (if associated with model), otherwise None. Representing the net flux if model.reversible_encoding == 'unsplit' or None if reaction is not associated with a model """ if self.model is not None: return 1.0 * self.forward_variable - 1.0 * self.reverse_variable else: return None @property def forward_variable(self) -> Optional["Variable"]: """Get an optlang variable representing the forward flux. Returns ------- optlang.interface.Variable, optional An optlang variable for the forward flux or None if reaction is not associated with a model. """ if self.model is not None: return self.model.variables[self.id] else: return None @property def reverse_variable(self) -> Optional["Variable"]: """Get an optlang variable representing the reverse flux. Returns ------- optlang.interface.Variable, optional An optlang variable for the reverse flux or None if reaction is not associated with a model. """ if self.model is not None: return self.model.variables[self.reverse_id] else: return None @property def objective_coefficient(self) -> float: """Get the coefficient for this reaction in a linear objective (float). Assuming that the objective of the associated model is summation of fluxes from a set of reactions, the coefficient for each reaction can be obtained individually using this property. A more general way is to use the `model.objective` property directly. Returns ------ float Linear coefficient if this reaction has any, or 0.0 otherwise. Raises ------ AttributeError If the model of the reaction is missing (None). """ return linear_reaction_coefficients(self.model, [self]).get(self, 0.0) @objective_coefficient.setter def objective_coefficient(self, value: float) -> None: """Set Objective coefficient. Parameters ---------- value: float Number to set coefficient to. Raises ------ AttributeError If reaction does not have model. """ if self.model is None: raise AttributeError("cannot assign objective to a missing model") if self.flux_expression is not None: set_objective(self.model, {self: value}, additive=True) def __copy__(self) -> "Reaction": """Copy the Reaction. Returns ------- Reaction A new reaction that is a copy of the original reaction. """ cop = copy(super(Reaction, self)) return cop def __deepcopy__(self, memo: dict) -> "Reaction": """Copy the reaction with memo. Parameters ---------- memo: dict Automatically passed parameter. Returns ------- Reaction A new reaction that is a deep copy of the original reaction with memo. """ cop = deepcopy(super(Reaction, self), memo) return cop @staticmethod def _check_bounds(lb: float, ub: float) -> None: """Check if the lower and upper bounds are valid. Parameters ---------- lb: float The lower bound. ub: float The upper bound. Raises ------ ValueError If the lower bound is higher than upper bound. """ if lb > ub: raise ValueError( f"The lower bound must be less than or equal to the upper bound " f"({lb} <= {ub})." ) def update_variable_bounds(self) -> None: """Update and correct variable bounds. Sets the forward_variable and reverse_variable bounds based on lower and upper bounds. This function corrects for bounds defined as inf or -inf. This function will also adjust the associated optlang variables associated with the reaction. See Also ------- optlang.interface.set_bounds """ if self.model is None: return # We know that `lb <= ub`. if self._lower_bound > 0: self.forward_variable.set_bounds( lb=None if isinf(self._lower_bound) else self._lower_bound, ub=None if isinf(self._upper_bound) else self._upper_bound, ) self.reverse_variable.set_bounds(lb=0, ub=0) elif self._upper_bound < 0: self.forward_variable.set_bounds(lb=0, ub=0) self.reverse_variable.set_bounds( lb=None if isinf(self._upper_bound) else -self._upper_bound, ub=None if isinf(self._lower_bound) else -self._lower_bound, ) else: self.forward_variable.set_bounds( lb=0, ub=None if isinf(self._upper_bound) else self._upper_bound ) self.reverse_variable.set_bounds( lb=0, ub=None if isinf(self._lower_bound) else -self._lower_bound ) @property def lower_bound(self) -> float: """Get the lower bound. Returns ------- float The lower bound of the reaction. """ return self._lower_bound @lower_bound.setter @resettable def lower_bound(self, value: float) -> None: """Set the lower bound. Parameters ---------- value: float The value to set the lower bound. Setting the lower bound (float) will also adjust the associated optlang variables associated with the reaction. When using a `HistoryManager` context, this attribute can be set temporarily, reversed when the exiting the context. Raises ------ ValueError If lower bound higher than the current upper bound. via _check_bounds. See Also -------- _check_bounds """ # Validate bounds before setting them. self._check_bounds(value, self._upper_bound) self._lower_bound = value self.update_variable_bounds() @property def upper_bound(self) -> float: """Get the upper bound. Returns ------- float The upper bound of the reaction. """ return self._upper_bound @upper_bound.setter @resettable def upper_bound(self, value: float) -> None: """Set the upper bound. Parameters ---------- value: float The value to set the upper bound. Setting the upper bound (float) will also adjust the associated optlang variables associated with the reaction. When using a `HistoryManager` context, this attribute can be set temporarily, reversed when the exiting the context. Raises ------ ValueError If upper bound lower than the current upper bound. via _check_bounds. See Also -------- _check_bounds """ # Validate bounds before setting them. self._check_bounds(self._lower_bound, value) self._upper_bound = value self.update_variable_bounds() @property def bounds(self) -> Tuple[float, float]: """Get or the bounds. Returns ------- tuple: lower_bound, upper_bound A tuple of floats, representing the lower and upper bound. """ return self.lower_bound, self.upper_bound @bounds.setter @resettable def bounds(self, value: Union[Tuple[float, float], Sequence[float]]) -> None: """Set the bounds directly, using a tuple or list. Parameters ---------- value: tuple or sequence The lower bound and upper bound. Invalid bounds will raise ValueError. When using a `HistoryManager` context, this attribute can be set temporarily, reversed when the exiting the context. Raises ------ ValueError If lower bound higher than upper bound, via _check_bounds. """ lower, upper = value # Validate bounds before setting them. self._check_bounds(lower, upper) self._lower_bound = lower self._upper_bound = upper self.update_variable_bounds() @property def flux(self) -> float: """ Get the flux value in the most recent solution. Flux is the primal value of the corresponding variable in the model. Returns ------- flux: float Flux is the primal value of the corresponding variable in the model. Warnings -------- * Accessing reaction fluxes through a `Solution` object is the safer, preferred, and only guaranteed to be correct way. You can see how to do so easily in the examples. * Reaction flux is retrieved from the currently defined `self._model.solver`. The solver status is checked but there are no guarantees that the current solver state is the one you are looking for. * If you modify the underlying model after an optimization, you will retrieve the old optimization values. Raises ------ RuntimeError If the underlying model was never optimized beforehand or the reaction is not part of a model. OptimizationError If the solver status is anything other than 'optimal'. AssertionError If the flux value is not within the bounds. Examples -------- >>> from cobra.io import load_model >>> model = load_model("textbook") >>> solution = model.optimize() >>> model.reactions.PFK.flux 7.477381962160283 >>> solution.fluxes.PFK 7.4773819621602833 """ try: check_solver_status(self._model.solver.status) return self.forward_variable.primal - self.reverse_variable.primal except AttributeError: raise RuntimeError(f"reaction '{self.id}' is not part of a model") # Due to below all-catch, which sucks, need to reraise these. except (RuntimeError, OptimizationError) as err: raise err # Would love to catch CplexSolverError and GurobiError here. except Exception as err: raise OptimizationError( f"Likely no solution exists. Original solver message: {str(err)}." ) from err @property def reduced_cost(self) -> float: """ Get the reduced cost in the most recent solution. Reduced cost is the dual value of the corresponding variable in the model. Returns ------- reducd_cost: float A float representing the reduced cost. Warnings -------- * Accessing reduced costs through a `Solution` object is the safer, preferred, and only guaranteed to be correct way. You can see how to do so easily in the examples. * Reduced cost is retrieved from the currently defined `self._model.solver`. The solver status is checked but there are no guarantees that the current solver state is the one you are looking for. * If you modify the underlying model after an optimization, you will retrieve the old optimization values. Raises ------ RuntimeError If the underlying model was never optimized beforehand or the reaction is not part of a model. OptimizationError If the solver status is anything other than 'optimal'. Examples -------- >>> from cobra.io import load_model >>> model = load_model("textbook") >>> solution = model.optimize() >>> model.reactions.PFK.reduced_cost -8.673617379884035e-18 >>> solution.reduced_costs.PFK -8.6736173798840355e-18 """ try: check_solver_status(self._model.solver.status) return self.forward_variable.dual - self.reverse_variable.dual except AttributeError: raise RuntimeError(f"reaction '{self.id}' is not part of a model") # Due to below all-catch, which sucks, need to reraise these. except (RuntimeError, OptimizationError) as err: raise err # Would love to catch CplexSolverError and GurobiError here. except Exception as err: raise OptimizationError( f"Likely no solution exists. Original solver message: {str(err)}." ) from err # read-only @property def metabolites(self) -> Dict[Metabolite, float]: """Get a dictionary of metabolites and coefficients. Returns ------- metaoblites: Dict[Metabolite, float] A copy of self._metabolites, which is a dictionary of cobra.Metabolite for keys and floats for coeffecieints. Positive coefficient means the reaction produces this metabolite, while negative coefficient means the reaction consumes this metabolite. """ return self._metabolites.copy() @property def genes(self) -> FrozenSet: """Return the genes of the reaction. Returns ------- genes: FrozenSet """ return frozenset(self._genes) def update_genes_from_gpr(self) -> None: """Update genes of reation based on GPR. If the reaction has a model, and new genes appear in the GPR, they will be created as Gene() entities and added to the model. If the reaction doesn't have a model, genes will be created without a model. Genes that no longer appear in the GPR will be removed from the reaction, but not the model. If you want to remove them expliclty, use model.remove_genes(). """ context = get_context(self) if self._gpr.body is not None: new_gene_names = self._gpr.genes else: new_gene_names = set() old_genes = self._genes.copy() new_genes = set() if self._model is None: self._genes = {Gene(i) for i in new_gene_names} else: model_genes = self._model.genes self._genes = set() for g_id in new_gene_names: if not model_genes.has_id(g_id): new_gene = Gene(g_id) new_gene._model = self._model model_genes.append(new_gene) if context: # Remove the gene later context( partial( remove_genes, model=self._model, gene_list=[model_genes.get_by_id(g_id)], remove_reactions=False, ) ) context(partial(setattr, new_gene, "_model", None)) # Maybe should be # context(partial(self._model.genes.__isub__, [new_gene])) new_gene = model_genes.get_by_id(g_id) self._genes.add(new_gene) new_genes.add(new_gene) # Make the genes aware that it is involved in this reaction for g in self._genes: self._associate_gene(g) if context: context(partial(self._dissociate_gene, g)) # make the old genes aware they are no longer involved in this reaction for g in old_genes.difference(new_genes): try: self._dissociate_gene(g) if context: context(partial(self._associate_gene, g)) except KeyError: warn(f"could not remove old gene {g.id} from reaction {self.id}") if g in self._genes: # if an old gene is still a new gene raise Exception("something wrong with sets. Shouldn't happen.") @property def gene_reaction_rule(self) -> str: """See gene reaction rule as str. Uses the to_string() method of the GPR class Returns ------- str """ return self._gpr.to_string() @gene_reaction_rule.setter @resettable def gene_reaction_rule(self, new_rule: str) -> None: """Set a new GPR for the reaction, using a str expression. Will use the new GPR to update reaction genes. Parameters ---------- new_rule : str which will be parsed by the string parser in GPR, GPR.from_string(new_rule). It makes a new GPR, and does not modify the existing one. See Also -------- update_genes_from_gpr() """ self._gpr = GPR.from_string(new_rule) self.update_genes_from_gpr() @property def gene_name_reaction_rule(self): """Display gene_reaction_rule with names intead. Do NOT use this string for computation. It is intended to give a representation of the rule using more familiar gene names instead of the often cryptic ids. """ names = {i.id: i.name for i in self._genes} return self._gpr.to_string(names=names) @property def gpr(self) -> GPR: """Return the GPR associated with the reaction. Returns ------- gpr: GPR The GPR class, see cobra.core.gene.GPR() for details. """ return self._gpr @gpr.setter @resettable def gpr(self, value: GPR) -> None: """Set a new GPR for the reaction, using GPR() class. Also updates the reaction genes based on GPR. Parameters ---------- value : GPR() class. See Also -------- cobra.core.gene.GPR() update_genes_from_gpr() """ self._gpr = value self.update_genes_from_gpr() @property def functional(self) -> bool: """All required enzymes for reaction are functional. Returns ------- bool True if the gene-protein-reaction (GPR) rule is fulfilled for this reaction, or if reaction is not associated to a model, otherwise False. """ if self._model: return self._gpr.eval( {gene.id for gene in self.genes if not gene.functional} ) return True @property def x(self) -> float: """Get the flux through the reaction in the most recent solution. Flux values are computed from the primal values of the variables in the solution. Returns ------- flux: float Float representing the flux value. .. deprecated :: Use reaction.flux instead. """ warn("Please use reaction.flux instead.", DeprecationWarning) return self.flux @property def y(self) -> float: """Get the reduced cost of the reaction in the most recent solution. Reduced costs are computed from the dual values of the variables in the solution. Returns ------- flux: float Float representing the reduced cost value. .. deprecated :: Use reaction.reduced_cost instead. """ warn("Please use reaction.reduced_cost instead.", DeprecationWarning) return self.reduced_cost @property def reversibility(self) -> bool: """Whether the reaction can proceed in both directions (reversible). This is computed from the current upper and lower bounds. Returns ------- bool: True if the reaction is reversible (lower bound lower than 0 and upper bound is higher than 0). """ return self._lower_bound < 0 < self._upper_bound @reversibility.setter def reversibility(self, value: bool) -> None: """Set reversiblitly (ignored). At this stage this is not set and is ignored. Present for compatiblity reasons. If you'd like to change the reversibility of the reaction change lower and upper bounds. Parameters ---------- value: bool This value is ignored. """ warn("Setting reaction reversibility is ignored") @property def boundary(self) -> bool: """Whether or not this reaction is an exchange reaction. Returns ------- bool: `True` if the reaction has either no products or reactants. """ return len(self.metabolites) == 1 and not (self.reactants and self.products) @property def model(self) -> Optional["Model"]: """Return the model the reaction is a part of. Returns ------- model: cobra.Model, optional The model this reaction belongs to. None if there is no model associated with this reaction. """ return self._model def _update_awareness(self) -> None: """Update awareness for genes and metaoblites of the reaction. Make sure all metabolites and genes that are associated with this reaction are aware of it. """ for x in self._metabolites: x._reaction.add(self) for x in self._genes: x._reaction.add(self) def remove_from_model(self, remove_orphans: bool = False) -> None: """Remove the reaction from a model. This removes all associations between a reaction the associated model, metabolites and genes. The change is reverted upon exit when using the model as a context. Parameters ---------- remove_orphans : bool Remove orphaned genes and metabolites from the model as well (default False). """ self._model.remove_reactions([self], remove_orphans=remove_orphans) def delete(self, remove_orphans: bool = False) -> None: """Remove the reaction from a model. This removes all associations between a reaction the associated model, metabolites and genes. The change is reverted upon exit when using the model as a context. .. deprecated :: use `reaction.remove_from_model` instead. Parameters ---------- remove_orphans : bool Remove orphaned genes and metabolites from the model as well (default False). """ warn( "delete is deprecated. Use reaction.remove_from_model instead", DeprecationWarning, ) self.remove_from_model(remove_orphans=remove_orphans) def __getstate__(self) -> Dict: """Get state for reaction. This serializes the reaction object. The GPR will be converted to a string to avoid unneccessary copies due to interdependencies of used objects. Returns ------- dict The state/attributes of the reaction in serilized form. """ state = self.__dict__.copy() state["_gpr"] = str(self._gpr) return state def __setstate__(self, state: Dict) -> None: """Set state for reaction. Probably not necessary to set _model as the cobra.Model that contains self sets the _model attribute for all metabolites and genes in the reaction. However, to increase performance speed we do want to let the metabolite and gene know that they are employed in this reaction Parameters ---------- state: dict A dictionary of state, where keys are attribute names (str). Similar to __dict__. """ # These are necessary for old pickles which store attributes # which have since been superceded by properties. if "reaction" in state: state.pop("reaction") if "gene_reaction_rule" in state: state["_gene_reaction_rule"] = state.pop("gene_reaction_rule") if "lower_bound" in state: state["_lower_bound"] = state.pop("lower_bound") if "upper_bound" in state: state["_upper_bound"] = state.pop("upper_bound") # Used for efficient storage in newer cobrapy versions if "_gpr" not in state: state["_gpr"] = state["_gene_reaction_rule"] if type(state["_gpr"]) is str: state["_gpr"] = GPR.from_string(state["_gpr"]) self.__dict__.update(state) for x in state["_metabolites"]: x._model = self._model x._reaction.add(self) for x in state["_genes"]: x._model = self._model x._reaction.add(self) def copy(self) -> "Reaction": """Copy a reaction. The referenced metabolites and genes are also copied. Returns ------- cobra.Reaction A copy of the Reaction. """ # no references to model when copying model = self._model self._model = None for i in self._metabolites: i._model = None for i in self._genes: i._model = None # now we can copy new_reaction = deepcopy(self) # restore the references self._model = model for i in self._metabolites: i._model = model for i in self._genes: i._model = model return new_reaction def __add__(self, other: "Reaction") -> "Reaction": """Add two reactions and return a new one. The stoichiometry will be the combined stoichiometry of the two reactions, and the gene reaction rule will be both rules combined by an and. All other attributes (i.e. reaction bounds) will match those of the first reaction. Does not modify in place. Parameters ---------- other: cobra.Reaction Another reaction to add to the current one. Returns ------- Reaction - new reaction with the added properties. """ new_reaction = self.copy() if other == 0: return new_reaction else: new_reaction += other return new_reaction __radd__ = __add__ def __iadd__(self, other: "Reaction") -> "Reaction": """Add two reactions in place and return the modified first one. The stoichiometry will be the combined stoichiometry of the two reactions, and the gene reaction rule will be both rules combined by an and. All other attributes (i.e. reaction bounds) will match those of the first reaction. Modifies in place. Parameters ---------- other: cobra.Reaction Another reaction to add to the current one. Returns ------- Reaction - original reaction (self) with the added properties. """ self.add_metabolites(other._metabolites, combine=True) rule1 = self.gene_reaction_rule.strip() rule2 = other.gene_reaction_rule.strip() if rule1 != "" and rule2 != "": self.gene_reaction_rule = ( f"({self.gene_reaction_rule}) and " f"({other.gene_reaction_rule})" ) elif rule1 != "" and rule2 == "": self.gene_reaction_rule = rule1 elif rule1 == "" and rule2 != "": self.gene_reaction_rule = rule2 return self def __sub__(self, other: "Reaction") -> "Reaction": """Subtract two reactions and return a new one. The stoichiometry will be the subtracted stoichiometry of the two reactions, and the gene_reaction_rule will be the gene_reaction_rule of the first reaction. All other attributes (i.e. reaction bounds) will match those of the first reaction. Does not modify in place. The name will still be that of the first reaction. Parameters ---------- other: Reaction The reaction to subtract from self. Returns ------- Reaction - new reaction with the added properties. """ new = self.copy() new -= other return new def __isub__(self, other: "Reaction") -> "Reaction": """Subtract metabolites of one reaction from another in place. The stoichiometry will be the metabolites of self minus the metabolites of the other. All other attributes including gene_reaction_rule (i.e. reaction bounds) will match those of the first reaction. Modifies in place and changes the original reaction. Parameters ---------- other: Reaction The reaction to subtract from self. Returns ------- Reaction - self with the subtracted metabolites. """ self.subtract_metabolites(other._metabolites, combine=True) return self def __imul__(self, coefficient: float) -> "Reaction": """Scale coefficients in a reaction by a given value in place. E.g. A -> B becomes 2A -> 2B. If coefficient is less than zero, the reaction is reversed and the bounds are swapped. Parameters ---------- coefficient: float Value to scale coefficients of metabolites by. If less than zero, reverses the reaction. Returns ------- Reaction Returns the same reaction modified in place. """ self._metabolites = { met: value * coefficient for met, value in self._metabolites.items() } if coefficient < 0: self.bounds = (-self.upper_bound, -self.lower_bound) if self._model: self._model._populate_solver([self]) context = get_context(self) if context: context(partial(self._model._populate_solver, [self])) context(partial(self.__imul__, 1.0 / coefficient)) return self def __mul__(self, coefficient: float) -> "Reaction": """Scale coefficients in a reaction by a given value and return new reaction. E.g. A -> B becomes 2A -> 2B. If coefficient is less than zero, the reaction is reversed and the bounds are swapped. Parameters ---------- coefficient: float Value to scale coefficients of metabolites by. If less than zero, reverses the reaction. Returns ------- Reaction Returns a new reaction, identical to the original except coefficients. """ new = self.copy() new *= coefficient return new @property def reactants(self) -> List[Metabolite]: """Return a list of reactants for the reaction. Returns ------- list A list of the metabolites consudmed (coefficient < 0) by the reaction. """ return [k for k, v in self._metabolites.items() if v < 0] @property def products(self) -> List[Metabolite]: """Return a list of products for the reaction. Returns ------- list A list of the metabolites produced (coefficient > 0) by the reaction. """ return [k for k, v in self._metabolites.items() if v >= 0] def get_coefficient(self, metabolite_id: Union[str, Metabolite]) -> float: """Return the stoichiometric coefficient of a metabolite. Parameters ---------- metabolite_id : str or cobra.Metabolite """ if isinstance(metabolite_id, Metabolite): return self._metabolites[metabolite_id] _id_to_metabolites = {m.id: m for m in self._metabolites} return self._metabolites[_id_to_metabolites[metabolite_id]] def get_coefficients( self, metabolite_ids: Iterable[Union[str, Metabolite]] ) -> Iterator[float]: """Return the stoichiometric coefficients for a list of metabolites. Parameters ---------- metabolite_ids : iterable Containing ``str`` or ``cobra.Metabolite``s. Returns ------- map: Iterable Returns the result of map function, which is a map object (an Iterable). """ return map(self.get_coefficient, metabolite_ids) def add_metabolites( self, metabolites_to_add: Dict[Metabolite, float], combine: bool = True, reversibly: bool = True, ) -> None: """Add metabolites and stoichiometric coefficients to the reaction. If the final coefficient for a metabolite is 0 then it is removed from the reaction. The change is reverted upon exit when using the model as a context. Parameters ---------- metabolites_to_add : dict Dictionary with metabolite objects or metabolite identifiers as keys and coefficients as values. If keys are strings (name of a metabolite) the reaction must already be part of a model and a metabolite with the given name must exist in the model. combine : bool Describes behavior if a metabolite already exists in the reaction (default True). True causes the coefficients to be added. False causes the coefficient to be replaced. reversibly : bool Whether to add the change to the context to make the change reversibly or not (primarily intended for internal use). Default is True. Raises ------ KeyError If the metabolite string id is not in the model. ValueError If the metabolite key in the dictionary is a string, and there is no model for the reaction. """ old_coefficients = self.metabolites new_metabolites = [] _id_to_metabolites = dict([(x.id, x) for x in self._metabolites]) for metabolite, coefficient in metabolites_to_add.items(): # Make sure metabolites being added belong to the same model, or # else copy them. if isinstance(metabolite, Metabolite): if (metabolite.model is not None) and ( metabolite.model is not self._model ): metabolite = metabolite.copy() met_id = str(metabolite) # If a metabolite already exists in the reaction then # just add them. if met_id in _id_to_metabolites: reaction_metabolite = _id_to_metabolites[met_id] if combine: self._metabolites[reaction_metabolite] += coefficient else: self._metabolites[reaction_metabolite] = coefficient else: # If the reaction is in a model, ensure we aren't using # a duplicate metabolite. if self._model: try: metabolite = self._model.metabolites.get_by_id(met_id) except KeyError as e: if isinstance(metabolite, Metabolite): new_metabolites.append(metabolite) else: # do we want to handle creation here? raise e elif isinstance(metabolite, str): # if we want to handle creation, this should be changed raise ValueError( f"Reaction '{self.id}' does not belong to a model. " f"Either add the reaction to a model or use Metabolite objects " f"instead of strings as keys." ) self._metabolites[metabolite] = coefficient # make the metabolite aware that it is involved in this # reaction metabolite._reaction.add(self) # from cameo ... model = self.model if model is not None: model.add_metabolites(new_metabolites) for metabolite, coefficient in self._metabolites.items(): model.constraints[metabolite.id].set_linear_coefficients( { self.forward_variable: coefficient, self.reverse_variable: -coefficient, } ) for metabolite, the_coefficient in list(self._metabolites.items()): if the_coefficient == 0: # make the metabolite aware that it no longer participates # in this reaction metabolite._reaction.remove(self) self._metabolites.pop(metabolite) context = get_context(self) if context and reversibly: if combine: # Just subtract the metabolites that were added context( partial( self.subtract_metabolites, metabolites_to_add, combine=True, reversibly=False, ) ) else: # Reset them with add_metabolites mets_to_reset = { key: old_coefficients[model.metabolites.get_by_any(key)[0]] for key in metabolites_to_add.keys() } context( partial( self.add_metabolites, mets_to_reset, combine=False, reversibly=False, ) ) def subtract_metabolites( self, metabolites: Dict[Metabolite, float], combine: bool = True, reversibly: bool = True, ) -> None: """Subtract metabolites from a reaction. That means add the metabolites with -1*coefficient. If the final coefficient for a metabolite is 0 then the metabolite is removed from the reaction. Notes ----- * A final coefficient < 0 implies a reactant. * The change is reverted upon exit when using the model as a context. Parameters ---------- metabolites : dict Dictionary where the keys are of class Metabolite and the values are the coefficients. These metabolites will be added to the reaction. combine : bool Describes behavior if a metabolite already exists in the reaction (default True). True causes the coefficients to be added. False causes the coefficient to be replaced. reversibly : bool Whether to add the change to the context to make the change reversibly or not ,primarily intended for internal use (default True). """ self.add_metabolites( {k: -v for k, v in metabolites.items()}, combine=combine, reversibly=reversibly, ) @property def reaction(self) -> str: """Return Human readable reaction str. Returns ------- str The reaction in a human readble str. """ return self.build_reaction_string() @reaction.setter def reaction(self, value: str) -> None: """Set reaction from Human readable reaction str. Parameters ------- value: str The reaction in a human readble str. See Also -------- build_reaction_from_string() """ self.build_reaction_from_string(value) def build_reaction_string(self, use_metabolite_names: bool = False) -> str: """Generate a human readable reaction str. Parameters ---------- use_metabolite_names: bool Whether to use metabolite names (when True) or metabolite ids (when False, default). Returns ------- str A human readable str. """ def format(number: Union[int, float]) -> str: return "" if number == 1 else str(number).rstrip(".") + " " id_type = "id" if use_metabolite_names: id_type = "name" reactant_bits = [] product_bits = [] for met in sorted(self._metabolites, key=attrgetter("id")): coefficient = self._metabolites[met] name = str(getattr(met, id_type)) if coefficient >= 0: product_bits.append(format(coefficient) + name) else: reactant_bits.append(format(abs(coefficient)) + name) reaction_string = " + ".join(reactant_bits) if not self.reversibility: if self.lower_bound < 0 and self.upper_bound <= 0: reaction_string += " <-- " else: reaction_string += " --> " else: reaction_string += " <=> " reaction_string += " + ".join(product_bits) return reaction_string def check_mass_balance(self) -> Dict[str, float]: """Compute mass and charge balance for the reaction. Returns ------- dict a dict of {element: amount} for unbalanced elements. "charge" is treated as an element in this dict This should be empty for balanced reactions. Raises ------ ValueError No elements were found in metabolite. """ reaction_element_dict = defaultdict(int) for metabolite, coefficient in self._metabolites.items(): if metabolite.charge is not None: reaction_element_dict["charge"] += coefficient * metabolite.charge if metabolite.elements is None: raise ValueError(f"No elements found in metabolite {metabolite.id}") for element, amount in metabolite.elements.items(): reaction_element_dict[element] += coefficient * amount # filter out 0 values return {k: v for k, v in reaction_element_dict.items() if v != 0} @property def compartments(self) -> Set: """Return set of compartments the metabolites are in. Returns ------- set A set of compartments the metabolites are in. """ return { met.compartment for met in self._metabolites if met.compartment is not None } def get_compartments(self) -> list: """List compartments the metabolites are in. Returns ------- list A list of compartments the metabolites are in. .. deprecated :: Use reaction.compartments() instead. """ warn("use Reaction.compartments instead", DeprecationWarning) return list(self.compartments) def _associate_gene(self, cobra_gene: Gene) -> None: """Associates a cobra.Gene object with a cobra.Reaction. Parameters ---------- cobra_gene : cobra.core.Gene.Gene """ self._genes.add(cobra_gene) cobra_gene._reaction.add(self) cobra_gene._model = self._model def _dissociate_gene(self, cobra_gene: Gene) -> None: """Dissociates a cobra.Gene object with a cobra.Reaction. Parameters ---------- cobra_gene : cobra.core.Gene.Gene """ self._genes.discard(cobra_gene) cobra_gene._reaction.discard(self) def knock_out(self) -> None: """Knockout reaction by setting its bounds to zero.""" self.bounds = (0, 0) def build_reaction_from_string( self, reaction_str: str, verbose: bool = True, fwd_arrow: Optional[AnyStr] = None, rev_arrow: Optional[AnyStr] = None, reversible_arrow: Optional[AnyStr] = None, term_split: str = "+", ) -> None: """Build reaction from reaction equation reaction_str using parser. Takes a string and using the specifications supplied in the optional arguments infers a set of metabolites, metabolite compartments and stoichiometries for the reaction. It also infers the reversibility of the reaction from the reaction arrow. Changes to the associated model are reverted upon exit when using the model as a context. Parameters ---------- reaction_str : str a string containing a reaction formula (equation) verbose: bool setting verbosity of function (default True) fwd_arrow : AnyStr, optional Str or bytes that encode forward irreversible reaction arrows (default None). rev_arrow : AnyStr, optional Str or bytes that encode backward irreversible reaction arrows (default None). reversible_arrow : AnyStr, optional Str or bytes that encode reversible reaction arrows (default None). term_split : str dividing individual metabolite entries (default "+")". Raises ------ ValueError No arrow found in reaction string. """ # set the arrows forward_arrow_finder = ( _forward_arrow_finder if fwd_arrow is None else re.compile(re.escape(fwd_arrow)) ) reverse_arrow_finder = ( _reverse_arrow_finder if rev_arrow is None else re.compile(re.escape(rev_arrow)) ) reversible_arrow_finder = ( _reversible_arrow_finder if reversible_arrow is None else re.compile(re.escape(reversible_arrow)) ) if self._model is None: warn("no model found") model = None else: model = self._model found_compartments = compartment_finder.findall(reaction_str) if len(found_compartments) == 1: compartment = found_compartments[0] reaction_str = compartment_finder.sub("", reaction_str) else: compartment = "" # reversible case arrow_match = reversible_arrow_finder.search(reaction_str) if arrow_match is not None: self.bounds = config.lower_bound, config.upper_bound else: # irreversible # try forward arrow_match = forward_arrow_finder.search(reaction_str) if arrow_match is not None: self.bounds = 0, config.upper_bound else: # must be reverse arrow_match = reverse_arrow_finder.search(reaction_str) if arrow_match is None: raise ValueError(f"no suitable arrow found in '{reaction_str}'") else: self.bounds = config.lower_bound, 0 reactant_str = reaction_str[: arrow_match.start()].strip() product_str = reaction_str[arrow_match.end() :].strip() self.subtract_metabolites(self.metabolites, combine=True) for substr, factor in ((reactant_str, -1), (product_str, 1)): if len(substr) == 0: continue for term in substr.split(term_split): term = term.strip() if term.lower() == "nothing": continue if " " in term: num_str, met_id = term.split() num = float(num_str.lstrip("(").rstrip(")")) * factor else: met_id = term num = factor met_id += compartment try: met = model.metabolites.get_by_id(met_id) except KeyError: if verbose: print(f"unknown metabolite '{met_id}' created") met = Metabolite(met_id) self.add_metabolites({met: num}) def summary( self, solution: Optional["Solution"] = None, fva: Optional[Union[float, "pd.DataFrame"]] = None, ) -> "ReactionSummary": """ Create a summary of the reaction flux. Parameters ---------- solution : cobra.Solution, optional A previous model solution to use for generating the summary. If ``None``, the summary method will generate a parsimonious flux distribution (default None). fva : pandas.DataFrame or float, optional Whether or not to include flux variability analysis in the output. If given, `fva` should either be a previous FVA solution matching the model or a float between 0 and 1 representing the fraction of the optimum objective to be searched (default None). Returns ------- cobra.summary.ReactionSummary See Also -------- Metabolite.summary Model.summary """ from cobra.summary import ReactionSummary return ReactionSummary( reaction=self, model=self._model, solution=solution, fva=fva, ) def __str__(self) -> str: """Return reaction id and reaction as str. Returns ------- str A string comprised out of reaction id and reaction. """ return f"{self.id}: {self.build_reaction_string()}" def _repr_html_(self) -> str: """Generate html representation of reaction. Returns ------- str HTML representation of the reaction. """ return f"""
Reaction identifier{format_long_string( self.id, 100)}
Name{format_long_string( self.name, 100)}
Memory address {f"{id(self):#x}"}
Stoichiometry

{format_long_string( self.build_reaction_string(), 200)}

{format_long_string( self.build_reaction_string(True), 200)}
GPR{format_long_string( self.gene_reaction_rule, 100)}
Lower bound{self.lower_bound}
Upper bound{self.upper_bound}
""" cobrapy-0.29.1/src/cobra/core/singleton.py000066400000000000000000000006161467301562300204350ustar00rootroot00000000000000"""Define the singleton meta class.""" class Singleton(type): """Implementation of the singleton pattern as a meta class.""" _instances = {} def __call__(cls, *args, **kwargs): """Override an inheriting class' call.""" if cls not in cls._instances: cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) return cls._instances[cls] cobrapy-0.29.1/src/cobra/core/solution.py000066400000000000000000000146231467301562300203120ustar00rootroot00000000000000"""Provide unified interfaces to optimization solutions.""" import logging from typing import TYPE_CHECKING, Iterable, Optional import numpy as np import pandas as pd from optlang.interface import OPTIMAL from ..util.solver import check_solver_status if TYPE_CHECKING: from cobra import Metabolite, Model, Reaction __all__ = ("Solution", "get_solution") logger = logging.getLogger(__name__) class Solution: """ A unified interface to a `cobra.Model` optimization solution. Parameters ---------- objective_value : float The (optimal) value for the objective function. status : str The solver status related to the solution. fluxes : pandas.Series Contains the reaction fluxes (primal values of variables). reduced_costs : pandas.Series Contains reaction reduced costs (dual values of variables) (default None). shadow_prices : pandas.Series Contains metabolite shadow prices (dual values of constraints) (default None). Attributes ---------- objective_value : float The (optimal) value for the objective function. status : str The solver status related to the solution. fluxes : pandas.Series Contains the reaction fluxes (primal values of variables). reduced_costs : pandas.Series Contains reaction reduced costs (dual values of variables). shadow_prices : pandas.Series Contains metabolite shadow prices (dual values of constraints). Notes ----- Solution is meant to be constructed by `get_solution` please look at that function to fully understand the `Solution` class. """ def __init__( self, objective_value: float, status: str, fluxes: pd.Series, reduced_costs: Optional[pd.Series] = None, shadow_prices: Optional[pd.Series] = None, **kwargs, ) -> None: """ Initialize a `Solution` from its components. Other Parameters ---------------- kwargs : Further keyword arguments are passed on to the parent class. """ super().__init__(**kwargs) self.objective_value = objective_value self.status = status self.fluxes = fluxes self.reduced_costs = reduced_costs self.shadow_prices = shadow_prices def __repr__(self) -> str: """Return a string representation of the solution instance.""" if self.status != OPTIMAL: return f"" return f"" def _repr_html_(self) -> str: """Return a rich HTML representation of the solution.""" if self.status == OPTIMAL: with pd.option_context("display.max_rows", 10): html = ( "Optimal solution with objective " f"value {self.objective_value:.3f}
" f"{self.to_frame()._repr_html_()}" ) else: html = f"{self.status} solution" return html def __getitem__(self, reaction_id: str) -> float: """ Return the flux of a reaction. Parameters ---------- reaction_id : str A model reaction ID. Returns ------- float The flux of the reaction with ID `reaction_id`. """ return self.fluxes[reaction_id] get_primal_by_id = __getitem__ def to_frame(self) -> pd.DataFrame: """Return the fluxes and reduced costs as a pandas DataFrame. Returns ------- pandas.DataFrame The fluxes and reduced cost. """ return pd.DataFrame( {"fluxes": self.fluxes, "reduced_costs": self.reduced_costs} ) def get_solution( model: "Model", reactions: Optional[Iterable["Reaction"]] = None, metabolites: Optional[Iterable["Metabolite"]] = None, raise_error: bool = False, ) -> Solution: """ Generate a solution representation of the current solver state. Parameters --------- model : cobra.Model The model whose reactions to retrieve values for. reactions : list, optional An iterable of `cobra.Reaction` objects. Uses `model.reactions` if None (default None). metabolites : list, optional An iterable of `cobra.Metabolite` objects. Uses `model.metabolites` if None (default None). raise_error : bool If True, raise an OptimizationError if solver status is not optimal (default False). Returns ------- cobra.Solution """ check_solver_status(model.solver.status, raise_error=raise_error) if reactions is None: reactions = model.reactions if metabolites is None: metabolites = model.metabolites rxn_index = [] fluxes = np.empty(len(reactions)) reduced = np.empty(len(reactions)) var_primals = model.solver.primal_values shadow = np.empty(len(metabolites)) try: var_duals = model.solver.reduced_costs constr_duals = model.solver.shadow_prices duals_available = True except Exception: duals_available = False if model.solver.is_integer or not duals_available: reduced.fill(np.nan) shadow.fill(np.nan) for i, rxn in enumerate(reactions): rxn_index.append(rxn.id) fluxes[i] = var_primals[rxn.id] - var_primals[rxn.reverse_id] met_index = [met.id for met in metabolites] else: var_duals = model.solver.reduced_costs for i, rxn in enumerate(reactions): forward = rxn.id reverse = rxn.reverse_id rxn_index.append(forward) fluxes[i] = var_primals[forward] - var_primals[reverse] reduced[i] = var_duals[forward] - var_duals[reverse] met_index = [] constr_duals = model.solver.shadow_prices for i, met in enumerate(metabolites): met_index.append(met.id) shadow[i] = constr_duals[met.id] return Solution( objective_value=model.solver.objective.value, status=model.solver.status, fluxes=pd.Series(index=rxn_index, data=fluxes, name="fluxes"), reduced_costs=pd.Series(index=rxn_index, data=reduced, name="reduced_costs"), shadow_prices=pd.Series(index=met_index, data=shadow, name="shadow_prices"), ) cobrapy-0.29.1/src/cobra/core/species.py000066400000000000000000000053521467301562300200700ustar00rootroot00000000000000"""Define the Species class, used as a base for Gene and Metabolite.""" from copy import deepcopy from typing import TYPE_CHECKING, FrozenSet, Optional from ..core.object import Object if TYPE_CHECKING: from .. import Model class Species(Object): """Species is a base class in Cobrapy. Species is a class for holding information regarding a chemical Species Parameters ---------- id : string An identifier for the chemical species name : string A human readable name. """ # noinspection PyShadowingBuiltins def __init__( self, id: Optional[str] = None, name: Optional[str] = None, **kwargs ) -> None: """Initialize a Species. Parameters ---------- id : string, optional, default None An identifier for the chemical species name : string, optional, default None A human readable name. A species also contains a _model, reference to a cobra.model (initialized as None) and a self._reaction, a set of cobra.reactions (initialized as empty set). """ super().__init__(id=id, name=name, **kwargs) self._model = None # references to reactions that operate on this species self._reaction = set() @property def reactions(self) -> FrozenSet: """Return a frozenset of reactions. Returns ------- FrozenSet A frozenset that includes the reactions of the species. """ return frozenset(self._reaction) def __getstate__(self) -> dict: """Return the state of the species. Remove the references to container reactions when serializing to avoid problems associated with recursion. Returns ------- dict A dictionary describing the state, without the self._reaction to avoid recursion. """ state = Object.__getstate__(self) state["_reaction"] = set() return state def copy(self) -> "Species": """Copy a species. When copying a reaction, it is necessary to deepcopy the components so the list references aren't carried over. Additionally, a copy of a reaction is no longer in a cobra.Model. This should be fixed with self.__deepcopy__ if possible Returns ------- Species A copy of the species. """ return deepcopy(self) @property def model(self) -> Optional["Model"]: """Return the model. Returns ------- model Returns the cobra model that the species is associated with. None if there is no model associated with this species. 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"objective_coefficient": 1.0, "upper_bound": 1000.0 }, { "gene_reaction_rule": "", "id": "D_LACt2", "lower_bound": -1000.0, "metabolites": {}, "name": "", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "ENO" }, "gene_reaction_rule": "b2779", "id": "ENO", "lower_bound": -1000.0, "metabolites": { "2pg_c": -1.0, "h2o_c": 1.0, "pep_c": 1.0 }, "name": "enolase", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "glc", "sbo": "SBO:0000627" }, "gene_reaction_rule": "", "id": "EX_glc__D_e", "lower_bound": -10.0, "metabolites": { "glc__D_e": -1.0 }, "name": "D-Glucose exchange", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "h", "sbo": "SBO:0000627" }, "gene_reaction_rule": "", "id": "EX_h_e", "lower_bound": -1000.0, "metabolites": { "h_e": -1.0 }, "name": "H+ exchange", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "lac__D", "sbo": "SBO:0000627" }, "gene_reaction_rule": "", "id": "EX_lac__D_e", "lower_bound": 0.0, "metabolites": { "lac__D_e": -1.0 }, "name": "D-lactate exchange", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "FBA" }, "gene_reaction_rule": "b1773 or b2097 or b2925", "id": "FBA", "lower_bound": -1000.0, "metabolites": { "dhap_c": 1.0, "fdp_c": -1.0, "g3p_c": 1.0 }, "name": "fructose-bisphosphate aldolase", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "GAPD" }, "gene_reaction_rule": "b1779", "id": "GAPD", "lower_bound": -1000.0, "metabolites": { "13dpg_c": 1.0, "g3p_c": -1.0, "h_c": 1.0, "nad_c": -1.0, "nadh_c": 1.0, "pi_c": -1.0 }, "name": "glyceraldehyde-3-phosphate dehydrogenase", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "GLCpts" }, "gene_reaction_rule": "(b2415 and b2417 and b1101 and b2416) or (b2415 and b2417 and b1621 and b2416) or (b2415 and b1818 and b1817 and b1819 and b2416)", "id": "GLCpts", "lower_bound": 0.0, "metabolites": { "g6p_c": 1.0, "glc__D_e": -1.0, "pep_c": -1.0, "pyr_c": 1.0 }, "name": "D-glucose transport via PEP:Pyr PTS", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "H2Ot" }, "gene_reaction_rule": "b0875 or s0001", "id": "H2Ot", "lower_bound": -1000.0, "metabolites": { "h2o_c": 1.0, "h2o_e": -1.0 }, "name": "R H2O transport via - diffusion", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "LDH_D", "biocyc": "META:DLACTDEHYDROGNAD-RXN", "ec-code": "1.1.1.28", "kegg.reaction": "R00704", "metanetx.reaction": "MNXR101037", "rhea": [ "16369", "16370", "16371", "16372" ], "sbo": "SBO:0000375" }, "gene_reaction_rule": "b2133 or b1380", "id": "LDH_D", "lower_bound": -1000.0, "metabolites": { "h_c": 1.0, "lac__D_c": -1.0, "nad_c": -1.0, "nadh_c": 1.0, "pyr_c": 1.0 }, "name": "D-lactate dehydrogenase", "subsystem": "Pyruvate Metabolism", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "PFK" }, "gene_reaction_rule": "b3916 or b1723", "id": "PFK", "lower_bound": 0.0, "metabolites": { "adp_c": 1.0, "atp_c": -1.0, "f6p_c": -1.0, "fdp_c": 1.0, "h_c": 1.0 }, "name": "phosphofructokinase", "objective_coefficient": 1.0, "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "PGI" }, "gene_reaction_rule": "b4025", "id": "PGI", "lower_bound": -1000.0, "metabolites": { "f6p_c": 1.0, "g6p_c": -1.0 }, "name": "glucose-6-phosphate isomerase", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "PGK" }, "gene_reaction_rule": "b2926", "id": "PGK", "lower_bound": -1000.0, "metabolites": { "13dpg_c": 1.0, "3pg_c": -1.0, "adp_c": 1.0, "atp_c": -1.0 }, "name": "phosphoglycerate kinase", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "PGM" }, "gene_reaction_rule": "b4395 or b3612 or b0755", "id": "PGM", "lower_bound": -1000.0, "metabolites": { "2pg_c": -1.0, "3pg_c": 1.0 }, "name": "phosphoglycerate mutase", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "PIt2r" }, "gene_reaction_rule": "b2987 or b3493", "id": "PIt2r", "lower_bound": -1000.0, "metabolites": { "h_c": 1.0, "h_e": -1.0, "pi_c": 1.0, "pi_e": -1.0 }, "name": "R phosphate reversible transport via - symport", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "PYK" }, "gene_reaction_rule": "b1854 or b1676", "id": "PYK", "lower_bound": 0.0, "metabolites": { "adp_c": -1.0, "atp_c": 1.0, "h_c": -1.0, "pep_c": -1.0, "pyr_c": 1.0 }, "name": "pyruvate kinase", "upper_bound": 1000.0 }, { "annotation": { "bigg.reaction": "TPI" }, "gene_reaction_rule": "b3919", "id": "TPI", "lower_bound": -1000.0, "metabolites": { "dhap_c": -1.0, "g3p_c": 1.0 }, "name": "triose-phosphate isomerase", "upper_bound": 1000.0 } ], "version": "1" }cobrapy-0.29.1/src/cobra/data/mini.mat000066400000000000000000001532701467301562300175060ustar00rootroot00000000000000MATLAB 5.0 MAT-file Platform: posix, Created on: Mon Jun 27 21:15:39 2022IM0 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CHEBI:28013, CHEBI:32967, CHEBI:41014, CHEBI:22767, CHEBI:10374, CHEBI:40595, CHEBI:40591h11CHEBI:17138, CHEBI:14333, CHEBI:5446, CHEBI:58027eeCHEBI:10399, CHEBI:22797, CHEBI:41041, CHEBI:17719, CHEBI:4170, CHEBI:61548, CHEBI:58247, CHEBI:123750hhCHEBI:15377, CHEBI:13365, CHEBI:41979, CHEBI:16234, CHEBI:36385, CHEBI:42857, CHEBI:27313, CHEBI:44819, CHEBI:29373, CHEBI:10743, CHEBI:5594, CHEBI:29356, CHEBI:53442, CHEBI:29375, CHEBI:29374, CHEBI:13419, CHEBI:43228, CHEBI:44292, CHEBI:13352, CHEBI:41981, CHEBI:29412, CHEBI:42043, CHEBI:33811, CHEBI:33813, CHEBI:35511, CHEBI:5585, CHEBI:44641, CHEBI:44701hhCHEBI:15377, CHEBI:13365, CHEBI:41979, CHEBI:16234, CHEBI:36385, CHEBI:42857, CHEBI:27313, CHEBI:44819, CHEBI:29373, CHEBI:10743, CHEBI:5594, CHEBI:29356, CHEBI:53442, CHEBI:29375, CHEBI:29374, CHEBI:13419, CHEBI:43228, CHEBI:44292, CHEBI:13352, CHEBI:41981, CHEBI:29412, CHEBI:42043, CHEBI:33811, CHEBI:33813, CHEBI:35511, CHEBI:5585, CHEBI:44641, CHEBI:44701p>>CHEBI:24636, CHEBI:15378, CHEBI:10744, CHEBI:13357, CHEBI:5584p>>CHEBI:24636, CHEBI:15378, CHEBI:10744, CHEBI:13357, CHEBI:5584WWCHEBI:11001, CHEBI:16004, CHEBI:18684, CHEBI:341, CHEBI:42105, CHEBI:42111, CHEBI:43701WWCHEBI:11001, CHEBI:16004, CHEBI:18684, CHEBI:341, CHEBI:42105, CHEBI:42111, CHEBI:43701CHEBI:21901, CHEBI:7422, CHEBI:44214, CHEBI:15846, CHEBI:13394, CHEBI:13393, CHEBI:44215, CHEBI:13389, CHEBI:57540, CHEBI:44281XXCHEBI:13395, CHEBI:21902, CHEBI:16908, CHEBI:7423, CHEBI:44216, CHEBI:57945, CHEBI:13396eeCHEBI:44897, CHEBI:44894, CHEBI:14812, CHEBI:8147, CHEBI:26055, CHEBI:26054, CHEBI:58702, CHEBI:18021h33CHEBI:37583, CHEBI:7793, CHEBI:37585, CHEBI:34683, CHEBI:14791, CHEBI:34855, CHEBI:29137, CHEBI:29139, CHEBI:63036, CHEBI:26020, CHEBI:39739, CHEBI:32597, CHEBI:32596, CHEBI:43474, CHEBI:63051, CHEBI:43470, CHEBI:9679, CHEBI:35433, CHEBI:4496, CHEBI:45024, CHEBI:18367, CHEBI:26078, CHEBI:39745, CHEBI:24838h33CHEBI:37583, CHEBI:7793, CHEBI:37585, CHEBI:34683, CHEBI:14791, CHEBI:34855, CHEBI:29137, CHEBI:29139, CHEBI:63036, CHEBI:26020, CHEBI:39739, CHEBI:32597, CHEBI:32596, CHEBI:43474, CHEBI:63051, CHEBI:43470, CHEBI:9679, CHEBI:35433, CHEBI:4496, CHEBI:45024, CHEBI:18367, CHEBI:26078, CHEBI:39745, CHEBI:24838XXCHEBI:15361, CHEBI:14987, CHEBI:8685, CHEBI:32816, CHEBI:45253, CHEBI:26466, CHEBI:2646280008G111130000000000000000000003535039040349703310033040341103385036470393003392033330330303303033800338000033050330603374033110331103324000858-64-0856-65-5857-04-58643-13-08488-69-78142-10-9856-73-5850-99-7@  7732-18-5@  7732-18-5@  12408-02-5@  12408-02-500853-84-9858-68-48138-08-9@  14265-44-2@  14265-44-28127-17-3P813dpg02pg03pg0adp0atp0dhap0f6p0fdp0g3p0g6p8glc__D0h2o0h2o0h0h8lac__D8lac__D0nad0nadh0pep0pi0pi0pyr08cpd002038cpd004828cpd001698cpd000088cpd000028cpd000958cpd000728cpd002908cpd001028cpd000790Hcpd15275, cpd00001Hcpd15275, cpd000018cpd000678cpd000678cpd002218cpd002218cpd000038cpd000048cpd00061Pcpd09464, cpd09463, cpd00009Pcpd09464, cpd09463, cpd000098cpd00020h0DPG02-PG0G3P@ADP, ADP-GROUP0ATPPDIHYDROXY-ACETONE-PHOSPHATE@  FRUCTOSE-6PHFRUCTOSE-16-DIPHOSPHATE0PD-glucose-6-phosphate, GLC-6-P0HWATER, OH, OXONIUMHWATER, OH, OXONIUM8PROTON8PROTON@META:D-LACTATE@META:D-LACTATE0NAD0NADHHPHOSPHO-ENOL-PYRUVATEPPi, PHOSPHATE-GROUP, CPD0-1421PPi, PHOSPHATE-GROUP, CPD0-14218PYRUVATE @  REACT_29800@  REACT_30485@  REACT_29728REACT_190072, REACT_481002, REACT_211606, REACT_429160, REACT_29370, REACT_196180, REACT_113581, REACT_113582, REACT_114564, REACT_114565, REACT_429153mmREACT_190078, REACT_113592, REACT_113593, REACT_114570, REACT_29358, REACT_389573, REACT_139836, REACT_211579X''REACT_188451, REACT_75970, REACT_390404000@  REACT_16297560REACT_947593, REACT_189422, REACT_141343, REACT_113518, REACT_1605715, REACT_109276, REACT_113521, REACT_113519, REACT_2022884, REACT_351603, REACT_29356REACT_947593, REACT_189422, REACT_141343, REACT_113518, REACT_1605715, REACT_109276, REACT_113521, REACT_113519, REACT_2022884, REACT_351603, REACT_29356@  REACT_194688, REACT_425978, REACT_193465, REACT_374900, REACT_74722, REACT_425999, REACT_428040, REACT_163953, REACT_372511, REACT_2000349, REACT_70106, REACT_1470067, REACT_113529, REACT_425969, REACT_428548, REACT_156540, REACT_1614597, REACT_351626, REACT_427899@  REACT_194688, REACT_425978, REACT_193465, REACT_374900, REACT_74722, REACT_425999, REACT_428040, REACT_163953, REACT_372511, REACT_2000349, REACT_70106, REACT_1470067, REACT_113529, REACT_425969, REACT_428548, REACT_156540, REACT_1614597, REACT_351626, REACT_42789900xCCREACT_192307, REACT_29360, REACT_427523, REACT_194653, REACT_113526h44REACT_192305, REACT_73473, REACT_194697, REACT_29362PREACT_29492, REACT_372364``REACT_947590, REACT_109277, REACT_113548, REACT_2255331, REACT_29372, REACT_113550, REACT_113551``REACT_947590, REACT_109277, REACT_113548, REACT_2255331, REACT_29372, REACT_113550, REACT_113551X''REACT_113557, REACT_389680, REACT_29398h8b07558b08758b11018b13808b16218b16768b17238b17738b17798b18178b18188b18198b18548b20978b21338b24158b24168b24178b27798b29258b29268b29878b34938b36128b39168b39198b40258b43958s00010gpmA0aqpZ0ptsG0ldhA0malX0pykF0pfkB0ydjI0gapA0manX0manY0manZ0pykA0fbaB0dld0ptsH0ptsI0crr0eno0fbaA0pgk0pitB0pitA0gpmM0pfkA0tpiA0pgi0ytjC8G_s000100000@1208453, 165265400000000000081653839081653609000@1006614, 165191908165325300t        x  ?????????????????????????????H008b2779000Hb1773 or b2097 or b29258b1779(b2415 and b2417 and b1101 and b2416) or (b2415 and b2417 and b1621 and b2416) or (b2415 and b1818 and b1817 and b1819 and b2416)@b0875 or s0001@b2133 or b1380@b3916 or b17238b40258b2926Hb4395 or b3612 or b0755@b2987 or b3493@b1854 or b16768b3919`0ATPM8D_LACt20ENO@  EX_glc__D_e8EX_h_e@  EX_lac__D_e0FBA0GAPD8GLCpts0H2Ot8LDH_D0PFK0PGI0PGK0PGM8PIt2r0PYK0TPIPATP maintenance requirement08enolaseHD-Glucose exchange@  H+ exchangeHD-lactate exchangePfructose-bisphosphate aldolaseX((glyceraldehyde-3-phosphate dehydrogenaseX##D-glucose transport via PEP:Pyr PTSPR H2O transport via - diffusionHD-lactate dehydrogenaseHphosphofructokinasePglucose-6-phosphate isomeraseHphosphoglycerate kinaseHphosphoglycerate mutase`..R phosphate reversible transport via - symport@pyruvate kinasePtriose-phosphate isomerase 000000000081.1.1.28000000080ATPM00ENO0glc0h8lac__D0FBA0GAPD8GLCpts0H2Ot8LDH_D0PFK0PGI0PGK0PGM8PIt2r0PYK0TPI80000000000P16369, 16370, 16371, 163720000000(0000000000@  MNXR1010370000000X000@  SBO:0000627@  SBO:0000627@  SBO:00006270000@  SBO:0000375000000080000000000PMETA:DLACTDEHYDROGNAD-RXN0000000 00000000008R00704000000000000000000HPyruvate Metabolism0000000  ???????????????????????????? Hz @@@$@@@@@@@@@@ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@  ?? @  mini_textbookcobrapy-0.29.1/src/cobra/data/mini.yml000066400000000000000000000623771467301562300175350ustar00rootroot00000000000000!!omap - metabolites: - !!omap - id: 13dpg_c - name: 3-Phospho-D-glyceroyl phosphate - compartment: c - charge: -4 - formula: C3H4O10P2 - annotation: !!omap - bigg.metabolite: 13dpg - biocyc: DPG - chebi: - CHEBI:16001 - CHEBI:1658 - CHEBI:20189 - CHEBI:57604 - CHEBI:11881 - hmdb: HMDB01270 - kegg.compound: C00236 - pubchem.substance: '3535' - reactome: REACT_29800 - seed.compound: cpd00203 - unipathway.compound: UPC00236 - !!omap - id: 2pg_c - name: D-Glycerate 2-phosphate - compartment: c - charge: -3 - formula: C3H4O7P - annotation: !!omap - bigg.metabolite: 2pg - biocyc: 2-PG - chebi: - CHEBI:1267 - CHEBI:58289 - CHEBI:17835 - CHEBI:21028 - CHEBI:11651 - CHEBI:12986 - CHEBI:24344 - CHEBI:39868 - hmdb: - HMDB03391 - HMDB00362 - kegg.compound: C00631 - pubchem.substance: '3904' - reactome: REACT_30485 - seed.compound: cpd00482 - unipathway.compound: UPC00631 - !!omap - id: 3pg_c - name: 3-Phospho-D-glycerate - compartment: c - charge: -3 - formula: C3H4O7P - annotation: !!omap - bigg.metabolite: 3pg - biocyc: G3P - chebi: - CHEBI:40016 - CHEBI:58272 - CHEBI:57998 - CHEBI:11879 - CHEBI:1657 - CHEBI:1659 - CHEBI:17050 - CHEBI:21029 - CHEBI:11882 - CHEBI:11880 - CHEBI:12987 - CHEBI:17794 - CHEBI:24345 - hmdb: HMDB00807 - kegg.compound: - C00197 - C00597 - pubchem.substance: '3497' - reactome: REACT_29728 - seed.compound: cpd00169 - unipathway.compound: - UPC00597 - UPC00197 - !!omap - id: adp_c - name: ADP - compartment: c - charge: -3 - formula: C10H12N5O10P2 - annotation: !!omap - bigg.metabolite: adp - biocyc: - ADP - ADP-GROUP - cas: - 58-64-0 - chebi: - CHEBI:13222 - CHEBI:16761 - CHEBI:2342 - CHEBI:22244 - CHEBI:40553 - CHEBI:456216 - hmdb: HMDB01341 - kegg.compound: C00008 - kegg.glycan: G11113 - pubchem.substance: '3310' - reactome: - REACT_190072 - REACT_481002 - REACT_211606 - REACT_429160 - REACT_29370 - REACT_196180 - REACT_113581 - REACT_113582 - REACT_114564 - REACT_114565 - REACT_429153 - seed.compound: cpd00008 - unipathway.compound: UPC00008 - !!omap - id: atp_c - name: ATP - compartment: c - charge: -4 - formula: C10H12N5O13P3 - annotation: !!omap - bigg.metabolite: atp - biocyc: ATP - cas: - 56-65-5 - chebi: - CHEBI:40938 - CHEBI:15422 - CHEBI:57299 - CHEBI:13236 - CHEBI:10789 - CHEBI:30616 - CHEBI:22249 - CHEBI:10841 - CHEBI:2359 - hmdb: HMDB00538 - kegg.compound: C00002 - kegg.drug: D08646 - pubchem.substance: '3304' - reactome: - REACT_190078 - REACT_113592 - REACT_113593 - REACT_114570 - REACT_29358 - REACT_389573 - REACT_139836 - REACT_211579 - seed.compound: cpd00002 - unipathway.compound: UPC00002 - !!omap - id: dhap_c - name: Dihydroxyacetone phosphate - compartment: c - charge: -2 - formula: C3H5O6P - annotation: !!omap - bigg.metabolite: dhap - biocyc: DIHYDROXY-ACETONE-PHOSPHATE - cas: - 57-04-5 - chebi: - CHEBI:14341 - CHEBI:57642 - CHEBI:14342 - CHEBI:16108 - CHEBI:5454 - CHEBI:24355 - CHEBI:39571 - hmdb: - HMDB01473 - HMDB11735 - kegg.compound: C00111 - pubchem.substance: '3411' - reactome: - REACT_188451 - REACT_75970 - REACT_390404 - seed.compound: cpd00095 - unipathway.compound: UPC00111 - !!omap - id: f6p_c - name: D-Fructose 6-phosphate - compartment: c - charge: -2 - formula: C6H11O9P - annotation: !!omap - bigg.metabolite: f6p - biocyc: FRUCTOSE-6P - cas: - 643-13-0 - chebi: - CHEBI:57634 - CHEBI:12352 - CHEBI:45804 - CHEBI:61527 - CHEBI:61553 - CHEBI:10375 - CHEBI:16084 - CHEBI:42378 - CHEBI:22768 - hmdb: HMDB03971 - kegg.compound: - C05345 - C00085 - pubchem.substance: '3385' - seed.compound: cpd00072 - unipathway.compound: - UPC05345 - UPC00085 - !!omap - id: fdp_c - name: D-Fructose 1,6-bisphosphate - compartment: c - charge: -4 - formula: C6H10O12P2 - annotation: !!omap - bigg.metabolite: fdp - biocyc: FRUCTOSE-16-DIPHOSPHATE - cas: - 488-69-7 - chebi: - CHEBI:32968 - CHEBI:49299 - CHEBI:42553 - CHEBI:32966 - CHEBI:37736 - CHEBI:28013 - CHEBI:32967 - CHEBI:41014 - CHEBI:22767 - CHEBI:10374 - CHEBI:40595 - CHEBI:40591 - kegg.compound: - C05378 - C00354 - pubchem.substance: '3647' - seed.compound: cpd00290 - unipathway.compound: UPC00354 - !!omap - id: g3p_c - name: Glyceraldehyde 3-phosphate - compartment: c - charge: -2 - formula: C3H5O6P - annotation: !!omap - bigg.metabolite: g3p - cas: - 142-10-9 - chebi: - CHEBI:17138 - CHEBI:14333 - CHEBI:5446 - CHEBI:58027 - hmdb: HMDB01112 - kegg.compound: - C00661 - C00118 - pubchem.substance: '3930' - seed.compound: cpd00102 - unipathway.compound: - UPC00661 - UPC00118 - !!omap - id: g6p_c - name: D-Glucose 6-phosphate - compartment: c - charge: -2 - formula: C6H11O9P - annotation: !!omap - bigg.metabolite: g6p - biocyc: - D-glucose-6-phosphate - GLC-6-P - cas: - 56-73-5 - chebi: - CHEBI:10399 - CHEBI:22797 - CHEBI:41041 - CHEBI:17719 - CHEBI:4170 - CHEBI:61548 - CHEBI:58247 - CHEBI:12375 - hmdb: - HMDB03498 - HMDB06793 - HMDB01401 - HMDB01549 - kegg.compound: - C00092 - C01172 - pubchem.substance: '3392' - reactome: REACT_1629756 - seed.compound: cpd00079 - unipathway.compound: UPC00092 - !!omap - id: glc__D_e - name: D-Glucose - compartment: e - charge: 0 - formula: C6H12O6 - annotation: !!omap - bigg.metabolite: glc__D - cas: - 50-99-7 - kegg.compound: C00031 - pubchem.substance: '3333' - !!omap - id: h2o_c - name: H2O - compartment: c - charge: 0 - formula: H2O - annotation: !!omap - bigg.metabolite: h2o - biocyc: - WATER - OH - OXONIUM - cas: - 7732-18-5 - chebi: - CHEBI:15377 - CHEBI:13365 - CHEBI:41979 - CHEBI:16234 - CHEBI:36385 - CHEBI:42857 - CHEBI:27313 - CHEBI:44819 - CHEBI:29373 - CHEBI:10743 - CHEBI:5594 - CHEBI:29356 - CHEBI:53442 - CHEBI:29375 - CHEBI:29374 - CHEBI:13419 - CHEBI:43228 - CHEBI:44292 - CHEBI:13352 - CHEBI:41981 - CHEBI:29412 - CHEBI:42043 - CHEBI:33811 - CHEBI:33813 - CHEBI:35511 - CHEBI:5585 - CHEBI:44641 - CHEBI:44701 - hmdb: - HMDB01039 - HMDB02111 - kegg.compound: - C01328 - C00001 - C18714 - C18712 - kegg.drug: - D00001 - D06322 - D03703 - pubchem.substance: '3303' - reactome: - REACT_947593 - REACT_189422 - REACT_141343 - REACT_113518 - REACT_1605715 - REACT_109276 - REACT_113521 - REACT_113519 - REACT_2022884 - REACT_351603 - REACT_29356 - seed.compound: - cpd15275 - cpd00001 - unipathway.compound: - UPC00001 - UPC01328 - !!omap - id: h2o_e - name: H2O - compartment: e - charge: 0 - formula: H2O - annotation: !!omap - bigg.metabolite: h2o - biocyc: - WATER - OH - OXONIUM - cas: - 7732-18-5 - chebi: - CHEBI:15377 - CHEBI:13365 - CHEBI:41979 - CHEBI:16234 - CHEBI:36385 - CHEBI:42857 - CHEBI:27313 - CHEBI:44819 - CHEBI:29373 - CHEBI:10743 - CHEBI:5594 - CHEBI:29356 - CHEBI:53442 - CHEBI:29375 - CHEBI:29374 - CHEBI:13419 - CHEBI:43228 - CHEBI:44292 - CHEBI:13352 - CHEBI:41981 - CHEBI:29412 - CHEBI:42043 - CHEBI:33811 - CHEBI:33813 - CHEBI:35511 - CHEBI:5585 - CHEBI:44641 - CHEBI:44701 - hmdb: - HMDB01039 - HMDB02111 - kegg.compound: - C01328 - C00001 - C18714 - C18712 - kegg.drug: - D00001 - D06322 - D03703 - pubchem.substance: '3303' - reactome: - REACT_947593 - REACT_189422 - REACT_141343 - REACT_113518 - REACT_1605715 - REACT_109276 - REACT_113521 - REACT_113519 - REACT_2022884 - REACT_351603 - REACT_29356 - seed.compound: - cpd15275 - cpd00001 - unipathway.compound: - UPC00001 - UPC01328 - !!omap - id: h_c - name: H+ - compartment: c - charge: 1 - formula: H - annotation: !!omap - bigg.metabolite: h - biocyc: PROTON - cas: - 12408-02-5 - chebi: - CHEBI:24636 - CHEBI:15378 - CHEBI:10744 - CHEBI:13357 - CHEBI:5584 - kegg.compound: C00080 - pubchem.substance: '3380' - reactome: - REACT_194688 - REACT_425978 - REACT_193465 - REACT_374900 - REACT_74722 - REACT_425999 - REACT_428040 - REACT_163953 - REACT_372511 - REACT_2000349 - REACT_70106 - REACT_1470067 - REACT_113529 - REACT_425969 - REACT_428548 - REACT_156540 - REACT_1614597 - REACT_351626 - REACT_427899 - seed.compound: cpd00067 - unipathway.compound: UPC00080 - !!omap - id: h_e - name: H+ - compartment: e - charge: 1 - formula: H - annotation: !!omap - bigg.metabolite: h - biocyc: PROTON - cas: - 12408-02-5 - chebi: - CHEBI:24636 - CHEBI:15378 - CHEBI:10744 - CHEBI:13357 - CHEBI:5584 - kegg.compound: C00080 - pubchem.substance: '3380' - reactome: - REACT_194688 - REACT_425978 - REACT_193465 - REACT_374900 - REACT_74722 - REACT_425999 - REACT_428040 - REACT_163953 - REACT_372511 - REACT_2000349 - REACT_70106 - REACT_1470067 - REACT_113529 - REACT_425969 - REACT_428548 - REACT_156540 - REACT_1614597 - REACT_351626 - REACT_427899 - seed.compound: cpd00067 - unipathway.compound: UPC00080 - !!omap - id: lac__D_c - name: D-Lactate - compartment: c - charge: -1 - formula: C3H5O3 - annotation: !!omap - bigg.metabolite: lac__D - biocyc: META:D-LACTATE - chebi: - CHEBI:11001 - CHEBI:16004 - CHEBI:18684 - CHEBI:341 - CHEBI:42105 - CHEBI:42111 - CHEBI:43701 - hmdb: - HMDB00171 - HMDB01311 - kegg.compound: C00256 - metanetx.chemical: MNXM285 - seed.compound: cpd00221 - !!omap - id: lac__D_e - name: D-Lactate - compartment: e - charge: -1 - formula: C3H5O3 - annotation: !!omap - bigg.metabolite: lac__D - biocyc: META:D-LACTATE - chebi: - CHEBI:11001 - CHEBI:16004 - CHEBI:18684 - CHEBI:341 - CHEBI:42105 - CHEBI:42111 - CHEBI:43701 - hmdb: - HMDB00171 - HMDB01311 - kegg.compound: C00256 - metanetx.chemical: MNXM285 - seed.compound: cpd00221 - !!omap - id: nad_c - name: Nicotinamide adenine dinucleotide - compartment: c - charge: -1 - formula: C21H26N7O14P2 - annotation: !!omap - bigg.metabolite: nad - biocyc: NAD - cas: - 53-84-9 - chebi: - CHEBI:21901 - CHEBI:7422 - CHEBI:44214 - CHEBI:15846 - CHEBI:13394 - CHEBI:13393 - CHEBI:44215 - CHEBI:13389 - CHEBI:57540 - CHEBI:44281 - hmdb: HMDB00902 - kegg.compound: C00003 - kegg.drug: D00002 - pubchem.substance: '3305' - reactome: - REACT_192307 - REACT_29360 - REACT_427523 - REACT_194653 - REACT_113526 - seed.compound: cpd00003 - unipathway.compound: UPC00003 - !!omap - id: nadh_c - name: Nicotinamide adenine dinucleotide - reduced - compartment: c - charge: -2 - formula: C21H27N7O14P2 - annotation: !!omap - bigg.metabolite: nadh - biocyc: NADH - cas: - 58-68-4 - chebi: - CHEBI:13395 - CHEBI:21902 - CHEBI:16908 - CHEBI:7423 - CHEBI:44216 - CHEBI:57945 - CHEBI:13396 - hmdb: HMDB01487 - kegg.compound: C00004 - pubchem.substance: '3306' - reactome: - REACT_192305 - REACT_73473 - REACT_194697 - REACT_29362 - seed.compound: cpd00004 - unipathway.compound: UPC00004 - !!omap - id: pep_c - name: Phosphoenolpyruvate - compartment: c - charge: -3 - formula: C3H2O6P - annotation: !!omap - bigg.metabolite: pep - biocyc: PHOSPHO-ENOL-PYRUVATE - cas: - 138-08-9 - chebi: - CHEBI:44897 - CHEBI:44894 - CHEBI:14812 - CHEBI:8147 - CHEBI:26055 - CHEBI:26054 - CHEBI:58702 - CHEBI:18021 - hmdb: HMDB00263 - kegg.compound: C00074 - pubchem.substance: '3374' - reactome: - REACT_29492 - REACT_372364 - seed.compound: cpd00061 - unipathway.compound: UPC00074 - !!omap - id: pi_c - name: Phosphate - compartment: c - charge: -2 - formula: HO4P - annotation: !!omap - bigg.metabolite: pi - biocyc: - Pi - PHOSPHATE-GROUP - CPD0-1421 - cas: - 14265-44-2 - chebi: - CHEBI:37583 - CHEBI:7793 - CHEBI:37585 - CHEBI:34683 - CHEBI:14791 - CHEBI:34855 - CHEBI:29137 - CHEBI:29139 - CHEBI:63036 - CHEBI:26020 - CHEBI:39739 - CHEBI:32597 - CHEBI:32596 - CHEBI:43474 - CHEBI:63051 - CHEBI:43470 - CHEBI:9679 - CHEBI:35433 - CHEBI:4496 - CHEBI:45024 - CHEBI:18367 - CHEBI:26078 - CHEBI:39745 - CHEBI:24838 - hmdb: HMDB02142 - kegg.compound: - C13556 - C13558 - C00009 - kegg.drug: D05467 - pubchem.substance: '3311' - reactome: - REACT_947590 - REACT_109277 - REACT_113548 - REACT_2255331 - REACT_29372 - REACT_113550 - REACT_113551 - seed.compound: - cpd09464 - cpd09463 - cpd00009 - unipathway.compound: UPC00009 - !!omap - id: pi_e - name: Phosphate - compartment: e - charge: -2 - formula: HO4P - annotation: !!omap - bigg.metabolite: pi - biocyc: - Pi - PHOSPHATE-GROUP - CPD0-1421 - cas: - 14265-44-2 - chebi: - CHEBI:37583 - CHEBI:7793 - CHEBI:37585 - CHEBI:34683 - CHEBI:14791 - CHEBI:34855 - CHEBI:29137 - CHEBI:29139 - CHEBI:63036 - CHEBI:26020 - CHEBI:39739 - CHEBI:32597 - CHEBI:32596 - CHEBI:43474 - CHEBI:63051 - CHEBI:43470 - CHEBI:9679 - CHEBI:35433 - CHEBI:4496 - CHEBI:45024 - CHEBI:18367 - CHEBI:26078 - CHEBI:39745 - CHEBI:24838 - hmdb: HMDB02142 - kegg.compound: - C13556 - C13558 - C00009 - kegg.drug: D05467 - pubchem.substance: '3311' - reactome: - REACT_947590 - REACT_109277 - REACT_113548 - REACT_2255331 - REACT_29372 - REACT_113550 - REACT_113551 - seed.compound: - cpd09464 - cpd09463 - cpd00009 - unipathway.compound: UPC00009 - !!omap - id: pyr_c - name: Pyruvate - compartment: c - charge: -1 - formula: C3H3O3 - annotation: !!omap - bigg.metabolite: pyr - biocyc: PYRUVATE - cas: - 127-17-3 - chebi: - CHEBI:15361 - CHEBI:14987 - CHEBI:8685 - CHEBI:32816 - CHEBI:45253 - CHEBI:26466 - CHEBI:26462 - hmdb: HMDB00243 - kegg.compound: C00022 - lipidmaps: LMFA01060077 - pubchem.substance: '3324' - reactome: - REACT_113557 - REACT_389680 - REACT_29398 - seed.compound: cpd00020 - unipathway.compound: UPC00022 - reactions: - !!omap - id: ATPM - name: ATP maintenance requirement - metabolites: !!omap - adp_c: 1.0 - atp_c: -1.0 - h2o_c: -1.0 - h_c: 1.0 - pi_c: 1.0 - lower_bound: 8.39 - upper_bound: 1000.0 - gene_reaction_rule: '' - objective_coefficient: 1.0 - annotation: !!omap - bigg.reaction: ATPM - !!omap - id: D_LACt2 - name: '' - metabolites: !!omap [] - lower_bound: -1000.0 - upper_bound: 1000.0 - gene_reaction_rule: '' - !!omap - id: ENO - name: enolase - metabolites: !!omap - 2pg_c: -1.0 - h2o_c: 1.0 - pep_c: 1.0 - lower_bound: -1000.0 - upper_bound: 1000.0 - gene_reaction_rule: b2779 - annotation: !!omap - bigg.reaction: ENO - !!omap - id: EX_glc__D_e - name: D-Glucose exchange - metabolites: !!omap - glc__D_e: -1.0 - lower_bound: -10.0 - upper_bound: 1000.0 - gene_reaction_rule: '' - annotation: !!omap - bigg.reaction: glc - sbo: SBO:0000627 - !!omap - id: EX_h_e - name: H+ exchange - metabolites: !!omap - h_e: -1.0 - lower_bound: -1000.0 - upper_bound: 1000.0 - gene_reaction_rule: '' - annotation: !!omap - bigg.reaction: h - sbo: SBO:0000627 - !!omap - id: EX_lac__D_e - name: D-lactate exchange - metabolites: !!omap - lac__D_e: -1.0 - lower_bound: 0.0 - upper_bound: 1000.0 - gene_reaction_rule: '' - annotation: !!omap - bigg.reaction: lac__D - sbo: SBO:0000627 - !!omap - id: FBA - name: fructose-bisphosphate aldolase - metabolites: !!omap - dhap_c: 1.0 - fdp_c: -1.0 - g3p_c: 1.0 - lower_bound: -1000.0 - upper_bound: 1000.0 - gene_reaction_rule: b1773 or b2097 or b2925 - annotation: !!omap - bigg.reaction: FBA - !!omap - id: GAPD - name: glyceraldehyde-3-phosphate dehydrogenase - metabolites: !!omap - 13dpg_c: 1.0 - g3p_c: -1.0 - h_c: 1.0 - nad_c: -1.0 - nadh_c: 1.0 - pi_c: -1.0 - lower_bound: -1000.0 - upper_bound: 1000.0 - gene_reaction_rule: b1779 - annotation: !!omap - bigg.reaction: GAPD - !!omap - id: GLCpts - name: D-glucose transport via PEP:Pyr PTS - metabolites: !!omap - g6p_c: 1.0 - glc__D_e: -1.0 - pep_c: -1.0 - pyr_c: 1.0 - lower_bound: 0.0 - upper_bound: 1000.0 - gene_reaction_rule: (b2415 and b2417 and b1101 and b2416) or (b2415 and b2417 and b1621 and b2416) or (b2415 and b1818 and b1817 and b1819 and b2416) - annotation: !!omap - bigg.reaction: GLCpts - !!omap - id: H2Ot - name: R H2O transport via - diffusion - metabolites: !!omap - h2o_c: 1.0 - h2o_e: -1.0 - lower_bound: -1000.0 - upper_bound: 1000.0 - gene_reaction_rule: b0875 or s0001 - annotation: !!omap - bigg.reaction: H2Ot - !!omap - id: LDH_D - name: D-lactate dehydrogenase - metabolites: !!omap - h_c: 1.0 - lac__D_c: -1.0 - nad_c: -1.0 - nadh_c: 1.0 - pyr_c: 1.0 - lower_bound: -1000.0 - upper_bound: 1000.0 - gene_reaction_rule: b2133 or b1380 - subsystem: Pyruvate Metabolism - annotation: !!omap - bigg.reaction: LDH_D - biocyc: META:DLACTDEHYDROGNAD-RXN - ec-code: 1.1.1.28 - kegg.reaction: R00704 - metanetx.reaction: MNXR101037 - rhea: - '16369' - '16370' - '16371' - '16372' - sbo: SBO:0000375 - !!omap - id: PFK - name: phosphofructokinase - metabolites: !!omap - adp_c: 1.0 - atp_c: -1.0 - f6p_c: -1.0 - fdp_c: 1.0 - h_c: 1.0 - lower_bound: 0.0 - upper_bound: 1000.0 - gene_reaction_rule: b3916 or b1723 - objective_coefficient: 1.0 - annotation: !!omap - bigg.reaction: PFK - !!omap - id: PGI - name: glucose-6-phosphate isomerase - metabolites: !!omap - f6p_c: 1.0 - g6p_c: -1.0 - lower_bound: -1000.0 - upper_bound: 1000.0 - gene_reaction_rule: b4025 - annotation: !!omap - bigg.reaction: PGI - !!omap - id: PGK - name: phosphoglycerate kinase - metabolites: !!omap - 13dpg_c: 1.0 - 3pg_c: -1.0 - adp_c: 1.0 - atp_c: -1.0 - lower_bound: -1000.0 - upper_bound: 1000.0 - gene_reaction_rule: b2926 - annotation: !!omap - bigg.reaction: PGK - !!omap - id: PGM - name: phosphoglycerate mutase - metabolites: !!omap - 2pg_c: -1.0 - 3pg_c: 1.0 - lower_bound: -1000.0 - upper_bound: 1000.0 - gene_reaction_rule: b4395 or b3612 or b0755 - annotation: !!omap - bigg.reaction: PGM - !!omap - id: PIt2r - name: R phosphate reversible transport via - symport - metabolites: !!omap - h_c: 1.0 - h_e: -1.0 - pi_c: 1.0 - pi_e: -1.0 - lower_bound: -1000.0 - upper_bound: 1000.0 - gene_reaction_rule: b2987 or b3493 - annotation: !!omap - bigg.reaction: PIt2r - !!omap - id: PYK - name: pyruvate kinase - metabolites: !!omap - adp_c: -1.0 - atp_c: 1.0 - h_c: -1.0 - pep_c: -1.0 - pyr_c: 1.0 - lower_bound: 0.0 - upper_bound: 1000.0 - gene_reaction_rule: b1854 or b1676 - annotation: !!omap - bigg.reaction: PYK - !!omap - id: TPI - name: triose-phosphate isomerase - metabolites: !!omap - dhap_c: -1.0 - g3p_c: 1.0 - lower_bound: -1000.0 - upper_bound: 1000.0 - gene_reaction_rule: b3919 - annotation: !!omap - bigg.reaction: TPI - genes: - !!omap - id: b0755 - name: gpmA - !!omap - id: b0875 - name: aqpZ - !!omap - id: b1101 - name: ptsG - !!omap - id: b1380 - name: ldhA - !!omap - id: b1621 - name: malX - !!omap - id: b1676 - name: pykF - annotation: !!omap - ncbiprotein: - '1208453' - '1652654' - !!omap - id: b1723 - name: pfkB - !!omap - id: b1773 - name: ydjI - !!omap - id: b1779 - name: gapA - !!omap - id: b1817 - name: manX - !!omap - id: b1818 - name: manY - !!omap - id: b1819 - name: manZ - !!omap - id: b1854 - name: pykA - !!omap - id: b2097 - name: fbaB - !!omap - id: b2133 - name: dld - !!omap - id: b2415 - name: ptsH - !!omap - id: b2416 - name: ptsI - !!omap - id: b2417 - name: crr - !!omap - id: b2779 - name: eno - annotation: !!omap - ncbiprotein: '1653839' - !!omap - id: b2925 - name: fbaA - !!omap - id: b2926 - name: pgk - annotation: !!omap - ncbiprotein: '1653609' - !!omap - id: b2987 - name: pitB - !!omap - id: b3493 - name: pitA - !!omap - id: b3612 - name: gpmM - !!omap - id: b3916 - name: pfkA - annotation: !!omap - ncbiprotein: - '1006614' - '1651919' - !!omap - id: b3919 - name: tpiA - !!omap - id: b4025 - name: pgi - annotation: !!omap - ncbiprotein: '1653253' - !!omap - id: b4395 - name: ytjC - !!omap - id: s0001 - name: G_s0001 - id: mini_textbook - compartments: !!omap - c: cytosol - e: extracellular - version: '1.2' cobrapy-0.29.1/src/cobra/data/mini_cobra.xml000066400000000000000000003104041467301562300206650ustar00rootroot00000000000000 cobrapy-0.29.1/src/cobra/data/salmonella.xml.gz000066400000000000000000021413371467301562300213420ustar00rootroot00000000000000`]iYS1720.xml}is"Yi5:ˬoݗ `Y$!ԯT~" $Auf=̌*J)q-?kyX7j_z aX\Ov]-67?|Z5_O٧_Gߞ6>ODž~0Yu盻)DO~qx0~͗~n?~nɏ-f8`6/"O2%߿[-?7o7?~x/ݏ់t2Yt'q]f~~ 7q;ca>]. #4G3['?OsO?翏&o>.5}vݟO;V|?bN~nnz|eW_tM3\EMh3{w__/?⸩;WOouww_Aא<\n'In*U,?.-p' aqun{S8}E}:>]Wn7||\7ɟbads5~ˇ~ǝ<5^6ngFן?vxTLųOCt|~.Mv}on҇-4-9T,49Ҁj;9Kݯ @^y/Re?\ի[bιO$?A_~5~a|E_!>9׿&~_|wv?ߦ_-W7Ou?|z7~-r[fzLzwoG>Y-]8S~IWNS/j0xc*- Y;1xLen67KJ.?}zj7vktU0nXx$K/TLnOq[p~j&yrwnl%-GY1ɵ'&0^V9]ϒ]?]x&ޘWrol >l^_9)-ӧ%y'7˧|Z~8[6߯pOB~Z, zѷ0ʛgrX-@.ooʳ/~A5 *sJI-MF_Q]^Qb-eR*׷-efÿۚo>kE5U9}?uNƳ }SϚP^QW4'&TPa+j ]MoL=kB- uɫksbB-"RęɾI; +ksbC- U{GmwcgoVH&;25;ۈ <U}5qX0 [BA(Ta U|RXf'jT;WV;  1v;@zf5˦`؏ُΟKG''l?v{a0D(#.yohMjn((Ns&0 S7~3L/W͕°K%ixzV)$t}Z,vAs/gۧ+F:#]Qg4dά2mwH ΝւG2v#4ߺq-S ,ʅbܩ4 J^v۬&UN/DY9(3l#?ôOW[<3`i}HϨN*6}|lC2>_$&&' UȨA0F2h,lk 3لIℜP X!R,%ތ!n;"@qy" el5 >8coFSb@y}!@-e$&|1TQP˱7{fPaTO2kR}9 pK!KR35cY4#;fDiFŒ|F 0ʹ&b; Qk2$ ;tbaD"@Jx3x>CG*@@e_]mް*RB!P o 6I|.vԞ=5S{')U><3L{ Qn69 T9IZDVW(4WZbQ 5H4[2!e ڤo%|k0w%ʼ5z6EP> s$\_0n:%4` 7ЖXK*)<nl6*Vqacƚ)+4 3q4&s>xEpB.H:0%`<̤exYeIw;`5FInw&V@ݰZSQ8R&%y?ޥ:G?L'S q1W9$Cr:hI4>""ρXX3z0+E-v*p2xA2jCP-u%lp1TO%OP,/ ǮFݿjFDU Hq/:[`^d Hf8GKEf8D*2Vrqx̫oyʇ! *'qG9[$rֺ0>+.z6Q܂yBQ yXZk")='_l6yz/{-lX ̉ZCl:z2z]0Z'RUbүrLjWq<-D"Omtf|{FO n..&ڊp"AI @p 1c&{7esVc| {7bNYLW[V"2\HL(+|R z?/H4md]a~b>y$*>ľ7Fk!8,xp J«)_a FkžA5^oQpڢ(E LBHBf/10 ź'3IMJUp2nؓMumxk.B pQ5< ^{ 6RX?ĥxkA5 a<Xԁ.0*'TTdBMlbڋ$3v?sB&bRNǬ?alF]@# ҵoz ZN2ymLw%BF^}P羈T 9ۮq4d/F& xQj9xYK$o.),I ,KW*/bI0/- s[1crToa@ARNY}wJ69E,Ch̨] IxO /L pY.V+j0NjkzagǛO%j0 Argh+!sX=8WL*Y W{  ] (x|/Iwʴ#ZDw4Yp,YxjMx\[A =HuG$u@*7nc IfnNhOb"Vrz1ü_o/,B$J?A.PaP kJe,H#h :q+FW~Hbo-x%V+)lNSX0y&o10*xEX)Uxt21 L/y$v5Vc>gF^*\PGʥQ2c;7ǩ|r4۬~}"Ig#WOKixIzyzż]dׂHe9 ŠHM΋!|/K|O?2\#\<}.!OelVqf{ζBWjBZUVHr☀4̚ `juU"Z'6~:w>5g:]mPَ&M@+z%!ǡ7 &e89ds.v]췼J^cc4+!+uHRLl>(̒:O! fA|zȪh|"}9l>0qN5չ"@Ozyuz/ HyZLxX/fwçYYJ ]* Ӛ{yy?:DxFu0%t2G1lL.!e.%iVXOZ z7j߬vSiI|VV @񦔀c8@.﹀)HLz5v8rnM'I/)^4pPy *NO,aq'x7 `:{ }3G2۔0 jB38u5셄(RaC\*\C|5UB09Y𪳒kHip 9CR^#CB4Q \Bp`:˽@հ[iJ<,NPzRݰi 8Te?͜]S8ui (C˅9l kl)H MYah ,C p/r?t?{' (avP! +XtrO,y ?w_ލ~9y^XiHJl! 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Parameters ---------- model : cobra.Model The model to obtain growth for. Returns ------- float The obtained growth value. Returns nan if there is some error while optimizing. """ try: if "moma_old_objective" in model.solver.variables: model.slim_optimize() growth = model.solver.variables.moma_old_objective.primal else: growth = model.slim_optimize() except SolverError: growth = float("nan") return growth, model.solver.status def _reaction_deletion( model: Model, reaction_ids: List[str] ) -> Tuple[List[str], float, str]: """Perform reaction deletion. Parameters ---------- model : cobra.Model The model to perform reaction deletion on. ids : list of str The reaction IDs to knock-out. Returns ------- tuple of (list of str, float, str) A tuple containing reaction IDs knocked out, growth of the model and the solver status. """ with model: for rxn_id in reaction_ids: model.reactions.get_by_id(rxn_id).knock_out() growth, status = _get_growth(model) return reaction_ids, growth, status def _reaction_deletion_worker(ids: List[str]) -> Tuple[List[str], float, str]: """Perform reaction deletions on worker process. Parameters ---------- ids : list of str The reaction IDs to knock-out. Returns ------- tuple of (list of str, float, str) A tuple containing reaction IDs knocked out, growth of the model and the solver status. """ global _model return _reaction_deletion(_model, ids) def _gene_deletion(model: Model, gene_ids: List[str]) -> Tuple[List[str], float, str]: """Perform gene deletions. Parameters ---------- model : cobra.Model The model to perform gene deletion on. ids : list of str The gene IDs to knock-out. Returns ------- tuple of (list of str, float, str) A tuple containing gene IDs knocked out, growth of the model and the solver status. """ with model: for gene_id in gene_ids: model.genes.get_by_id(gene_id).knock_out() growth, status = _get_growth(model) return gene_ids, growth, status def _gene_deletion_worker(ids: List[str]) -> Tuple[List[str], float, str]: """Perform gene deletions on worker process. Parameters ---------- ids : list of str The gene IDs to knock-out. Returns ------- tuple of (list of str, float, str) A tuple containing gene IDs knocked out, growth of the model and the solver status. """ global _model return _gene_deletion(_model, ids) def _init_worker(model: Model) -> None: """Initialize worker process.""" global _model _model = model def _multi_deletion( model: Model, entity: str, element_lists: List[Union[Gene, Reaction]], method: str = "fba", solution: Optional["Solution"] = None, processes: Optional[int] = None, **kwargs, ) -> pd.DataFrame: """Provide a common interface for single or multiple knockouts. Parameters ---------- model : cobra.Model The metabolic model to perform deletions in. entity : {"gene", "reaction"} The entity to knockout. element_lists : list of cobra.Gene or cobra.Reaction List of cobra.Gene or cobra.Reaction to be deleted. method : {"fba", "moma", "linear moma", "room", "linear room"}, optional Method used to predict the growth rate (default "fba"). solution : cobra.Solution, optional A previous solution to use as a reference for (linear) MOMA or ROOM (default None). processes : int, optional The number of parallel processes to run. Can speed up the computations if the number of knockouts to perform is large. If not passed, will be set to `configuration.processes` (default None). **kwargs : Passed on to underlying simulation functions. Returns ------- pandas.DataFrame A representation of all combinations of entity deletions. The columns are 'growth' and 'status', where index : tuple(str) The gene or reaction identifiers that were knocked out. growth : float The growth rate of the adjusted model. status : str The solution's status. """ solver = sutil.interface_to_str(model.problem.__name__) if method == "moma" and solver not in sutil.qp_solvers: raise RuntimeError( f"Cannot use MOMA since '{solver}' is not QP-capable. " "Please choose a different solver or use FBA only." ) if processes is None: processes = configuration.processes with model: if "moma" in method: add_moma(model, solution=solution, linear="linear" in method) elif "room" in method: add_room(model, solution=solution, linear="linear" in method, **kwargs) args = {frozenset(comb) for comb in product(*element_lists)} processes = min(processes, len(args)) def extract_knockout_results(result_iter): result = pd.DataFrame( [ ( set(ids), growth, status, ) for (ids, growth, status) in result_iter ], columns=["ids", "growth", "status"], ) return result if processes > 1: worker = { "gene": _gene_deletion_worker, "reaction": _reaction_deletion_worker, }[entity] chunk_size = len(args) // processes with ProcessPool( processes, initializer=_init_worker, initargs=(model,) ) as pool: results = extract_knockout_results( pool.imap_unordered(worker, args, chunksize=chunk_size) ) else: worker = {"gene": _gene_deletion, "reaction": _reaction_deletion}[entity] results = extract_knockout_results(map(partial(worker, model), args)) return results def _entities_ids(entities: List[Union[str, Gene, Reaction]]) -> List[str]: """Return the IDs of the `entities`. Parameters ---------- entities : list of str or cobra.Gene or cobra.Reaction The list of entities whose IDs need to be returned. Returns ------- list of str The IDs of the `entities`. """ try: return [e.id for e in entities] except AttributeError: return list(entities) def _element_lists( entities: List[Union[str, Gene, Reaction]], *ids: List[str] ) -> List[str]: """Return the elements. Parameters ---------- entities : list of str or cobra.Gene or cobra.Reaction The list of entities. *ids : list of str The list of IDs. Returns ------- list of str The list of IDs. """ lists = list(ids) if lists[0] is None: lists[0] = entities result = [_entities_ids(lists[0])] for _list in lists[1:]: if _list is None: result.append(result[-1]) else: result.append(_entities_ids(_list)) return result def single_reaction_deletion( model: Model, reaction_list: Optional[List[Union[Reaction, str]]] = None, method: str = "fba", solution: Optional["Solution"] = None, processes: Optional[int] = None, **kwargs, ) -> pd.DataFrame: """Knock out each reaction from `reaction_list`. Parameters ---------- model : cobra.Model The metabolic model to perform deletions in. reaction_list : list of cobra.Reaction or str, optional The reactions be knocked out. If not passed, all the reactions from the model are used (default None). method: {"fba", "moma", "linear moma", "room", "linear room"}, optional Method used to predict the growth rate (default "fba"). solution : cobra.Solution, optional A previous solution to use as a reference for (linear) MOMA or ROOM (default None). processes : int, optional The number of parallel processes to run. Can speed up the computations if the number of knockouts to perform is large. If not passed, will be set to `configuration.processes` (default None). **kwargs : Keyword arguments are passed on to underlying simulation functions such as `add_room`. Returns ------- pandas.DataFrame A representation of all single reaction deletions. The columns are 'growth' and 'status', where index : tuple(str) The reaction identifier that was knocked out. growth : float The growth rate of the adjusted model. status : str The solution's status. """ return _multi_deletion( model, "reaction", element_lists=_element_lists(model.reactions, reaction_list), method=method, solution=solution, processes=processes, **kwargs, ) def single_gene_deletion( model: Model, gene_list: Optional[List[Union[Gene, str]]] = None, method: str = "fba", solution: Optional["Solution"] = None, processes: Optional[int] = None, **kwargs, ) -> pd.DataFrame: """Knock out each gene from `gene_list`. Parameters ---------- model : cobra.Model The metabolic model to perform deletions in. gene_list : list of cobra.Gene or str, optional The gene objects to be deleted. If not passed, all the genes from the model are used (default None). method : {"fba", "moma", "linear moma", "room", "linear room"}, optional Method used to predict the growth rate (default "fba"). solution : cobra.Solution, optional A previous solution to use as a reference for (linear) MOMA or ROOM (default None). processes : int, optional The number of parallel processes to run. Can speed up the computations if the number of knockouts to perform is large. If not passed, will be set to `configuration.processes` (default None). **kwargs : Keyword arguments are passed on to underlying simulation functions such as `add_room`. Returns ------- pandas.DataFrame A representation of all single gene deletions. The columns are 'growth' and 'status', where index : tuple(str) The gene identifier that was knocked out. growth : float The growth rate of the adjusted model. status : str The solution's status. """ return _multi_deletion( model, "gene", element_lists=_element_lists(model.genes, gene_list), method=method, solution=solution, processes=processes, **kwargs, ) def double_reaction_deletion( model: Model, reaction_list1: Optional[List[Union[Reaction, str]]] = None, reaction_list2: Optional[List[Union[Reaction, str]]] = None, method: str = "fba", solution: Optional["Solution"] = None, processes: Optional[int] = None, **kwargs, ) -> pd.DataFrame: """Knock out each reaction pair from the combinations of two given lists. We say 'pair' here but the order order does not matter. Parameters ---------- model : cobra.Model The metabolic model to perform deletions in. reaction_list1 : list of cobra.Reaction or str, optional The first reaction list to be deleted. If not passed, all the reactions from the model are used (default None). reaction_list2 : list of cobra.Reaction or str, optional The second reaction list to be deleted. If not passed, all the reactions from the model are used (default None). method: {"fba", "moma", "linear moma", "room", "linear room"}, optional Method used to predict the growth rate (default "fba"). solution : cobra.Solution, optional A previous solution to use as a reference for (linear) MOMA or ROOM (default None). processes : int, optional The number of parallel processes to run. Can speed up the computations if the number of knockouts to perform is large. If not passed, will be set to `configuration.processes` (default None). **kwargs : Keyword arguments are passed on to underlying simulation functions such as `add_room`. Returns ------- pandas.DataFrame A representation of all combinations of reaction deletions. The columns are 'growth' and 'status', where index : tuple(str) The reaction identifiers that were knocked out. growth : float The growth rate of the adjusted model. status : str The solution's status. """ reaction_list1, reaction_list2 = _element_lists( model.reactions, reaction_list1, reaction_list2 ) return _multi_deletion( model, "reaction", element_lists=[reaction_list1, reaction_list2], method=method, solution=solution, processes=processes, **kwargs, ) def double_gene_deletion( model: Model, gene_list1: Optional[List[Union[Gene, str]]] = None, gene_list2: Optional[List[Union[Gene, str]]] = None, method: str = "fba", solution: Optional["Solution"] = None, processes: Optional[int] = None, **kwargs, ) -> pd.DataFrame: """Knock out each gene pair from the combination of two given lists. We say 'pair' here but the order order does not matter. Parameters ---------- model : cobra.Model The metabolic model to perform deletions in. gene_list1 : list of cobra.Gene or str, optional The first gene list to be deleted. If not passed, all the genes from the model are used (default None). gene_list2 : list of cobra.Gene or str, optional The second gene list to be deleted. If not passed, all the genes from the model are used (default None). method : {"fba", "moma", "linear moma", "room", "linear room"}, optional Method used to predict the growth rate (default None). solution : cobra.Solution, optional A previous solution to use as a reference for (linear) MOMA or ROOM (default None). processes : int, optional The number of parallel processes to run. Can speed up the computations if the number of knockouts to perform is large. If not passed, will be set to `configuration.processes` (default None). **kwargs : Keyword arguments are passed on to underlying simulation functions such as `add_room`. Returns ------- pandas.DataFrame A representation of all combinations of gene deletions. The columns are 'growth' and 'status', where index : tuple(str) The gene identifiers that were knocked out. growth : float The growth rate of the adjusted model. status : str The solution's status. """ gene_list1, gene_list2 = _element_lists(model.genes, gene_list1, gene_list2) return _multi_deletion( model, "gene", element_lists=[gene_list1, gene_list2], method=method, solution=solution, processes=processes, **kwargs, ) @pd.api.extensions.register_dataframe_accessor("knockout") class KnockoutAccessor: """ Access unique combinations of reactions in deletion results. This allows acces in the form of `results.knockout[rxn1]` or `results.knockout["rxn1_id"]`. Each individual entry will return a deletion so `results.knockout[rxn1, rxn2]` will return two deletions (for individual knockouts of rxn1 and rxn2 respectively). Multi-deletions can be accessed by passing in sets like `results.knockout[{rxn1, rxn2}]` which denotes the double deletion of both reactions. Thus, the following are allowed index elements: - single reactions or genes (depending on whether it is a gene or reaction deletion) - single reaction IDs or gene IDs - lists of single single reaction IDs or gene IDs (will return one row for each element in the list) - sets of reactions or genes (for multi-deletions) - sets of reactions IDs or gene IDs - list of sets of objects or IDs (to get several multi-deletions) Parameters: ----------- pandas_obj : pandas.DataFrame or pandas.Series A result from one of the deletion methods. """ def __init__(self, pandas_obj: Union[pd.DataFrame, pd.Series]) -> None: """Set up the accessor.""" self._validate(pandas_obj) self._result = pandas_obj @staticmethod def _validate(obj: pd.DataFrame) -> None: """Validate the object given. Parameters ---------- obj : pandas.DataFrame The object to validate. Raises ------ AttributeError If the object supplied is not a DataFrame. """ # verify it is a deletion results if any(name not in obj.columns for name in ["ids", "growth", "status"]): raise AttributeError("Must be DataFrame returned by a deletion method.") def __getitem__( self, args: Union[ Gene, List[Gene], Set[Gene], List[Set[Gene]], Reaction, List[Reaction], Set[Reaction], List[Set[Reaction]], str, List[str], Set[str], List[Set[str]], ], ) -> pd.DataFrame: """Return the deletion result for a particular set of knocked entities. Parameters ---------- args : cobra.Reaction, cobra.Gene, str, set, or list The deletions to be returned. Accepts: - single reactions or genes - single reaction IDs or gene IDs - lists of single single reaction IDs or gene IDs - sets of reactions or genes - sets of reactions IDs or gene IDs - list of sets of objects or IDs See the docs for usage examples. Returns ------- pandas.DataFrame The deletion result where the chosen entities have been deleted. Each row denotes a deletion. Raises ------ ValueError If any other object is used as index for lookup. """ if not any(isinstance(args, t) for t in [tuple, list]): args = [args] if any(isinstance(args[0], t) for t in [Reaction, Gene, str]): try: args = [{obj.id} for obj in args] except AttributeError: # are already strings args = [{obj} for obj in args] elif isinstance(args[0], set): try: args = [{elem.id for elem in obj} for obj in args] except AttributeError: args = [set(obj) for obj in args] else: raise ValueError( "Allowed indices are single cobra.Reaction or cobra.Gene, " "lists of cobra.Reaction of cobra.Gene, or lists of sets " "of cobra.Reaction or cobra.Gene." ) found = [x in args for x in self._result.ids] return self._result[found] cobrapy-0.29.1/src/cobra/flux_analysis/fastcc.py000066400000000000000000000133661467301562300216350ustar00rootroot00000000000000"""Provide an implementation of FASTCC.""" from typing import TYPE_CHECKING, List, Optional from optlang.symbolics import Zero from .helpers import normalize_cutoff if TYPE_CHECKING: from cobra.core import Model, Reaction def _find_sparse_mode( model: "Model", rxns: List["Reaction"], flux_threshold: float, zero_cutoff: float ) -> List["Reaction"]: """Perform the LP required for FASTCC. Parameters ---------- model: cobra.Model The model to perform FASTCC on. rxns: list of cobra.Reaction The reactions to use for LP. flux_threshold: float The upper threshold an auxiliary variable can have. zero_cutoff: float The cutoff below which flux is considered zero. Returns ------- list of cobra.Reaction The list of reactions to consider as consistent. """ if rxns: obj_vars = [] vars_and_cons = [] prob = model.problem for rxn in rxns: var = prob.Variable( "auxiliary_{}".format(rxn.id), lb=0.0, ub=flux_threshold ) const = prob.Constraint( rxn.forward_variable + rxn.reverse_variable - var, name="constraint_{}".format(rxn.id), lb=0.0, ) vars_and_cons.extend([var, const]) obj_vars.append(var) model.add_cons_vars(vars_and_cons) model.objective = prob.Objective(Zero, sloppy=True) model.objective.set_linear_coefficients({v: 1.0 for v in obj_vars}) model.optimize(objective_sense="max") result = [rxn for rxn in model.reactions if abs(rxn.flux) > zero_cutoff] else: result = [] return result def _flip_coefficients(model: "Model", rxns: List["Reaction"]) -> None: """Flip the coefficients for optimizing in reverse direction. Parameters ---------- model: cobra.Model The model to operate on. rxns: list of cobra.Reaction The list of reactions whose coefficients will be flipped. """ # flip reactions for rxn in rxns: const = model.constraints.get("constraint_{}".format(rxn.id)) var = model.variables.get("auxiliary_{}".format(rxn.id)) coefs = const.get_linear_coefficients(const.variables) const.set_linear_coefficients({k: -v for k, v in coefs.items() if k is not var}) # flip objective objective = model.objective objective_coefs = objective.get_linear_coefficients(objective.variables) objective.set_linear_coefficients({k: -v for k, v in objective_coefs.items()}) def fastcc( model: "Model", flux_threshold: float = 1.0, zero_cutoff: Optional[float] = None ) -> "Model": r""" Check consistency of a metabolic network using FASTCC [1]_. FASTCC (Fast Consistency Check) is an algorithm for rapid and efficient consistency check in metabolic networks. FASTCC is a pure LP implementation and is low on computation resource demand. FASTCC also circumvents the problem associated with reversible reactions for the purpose. Given a global model, it will generate a consistent global model i.e., remove blocked reactions. For more details on FASTCC, please check [1]_. Parameters ---------- model: cobra.Model The model to operate on. flux_threshold: float, optional The flux threshold to consider (default 1.0). zero_cutoff: float, optional The cutoff to consider for zero flux (default model.tolerance). Returns ------- cobra.Model The consistent model. Notes ----- The LP used for FASTCC is like so: maximize: \sum_{i \in J} z_i s.t. : z_i \in [0, \varepsilon] \forall i \in J, z_i \in \mathbb{R}_+ v_i \ge z_i \forall i \in J Sv = 0 v \in B References ---------- .. [1] Vlassis N, Pacheco MP, Sauter T (2014) Fast Reconstruction of Compact Context-Specific Metabolic Network Models. PLoS Comput Biol 10(1): e1003424. doi:10.1371/journal.pcbi.1003424 """ zero_cutoff = normalize_cutoff(model, zero_cutoff) irreversible_rxns = [rxn for rxn in model.reactions if not rxn.reversibility] rxns_to_check = irreversible_rxns with model: rxns_to_keep = _find_sparse_mode( model, rxns_to_check, flux_threshold, zero_cutoff ) rxns_to_check = list(set(model.reactions).difference(rxns_to_keep)) while rxns_to_check: with model: new_rxns = _find_sparse_mode( model, rxns_to_check, flux_threshold, zero_cutoff ) rxns_to_keep.extend(new_rxns) # this condition will be valid for all but the last iteration if list(set(rxns_to_check).intersection(rxns_to_keep)): rxns_to_check = list(set(rxns_to_check).difference(rxns_to_keep)) else: rxns_to_flip = list(set(rxns_to_check).difference(irreversible_rxns)) _flip_coefficients(model, rxns_to_flip) sol = model.optimize(min) to_add_rxns = sol.fluxes.index[sol.fluxes.abs() > zero_cutoff].tolist() rxns_to_keep.extend( [model.reactions.get_by_id(rxn) for rxn in to_add_rxns] ) # since this is the last iteration, it needs to break or else # it will run forever since rxns_to_check won't be empty break consistent_rxns = set(rxns_to_keep) # need the ids since Reaction objects are created fresh with model.copy() rxns_to_remove = [ rxn.id for rxn in set(model.reactions).difference(consistent_rxns) ] consistent_model = model.copy() consistent_model.remove_reactions(rxns_to_remove, remove_orphans=True) return consistent_model cobrapy-0.29.1/src/cobra/flux_analysis/gapfilling.py000066400000000000000000000365121467301562300225040ustar00rootroot00000000000000"""Provide the base class and utility function for gap filling.""" import logging from typing import TYPE_CHECKING, Dict, List, Optional, Union from optlang.interface import OPTIMAL from optlang.symbolics import Zero from ..core import Model from ..util import fix_objective_as_constraint, interface_to_str if TYPE_CHECKING: from cobra import Reaction logger = logging.getLogger(__name__) class GapFiller: r""" The base class for performing gap filling. This class implements gap filling based on a mixed-integer approach, very similar to that described in [1]_ and the 'no-growth but growth' part of [2]_ but with minor adjustments. In short, we add indicator variables for using the reactions in the universal model, z_i and then solve problem minimize: \sum_i c_i * z_i s.t. : Sv = 0 v_o \ge t lb_i \le v_i \le ub_i v_i = 0 if z_i = 0 where lb, ub are respectively the upper, lower flux bounds for reaction i, c_i is a cost parameter and the objective v_o is greater than the lower bound t. The default costs are 1 for reactions from the universal model, 100 for exchange (uptake) reactions added and 1 for added demand reactions. Note that this is a mixed-integer linear program and as such will be expensive to solve for large models. Consider using alternatives [3]_ such as CORDA instead [4,5]_ . Parameters ---------- model : cobra.Model The model to perform gap filling on. universal : cobra.Model, optional A universal model with reactions that can be used to complete the `model` (default None). lower_bound : float, optional The minimally accepted flux for the objective in the filled model (default 0.05). penalties : dict, optional A dictionary with keys being 'universal' (all reactions included in the universal model), 'exchange' and 'demand' (all additionally added exchange and demand reactions) for the three reaction types. Can also have reaction identifiers for reaction specific costs. Defaults are 1, 100 and 1 respectively (default None). exchange_reactions : bool, optional Consider adding exchange (uptake) reactions for all metabolites in the model (default False). demand_reactions : bool, optional Consider adding demand reactions for all metabolites (default True). integer_threshold : float, optional The threshold at which a value is considered non-zero (aka integrality threshold). If gapfilled models fail to validate, you may want to lower this value (default 1E-6). Attributes ---------- indicators: list of optlang.interface.Variable The list of symbolic indicator variables. costs: dict of {optlang.interface.Variable: float} The dictionary with symbolic variables as keys and their cost as values. References ---------- .. [1] Reed, Jennifer L., Trina R. Patel, Keri H. Chen, Andrew R. Joyce, Margaret K. Applebee, Christopher D. Herring, Olivia T. Bui, Eric M. Knight, Stephen S. Fong, and Bernhard O. Palsson. “Systems Approach to Refining Genome Annotation.” Proceedings of the National Academy of Sciences 103, no. 46 (2006): 17480–17484. .. [2] Kumar, Vinay Satish, and Costas D. Maranas. “GrowMatch: An Automated Method for Reconciling In Silico/In Vivo Growth Predictions.” Edited by Christos A. Ouzounis. PLoS Computational Biology 5, no. 3 (March 13, 2009): e1000308. doi:10.1371/journal.pcbi.1000308. .. [3] http://opencobra.github.io/cobrapy/tags/gapfilling/ .. [4] Schultz, André, and Amina A. Qutub. “Reconstruction of Tissue-Specific Metabolic Networks Using CORDA.” Edited by Costas D. Maranas. PLOS Computational Biology 12, no. 3 (March 4, 2016): e1004808. doi:10.1371/journal.pcbi.1004808. .. [5] Diener, Christian https://github.com/cdiener/corda """ def __init__( self, model: Model, universal: Optional[Model] = None, lower_bound: float = 0.05, penalties: Optional[Union[Dict[str, int], Dict["Reaction", int]]] = None, exchange_reactions: bool = False, demand_reactions: bool = True, integer_threshold: float = 1e-6, **kwargs, ) -> None: """Initialize a new GapFiller object. Other Parameters ---------------- kwargs : Further keyword arguments are passed on to the parent class. """ self.original_model = model self.lower_bound = lower_bound self.model = model.copy() tolerances = self.model.solver.configuration.tolerances try: tolerances.integrality = integer_threshold except AttributeError: logger.warning( f"The current solver interface {interface_to_str(self.model.problem)} " f"doesn't support setting the integrality tolerance." ) # TODO (Midnighter): One could debate how useful it is to compare against this # threshold when it is not supported by the chosen solver. self.integer_threshold = integer_threshold self.universal = universal.copy() if universal else Model("universal") self.penalties = {"universal": 1, "exchange": 100, "demand": 1} if penalties is not None: self.penalties.update(penalties) self.indicators = [] self.costs = {} self.extend_model(exchange_reactions, demand_reactions) fix_objective_as_constraint(self.model, bound=lower_bound) self.add_switches_and_objective() def extend_model( self, exchange_reactions: bool = False, demand_reactions: bool = True ) -> None: """Extend gap filling model. Add reactions from universal model and optionally exchange and demand reactions for all metabolites in the model to perform gap filling on. Parameters ---------- exchange_reactions : bool, optional Consider adding exchange (uptake) reactions for all metabolites in the model (default False). demand_reactions : bool, optional Consider adding demand reactions for all metabolites (default True). """ for rxn in self.universal.reactions: rxn.gapfilling_type = "universal" new_metabolites = self.universal.metabolites.query( lambda metabolite: metabolite not in self.model.metabolites ) self.model.add_metabolites(new_metabolites) existing_exchanges = [] for rxn in self.universal.boundary: existing_exchanges = existing_exchanges + [ met.id for met in list(rxn.metabolites) ] for met in self.model.metabolites: if exchange_reactions: # check for exchange reaction in model already if met.id not in existing_exchanges: rxn = self.universal.add_boundary( met, type="exchange_smiley", lb=-1000, ub=0, reaction_id=f"EX_{met.id}", ) rxn.gapfilling_type = "exchange" if demand_reactions: rxn = self.universal.add_boundary( met, type="demand_smiley", lb=0, ub=1000, reaction_id=f"DM_{met.id}", ) rxn.gapfilling_type = "demand" new_reactions = self.universal.reactions.query( lambda reaction: reaction not in self.model.reactions ) self.model.add_reactions(new_reactions) def update_costs(self) -> None: """Update coefficients for the indicator variables in the objective. Done incrementally so that second time the function is called, active indicators in the current solutions gets higher cost than the unused indicators. """ for var in self.indicators: if var not in self.costs: self.costs[var] = var.cost else: if var._get_primal() > self.integer_threshold: self.costs[var] += var.cost self.model.objective.set_linear_coefficients(self.costs) def add_switches_and_objective(self) -> None: """Update gap filling model with switches and indicator objective.""" constraints = [] big_m = max(max(abs(b) for b in r.bounds) for r in self.model.reactions) prob = self.model.problem for rxn in self.model.reactions: if not hasattr(rxn, "gapfilling_type"): continue indicator = prob.Variable( name=f"indicator_{rxn.id}", lb=0, ub=1, type="binary" ) if rxn.id in self.penalties: indicator.cost = self.penalties[rxn.id] else: indicator.cost = self.penalties[rxn.gapfilling_type] indicator.rxn_id = rxn.id self.indicators.append(indicator) # if z = 1 v_i is allowed non-zero # v_i - Mz <= 0 and v_i + Mz >= 0 constraint_lb = prob.Constraint( rxn.flux_expression - big_m * indicator, ub=0, name=f"constraint_lb_{rxn.id}", sloppy=True, ) constraint_ub = prob.Constraint( rxn.flux_expression + big_m * indicator, lb=0, name=f"constraint_ub_{rxn.id}", sloppy=True, ) constraints.extend([constraint_lb, constraint_ub]) self.model.add_cons_vars(self.indicators) self.model.add_cons_vars(constraints, sloppy=True) self.model.objective = prob.Objective(Zero, direction="min", sloppy=True) self.model.objective.set_linear_coefficients({i: 1 for i in self.indicators}) self.update_costs() def fill(self, iterations: int = 1) -> List[List["Reaction"]]: """Perform the gap filling. With every iteration, it solves the model, updates the costs and records the used reactions. Parameters ---------- iterations : int, optional The number of rounds of gap filling to perform. For every iteration, the penalty for every used reaction increases linearly. This way, the algorithm is encouraged to search for alternative solutions which may include previously used reactions i.e., with enough iterations pathways including 10 steps will eventually be reported even if the shortest pathway is a single reaction (default 1). Returns ------- list of list of cobra.Reaction A list of lists where each element is a list of reactions that were used to gap fill the model. Raises ------ RuntimeError If the model fails to be validated (i.e. the original model with the proposed reactions added, still cannot get the required flux through the objective). """ used_reactions = [] for _ in range(iterations): self.model.slim_optimize( error_value=None, message="gap filling optimization failed" ) solution = [ self.model.reactions.get_by_id(ind.rxn_id) for ind in self.indicators if ind._get_primal() > self.integer_threshold ] if not self.validate(solution): raise RuntimeError( "Failed to validate gap filled model, " "try lowering the integer threshold." ) used_reactions.append(solution) self.update_costs() return used_reactions def validate(self, reactions: List["Reaction"]) -> bool: """Validate the model. Parameters ---------- reactions: list of cobra.Reaction The reactions to add to the model for validation. Returns ------- bool Whether the model is valid or not. """ with self.original_model as model: mets = [x.metabolites for x in reactions] all_keys = set().union(*(d.keys() for d in mets)) model.add_metabolites(all_keys) model.add_reactions(reactions) model.slim_optimize() return ( model.solver.status == OPTIMAL and model.solver.objective.value >= self.lower_bound ) def gapfill( model: Model, universal: Optional[Model] = None, lower_bound: float = 0.05, penalties: Optional[Dict[str, "Reaction"]] = None, demand_reactions: bool = True, exchange_reactions: bool = False, iterations: int = 1, ): """Perform gap filling on a model. Parameters ---------- model : cobra.Model The model to perform gap filling on. universal : cobra.Model, optional A universal model with reactions that can be used to complete the model. Only gapfill considering demand and exchange reactions if left missing (default None). lower_bound : float, optional The minimally accepted flux for the objective in the filled model. (default 0.05). penalties : dict, optional A dictionary with keys being 'universal' (all reactions included in the universal model), 'exchange' and 'demand' (all additionally added exchange and demand reactions) for the three reaction types. Can also have reaction identifiers for reaction specific costs. Defaults are 1, 100 and 1 respectively (default None). exchange_reactions : bool, optional Consider adding exchange (uptake) reactions for all metabolites in the model (default False). demand_reactions : bool, optional Consider adding demand reactions for all metabolites (default True). iterations : int, optional The number of rounds of gap filling to perform. For every iteration, the penalty for every used reaction increases linearly. This way, the algorithm is encouraged to search for alternative solutions which may include previously used reactions i.e., with enough iterations pathways including 10 steps will eventually be reported even if the shortest pathway is a single reaction (default 1). Returns ------- list of list of cobra.Reaction A list of lists with on set of reactions that completes the model per requested iteration. Examples -------- >>> from cobra.io import load_model >>> from cobra import Model >>> from cobra.flux_analysis import gapfill >>> model = load_model("iYS1720") >>> universal = Model("universal") >>> universal.add_reactions([model.reactions.GF6PTA.copy()]) >>> model.remove_reactions([model.reactions.GF6PTA]) >>> gapfill(model, universal) [[]] """ gapfiller = GapFiller( model, universal=universal, lower_bound=lower_bound, penalties=penalties, demand_reactions=demand_reactions, exchange_reactions=exchange_reactions, ) return gapfiller.fill(iterations=iterations) cobrapy-0.29.1/src/cobra/flux_analysis/geometric.py000066400000000000000000000112321467301562300223360ustar00rootroot00000000000000"""Provide an implementation of geometric FBA.""" import logging from typing import TYPE_CHECKING, Optional from optlang.symbolics import Zero from .parsimonious import add_pfba from .variability import flux_variability_analysis logger = logging.getLogger(__name__) if TYPE_CHECKING: from cobra import Model, Solution def geometric_fba( model: "Model", epsilon: float = 1e-06, max_tries: int = 200, processes: Optional[int] = None, ) -> "Solution": """Perform geometric FBA to obtain a unique, centered flux distribution. Geometric FBA [1]_ formulates the problem as a polyhedron and then solves it by bounding the convex hull of the polyhedron. The bounding forms a box around the convex hull which reduces with every iteration and extracts a unique solution in this way. Parameters ---------- model: cobra.Model The model to perform geometric FBA on. epsilon: float, optional The convergence tolerance of the model (default 1E-06). max_tries: int, optional Maximum number of iterations (default 200). processes : int, optional The number of parallel processes to run. If not explicitly passed, will be set from the global configuration singleton (default None). Returns ------- cobra.Solution The solution object containing all the constraints required for geometric FBA. Raises ------ RuntimeError If iteration count becomes equal to `max_tries`. References ---------- .. [1] Smallbone, Kieran & Simeonidis, Vangelis. (2009). Flux balance analysis: A geometric perspective. Journal of theoretical biology.258. 311-5. 10.1016/j.jtbi.2009.01.027. """ with model: # Variables' and constraints' storage variables. consts = [] obj_vars = [] updating_vars_cons = [] # The first iteration. prob = model.problem add_pfba(model) # Minimize the solution space to a convex hull. model.optimize() fva_sol = flux_variability_analysis(model, processes=processes) mean_flux = (fva_sol["maximum"] + fva_sol["minimum"]).abs() / 2 # Set the gFBA constraints. for rxn in model.reactions: var = prob.Variable("geometric_fba_" + rxn.id, lb=0, ub=mean_flux[rxn.id]) upper_const = prob.Constraint( rxn.flux_expression - var, ub=mean_flux[rxn.id], name="geometric_fba_upper_const_" + rxn.id, ) lower_const = prob.Constraint( rxn.flux_expression + var, lb=fva_sol.at[rxn.id, "minimum"], name="geometric_fba_lower_const_" + rxn.id, ) updating_vars_cons.append((rxn.id, var, upper_const, lower_const)) consts.extend([var, upper_const, lower_const]) obj_vars.append(var) model.add_cons_vars(consts) # Minimize the distance between the flux distribution and center. model.objective = prob.Objective(Zero, sloppy=True, direction="min") model.objective.set_linear_coefficients({v: 1.0 for v in obj_vars}) # Update loop variables. sol = model.optimize() fva_sol = flux_variability_analysis(model, processes=processes) mean_flux = (fva_sol["maximum"] + fva_sol["minimum"]).abs() / 2 delta = (fva_sol["maximum"] - fva_sol["minimum"]).max() count = 1 logger.debug(f"Iteration: {count}; delta: {delta}; status: {sol.status}.") # Following iterations that minimize the distance below threshold. while delta > epsilon and count < max_tries: for rxn_id, var, u_c, l_c in updating_vars_cons: var.ub = mean_flux[rxn_id] u_c.ub = mean_flux[rxn_id] l_c.lb = fva_sol.at[rxn_id, "minimum"] # Update loop variables. sol = model.optimize() fva_sol = flux_variability_analysis(model, processes=processes) mean_flux = (fva_sol["maximum"] + fva_sol["minimum"]).abs() / 2 delta = (fva_sol["maximum"] - fva_sol["minimum"]).max() count += 1 logger.debug(f"Iteration: {count}; delta: {delta}; status: {sol.status}.") if count == max_tries: raise RuntimeError( "The iterations have exceeded the maximum value of " f"{max_tries}. This is probably due to the increased " "complexity of the model and can lead to inaccurate " "results. Please set a different convergence tolerance " "and/or increase the maximum iterations." ) return sol cobrapy-0.29.1/src/cobra/flux_analysis/helpers.py000066400000000000000000000017651467301562300220340ustar00rootroot00000000000000"""Helper functions for all flux analysis methods.""" from typing import TYPE_CHECKING, Optional if TYPE_CHECKING: from cobra import Model def normalize_cutoff(model: "Model", zero_cutoff: Optional[float] = None) -> float: """Return a valid zero cutoff value. Parameters ---------- model : cobra.Model The model to operate on. zero_cutoff : positive float, optional The zero cutoff value. If not specified, defaults to `model.tolerance` (default None). Returns ------- float The normalized zero cutoff value. Raises ------ ValueError If the specified `zero_cutoff` is lesser than `model.tolerance`. """ if zero_cutoff is None: return model.tolerance else: if zero_cutoff < model.tolerance: raise ValueError( "The chosen zero cutoff cannot be less than the model's " "tolerance value." ) else: return zero_cutoff cobrapy-0.29.1/src/cobra/flux_analysis/loopless.py000066400000000000000000000243521467301562300222270ustar00rootroot00000000000000"""Provide functions to remove thermodynamically infeasible loops.""" from typing import TYPE_CHECKING, Dict, Optional, Union import numpy as np from optlang.symbolics import Zero from ..core import get_solution from ..util import create_stoichiometric_matrix, nullspace from .helpers import normalize_cutoff if TYPE_CHECKING: from cobra import Model, Reaction, Solution def add_loopless(model: "Model", zero_cutoff: Optional[float] = None) -> None: """Modify a model so all feasible flux distributions are loopless. It adds variables and constraints to a model which will disallow flux distributions with loops. The used formulation is described in [1]_. This function *will* modify your model. In most cases you probably want to use the much faster `loopless_solution`. May be used in cases where you want to add complex constraints and objecives (for instance quadratic objectives) to the model afterwards or use an approximation of Gibbs free energy directions in your model. Parameters ---------- model : cobra.Model The model to which to add the constraints. zero_cutoff : positive float, optional Cutoff used for null space. Coefficients with an absolute value smaller than `zero_cutoff` are considered to be zero. The default uses the `model.tolerance` (default None). References ---------- .. [1] Elimination of thermodynamically infeasible loops in steady-state metabolic models. Schellenberger J, Lewis NE, Palsson BO. Biophys J. 2011 Feb 2;100(3):544-53. doi: 10.1016/j.bpj.2010.12.3707. Erratum in: Biophys J. 2011 Mar 2;100(5):1381. """ zero_cutoff = normalize_cutoff(model, zero_cutoff) internal = [i for i, r in enumerate(model.reactions) if not r.boundary] s_int = create_stoichiometric_matrix(model)[:, np.array(internal)] n_int = nullspace(s_int).T max_bound = max(max(abs(b) for b in r.bounds) for r in model.reactions) prob = model.problem # Add indicator variables and new constraints to_add = [] for i in internal: rxn = model.reactions[i] # indicator variable a_i indicator = prob.Variable(f"indicator_{rxn.id}", type="binary") # -M*(1 - a_i) <= v_i <= M*a_i on_off_constraint = prob.Constraint( rxn.flux_expression - max_bound * indicator, lb=-max_bound, ub=0, name=f"on_off_{rxn.id}", ) # -(max_bound + 1) * a_i + 1 <= G_i <= -(max_bound + 1) * a_i + 1000 delta_g = prob.Variable(f"delta_g_{rxn.id}") delta_g_range = prob.Constraint( delta_g + (max_bound + 1) * indicator, lb=1, ub=max_bound, name=f"delta_g_range_{rxn.id}", ) to_add.extend([indicator, on_off_constraint, delta_g, delta_g_range]) model.add_cons_vars(to_add) # Add nullspace constraints for G_i for i, row in enumerate(n_int): name = f"nullspace_constraint_{str(i)}" nullspace_constraint = prob.Constraint(Zero, lb=0, ub=0, name=name) model.add_cons_vars([nullspace_constraint]) coefs = { model.variables[f"delta_g_{model.reactions[ridx].id}"]: row[i] for i, ridx in enumerate(internal) if abs(row[i]) > zero_cutoff } model.constraints[name].set_linear_coefficients(coefs) def _add_cycle_free(model: "Model", fluxes: Dict[str, float]) -> None: """Add constraints for CycleFreeFlux. Parameters ---------- model : cobra.Model The model to operate on. fluxes : dict of {str: float} A dictionary having keys as reaction IDs and values as their flux values. """ model.objective = model.solver.interface.Objective( Zero, direction="min", sloppy=True ) objective_vars = [] for rxn in model.reactions: flux = fluxes[rxn.id] if rxn.boundary: rxn.bounds = (flux, flux) continue if flux >= 0: rxn.bounds = max(0, rxn.lower_bound), min(flux, rxn.upper_bound) objective_vars.append(rxn.forward_variable) else: rxn.bounds = max(flux, rxn.lower_bound), min(0, rxn.upper_bound) objective_vars.append(rxn.reverse_variable) model.objective.set_linear_coefficients({v: 1.0 for v in objective_vars}) def loopless_solution( model: "Model", fluxes: Optional[Dict[str, float]] = None ) -> "Solution": """Convert an existing solution to a loopless one. Removes as many loops as possible (see Notes). Uses the method from CycleFreeFlux [1]_ and is much faster than `add_loopless` and should therefore be the preferred option to get loopless flux distributions. Parameters ---------- model : cobra.Model The model to which to add the constraints. fluxes : dict of {str, float}, optional A dictionary having keys as reaction IDs and values as their flux values. If not None will use the provided flux values to obtain a close loopless solution (default None). Returns ------- cobra.Solution A solution object containing the fluxes with the least amount of loops possible or None if the optimization failed (usually happening if the flux distribution in `fluxes` is infeasible). Notes ----- The returned flux solution has the following properties: - It contains the minimal number of loops possible and no loops at all if all flux bounds include zero and the objective is not in a cycle. - It has the same objective value as the original flux solution and assumes that the objective does not participate in a cycle (which is usually true since it consumes metabolites). - It has the same exact exchange fluxes as the previous solution. - All fluxes have the same sign (flow in the same direction) as the previous solution. When providing fluxes to the method, please note that those have to come from the exact same model that you provided, meaning that no bounds or coefficients have been changed, and the optimum has remained the same. References ---------- .. [1] CycleFreeFlux: efficient removal of thermodynamically infeasible loops from flux distributions. Desouki AA, Jarre F, Gelius-Dietrich G, Lercher MJ. Bioinformatics. 2015 Jul 1;31(13):2159-65. doi: 10.1093/bioinformatics/btv096. """ # Need to reoptimize otherwise spurious solution artifacts can cause # all kinds of havoc # TODO: check solution status if fluxes is None: sol = model.optimize(objective_sense=None) fluxes = sol.fluxes opt = sol.objective_value else: opt = model.slim_optimize() with model: prob = model.problem # Fix the objective loopless_obj_constraint = prob.Constraint( model.objective.expression, lb=opt, name="loopless_obj_constraint", ) model.add_cons_vars([loopless_obj_constraint]) _add_cycle_free(model, fluxes) solution = model.optimize(objective_sense=None) solution.objective_value = loopless_obj_constraint.primal return solution def loopless_fva_iter( model: "Model", reaction: "Reaction", solution: bool = False, zero_cutoff: Optional[float] = None, ) -> Union[float, Dict[str, float]]: """Plugin to get a loopless FVA solution from single FVA iteration. Assumes the following about `model` and `reaction`: 1. The model objective is set to be `reaction`. 2. The model has been optimized and contains the minimum/maximum flux for `reaction`. 3. The model contains an auxiliary variable called "fva_old_objective" denoting the previous objective. Parameters ---------- model : cobra.Model The model to be used. reaction : cobra.Reaction The reaction currently minimized/maximized. solution : bool, optional Whether to return the entire solution or only the minimum/maximum for `reaction` (default False). zero_cutoff : positive float, optional Cutoff used for loop removal. Fluxes with an absolute value smaller than `zero_cutoff` are considered to be zero. The default is to use `model.tolerance` (default None). Returns ------- single float or dict of {str: float} Returns the minimized/maximized flux through `reaction` if `solution` is False. Otherwise, returns a loopless flux solution object containing the minimum/maximum flux for `reaction`. """ zero_cutoff = normalize_cutoff(model, zero_cutoff) current = model.objective.value sol = get_solution(model) objective_dir = model.objective.direction # Handle a suddenly infeasible solution, # usually due to numerical instability if current is None: return None # boundary reactions can not be part of cycles if reaction.boundary: if solution: return sol else: return current with model: _add_cycle_free(model, sol.fluxes) model.slim_optimize() # If the previous optimum is maintained in the loopless solution it was # loopless and we are done if abs(reaction.flux - current) < zero_cutoff: if solution: return sol return current # If previous optimum was not in the loopless solution create a new # almost loopless solution containing only loops including the current # reaction. Than remove all of those loops. ll_sol = get_solution(model).fluxes reaction.bounds = (current, current) model.slim_optimize() almost_ll_sol = get_solution(model).fluxes with model: # find the reactions with loops using the current reaction and remove # the loops for rxn in model.reactions: rid = rxn.id if (abs(ll_sol[rid]) < zero_cutoff) and ( abs(almost_ll_sol[rid]) > zero_cutoff ): rxn.bounds = max(0, rxn.lower_bound), min(0, rxn.upper_bound) if solution: best = model.optimize() else: model.slim_optimize() best = reaction.flux model.objective.direction = objective_dir return best cobrapy-0.29.1/src/cobra/flux_analysis/moma.py000066400000000000000000000134441467301562300213200ustar00rootroot00000000000000"""Provide minimization of metabolic adjustment (MOMA).""" from typing import TYPE_CHECKING, Optional from optlang.symbolics import Zero, add from ..util import solver as sutil from .parsimonious import pfba if TYPE_CHECKING: from cobra.core import Model, Solution def moma( model: "Model", solution: Optional["Solution"] = None, linear: bool = True ) -> "Solution": """Compute a single solution based on (linear) MOMA. Compute a new flux distribution that is at a minimal distance to a previous reference solution `solution`. Minimization of metabolic adjustment (MOMA) is generally used to assess the impact of knock-outs. Thus, the typical usage is to provide a wild-type flux distribution as reference and a model in knock-out state. Parameters ---------- model : cobra.Model The model state to compute a MOMA-based solution for. solution : cobra.Solution, optional A (wild-type) reference solution (default None). linear : bool, optional Whether to use the linear MOMA formulation or not (default True). Returns ------- cobra.Solution A flux distribution that is at a minimal distance compared to the reference solution. See Also -------- add_moma : add MOMA constraints and objective """ with model: add_moma(model=model, solution=solution, linear=linear) solution = model.optimize() return solution def add_moma( model: "Model", solution: Optional["Solution"] = None, linear: bool = True ) -> None: r""" Add MOMA constraints and objective representing to the `model`. This adds variables and constraints for the minimization of metabolic adjustment (MOMA) to the model. Parameters ---------- model : cobra.Model The model to add MOMA constraints and objective to. solution : cobra.Solution, optional A previous solution to use as a reference. If no solution is given, one will be computed using pFBA (default None). linear : bool, optional Whether to use the linear MOMA formulation or not (default True). Notes ----- In the original MOMA [1]_ specification, one looks for the flux distribution of the deletion (v^d) closest to the fluxes without the deletion (v). In math this means: minimize: \sum_i (v^d_i - v_i)^2 s.t. : Sv^d = 0 lb_i \le v^d_i \le ub_i Here, we use a variable transformation v^t := v^d_i - v_i. Substituting and using the fact that Sv = 0 gives: minimize: \sum_i (v^t_i)^2 s.t. : Sv^d = 0 v^t = v^d_i - v_i lb_i \le v^d_i \le ub_i So, basically we just re-center the flux space at the old solution and then find the flux distribution closest to the new zero (center). This is the same strategy as used in cameo. In the case of linear MOMA [2]_, we instead minimize \sum_i abs(v^t_i). The linear MOMA is typically significantly faster. Also, quadratic MOMA tends to give flux distributions in which all fluxes deviate from the reference fluxes a little bit whereas linear MOMA tends to give flux distributions where the majority of fluxes are the same reference with few fluxes deviating a lot (typical effect of L2 norm vs L1 norm). The former objective function is saved in the optlang solver interface as ``"moma_old_objective"`` and this can be used to immediately extract the value of the former objective after MOMA optimization. See Also -------- pfba : parsimonious FBA References ---------- .. [1] Segrè, Daniel, Dennis Vitkup, and George M. Church. “Analysis of Optimality in Natural and Perturbed Metabolic Networks.” Proceedings of the National Academy of Sciences 99, no. 23 (November 12, 2002): 15112. https://doi.org/10.1073/pnas.232349399. .. [2] Becker, Scott A, Adam M Feist, Monica L Mo, Gregory Hannum, Bernhard Ø Palsson, and Markus J Herrgard. “Quantitative Prediction of Cellular Metabolism with Constraint-Based Models: The COBRA Toolbox.” Nature Protocols 2 (March 29, 2007): 727. """ if "moma_old_objective" in model.solver.variables: raise ValueError("The model is already adjusted for MOMA.") # Fall back to default QP solver if current one has no QP capability if not linear and sutil.interface_to_str(model.problem) not in sutil.qp_solvers: model.solver = sutil.choose_solver(model, qp=True) if solution is None: solution = pfba(model) prob = model.problem v = prob.Variable("moma_old_objective") c = prob.Constraint( model.solver.objective.expression - v, lb=0.0, ub=0.0, name="moma_old_objective_constraint", ) to_add = [v, c] model.objective = prob.Objective(Zero, direction="min", sloppy=True) obj_vars = [] for r in model.reactions: flux = solution.fluxes[r.id] if linear: components = sutil.add_absolute_expression( model, r.flux_expression, name="moma_dist_" + r.id, difference=flux, add=False, ) to_add.extend(components) obj_vars.append(components.variable) else: dist = prob.Variable("moma_dist_" + r.id) const = prob.Constraint( r.flux_expression - dist, lb=flux, ub=flux, name="moma_constraint_" + r.id, ) to_add.extend([dist, const]) obj_vars.append(dist**2) model.add_cons_vars(to_add) if linear: model.objective.set_linear_coefficients({v: 1.0 for v in obj_vars}) else: model.objective = prob.Objective(add(obj_vars), direction="min", sloppy=True) cobrapy-0.29.1/src/cobra/flux_analysis/parsimonious.py000066400000000000000000000114361467301562300231160ustar00rootroot00000000000000"""Provide parsimonious FBA implementation.""" from itertools import chain from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Union from warnings import warn from optlang.symbolics import Zero from ..core.solution import get_solution from ..util import solver as sutil if TYPE_CHECKING: from optlang.interface import Objective from cobra import Model, Reaction, Solution def optimize_minimal_flux( *args, **kwargs ) -> Callable[["Model", float, Union[Dict, "Objective"], List["Reaction"]], "Solution"]: """Perform basic pFBA to minimize total flux. .. deprecated:: 0.6.0a4 `optimize_minimal_flux` will be removed in cobrapy 1.0.0, it is replaced by `pfba`. Parameters ---------- *args: Any Non-keyword variable-length arguments. **kwargs: Any Keyword-only variable-length arguments. Returns ------- A function performing the parsimonious FBA. """ warn("optimize_minimal_flux has been renamed to pfba", DeprecationWarning) return pfba(*args, **kwargs) def pfba( model: "Model", fraction_of_optimum: float = 1.0, objective: Union[Dict, "Objective", None] = None, reactions: Optional[List["Reaction"]] = None, ) -> "Solution": """Perform basic pFBA (parsimonious Enzyme Usage Flux Balance Analysis). pFBA [1] adds the minimization of all fluxes the the objective of the model. This approach is motivated by the idea that high fluxes have a higher enzyme turn-over and that since producing enzymes is costly, the cell will try to minimize overall flux while still maximizing the original objective function, e.g. the growth rate. Parameters ---------- model : cobra.Model The model to perform pFBA on. fraction_of_optimum : float, optional The fraction of optimum which must be maintained. The original objective reaction is constrained to be greater than maximal value times the `fraction_of_optimum` (default 1.0). objective : dict or cobra.Model.objective, optional A desired objective to use during optimization in addition to the pFBA objective. Dictionaries (reaction as key, coefficient as value) can be used for linear objectives (default None). reactions : list of cobra.Reaction, optional List of cobra.Reaction. Implies `return_frame` to be true. Only return fluxes for the given reactions. Faster than fetching all fluxes if only a few are needed (default None). Returns ------- cobra.Solution The solution object to the optimized model with pFBA constraints added. References ---------- .. [1] Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A., Charusanti, P., Polpitiya, A. D., Palsson, B. O. (2010). Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Molecular Systems Biology, 6, 390. doi:10.1038/msb.2010.47 """ reactions = ( model.reactions if reactions is None else model.reactions.get_by_any(reactions) ) with model as m: add_pfba(m, objective=objective, fraction_of_optimum=fraction_of_optimum) m.slim_optimize(error_value=None) solution = get_solution(m, reactions=reactions) return solution def add_pfba( model: "Model", objective: Union[Dict, "Objective", None] = None, fraction_of_optimum: float = 1.0, ) -> None: """Add pFBA objective to the `model`. This adds objective to minimize the summed flux of all reactions to the current objective. Parameters ---------- model : cobra.Model The model to add the objective to. objective : dict or cobra.Model.objective, optional A desired objective to use during optimization in addition to the pFBA objective. Dictionaries (reaction as key, coefficient as value) can be used for linear objectives (default None). fraction_of_optimum : float, optional Fraction of optimum which must be maintained. The original objective reaction is constrained to be greater than maximal value times the `fraction_of_optimum`. See Also ------- pfba """ if objective is not None: model.objective = objective if model.solver.objective.name == "_pfba_objective": raise ValueError("The model already has a pFBA objective.") sutil.fix_objective_as_constraint(model, fraction=fraction_of_optimum) reaction_variables = ( (rxn.forward_variable, rxn.reverse_variable) for rxn in model.reactions ) variables = chain(*reaction_variables) model.objective = model.problem.Objective( Zero, direction="min", sloppy=True, name="_pfba_objective" ) model.objective.set_linear_coefficients({v: 1.0 for v in variables}) cobrapy-0.29.1/src/cobra/flux_analysis/phenotype_phase_plane.py000066400000000000000000000314671467301562300247460ustar00rootroot00000000000000"""Provide functions for phenotype phase plane analysis.""" from itertools import product from typing import TYPE_CHECKING, Dict, List, Optional, Union import numpy as np import pandas as pd from optlang.interface import OPTIMAL from ..exceptions import OptimizationError from ..util import solver as sutil from .helpers import normalize_cutoff from .variability import flux_variability_analysis as fva if TYPE_CHECKING: from optlang.interface import Objective from cobra import Model, Reaction def production_envelope( model: "Model", reactions: List["Reaction"], objective: Union[Dict, "Objective", None] = None, carbon_sources: Optional[List["Reaction"]] = None, points: int = 20, threshold: Optional[float] = None, ) -> pd.DataFrame: """Calculate the objective value conditioned on all flux combinations. The production envelope can be used to analyze a model's ability to produce a given compound conditional on the fluxes for another set of reactions, such as the uptake rates. The model is alternately optimized with respect to minimizing and maximizing the objective and the obtained fluxes are recorded. Ranges to compute production is set to the effective bounds, i.e., the minimum / maximum fluxes that can be obtained given current reaction bounds. Parameters ---------- model : cobra.Model The model to compute the production envelope for. reactions : list of cobra.Reaction A list of reaction objects. objective : dict or cobra.Model.objective, optional The objective (reaction) to use for the production envelope. Use the model's current objective if left missing (default None). carbon_sources : list of cobra.Reaction, optional One or more reactions that are the source of carbon for computing carbon (mol carbon in output over mol carbon in input) and mass yield (gram product over gram output). Only objectives with a carbon containing input and output metabolite is supported. Will identify active carbon sources in the medium if none are specified (default None). points : int, optional The number of points to calculate production for (default 20). threshold : float, optional A cut-off under which flux values will be considered to be zero. If not specified, it defaults to `model.tolerance` (default None). Returns ------- pandas.DataFrame A DataFrame with fixed columns as: - carbon_source : identifiers of carbon exchange reactions - flux_maximum : maximum objective flux - flux_minimum : minimum objective flux - carbon_yield_maximum : maximum yield of a carbon source - carbon_yield_minimum : minimum yield of a carbon source - mass_yield_maximum : maximum mass yield of a carbon source - mass_yield_minimum : minimum mass yield of a carbon source and variable columns (for each input `reactions`) as: - reaction_id : flux at each given point Raises ------ ValueError If model's objective is comprised of multiple reactions. Examples -------- >>> import cobra.io >>> from cobra.flux_analysis import production_envelope >>> model = cobra.io.load_model("textbook") >>> production_envelope(model, ["EX_glc__D_e", "EX_o2_e"]) carbon_source flux_minimum carbon_yield_minimum mass_yield_minimum ... 0 EX_glc__D_e 0.0 0.0 NaN ... 1 EX_glc__D_e 0.0 0.0 NaN ... 2 EX_glc__D_e 0.0 0.0 NaN ... 3 EX_glc__D_e 0.0 0.0 NaN ... 4 EX_glc__D_e 0.0 0.0 NaN ... .. ... ... ... ... ... 395 EX_glc__D_e NaN NaN NaN ... 396 EX_glc__D_e NaN NaN NaN ... 397 EX_glc__D_e NaN NaN NaN ... 398 EX_glc__D_e NaN NaN NaN ... 399 EX_glc__D_e NaN NaN NaN ... [400 rows x 9 columns] """ reactions = model.reactions.get_by_any(reactions) objective = model.solver.objective if objective is None else objective data = {} if carbon_sources is None: c_input = _find_carbon_sources(model) else: c_input = model.reactions.get_by_any(carbon_sources) if c_input is None: data["carbon_source"] = None elif hasattr(c_input, "id"): data["carbon_source"] = c_input.id else: data["carbon_source"] = ", ".join(rxn.id for rxn in c_input) threshold = normalize_cutoff(model, threshold) size = points ** len(reactions) for direction in ("minimum", "maximum"): data[f"flux_{direction}"] = np.full(size, np.nan, dtype=float) data[f"carbon_yield_{direction}"] = np.full(size, np.nan, dtype=float) data[f"mass_yield_{direction}"] = np.full(size, np.nan, dtype=float) grid = pd.DataFrame(data) with model: model.objective = objective objective_reactions = list(sutil.linear_reaction_coefficients(model)) if len(objective_reactions) != 1: raise ValueError( "Cannot calculate yields for objectives with multiple reactions." ) c_output = objective_reactions[0] min_max = fva(model, reactions, fraction_of_optimum=0) min_max[min_max.abs() < threshold] = 0.0 points = list( product( *[ np.linspace( min_max.at[rxn.id, "minimum"], min_max.at[rxn.id, "maximum"], points, endpoint=True, ) for rxn in reactions ] ) ) tmp = pd.DataFrame(points, columns=[rxn.id for rxn in reactions]) grid = pd.concat([grid, tmp], axis=1, copy=False) _add_envelope(model, reactions, grid, c_input, c_output, threshold) return grid def _add_envelope( model: "Model", reactions: List["Reaction"], grid: pd.DataFrame, c_input: List["Reaction"], c_output: List["Reaction"], threshold: float, ) -> None: """Add a production envelope based on the parameters provided. Parameters ---------- model : cobra.Model The model to operate on. reactions : list of cobra.Reaction The input reaction objects. grid : pandas.DataFrame The DataFrame containing all the data regarding the operation. c_input : list of cobra.Reaction The list of reaction objects acting as carbon inputs. c_output : list of cobra.Reaction The list of reaction objects acting as carbon outputs. """ if c_input is not None: input_components = [_reaction_elements(rxn) for rxn in c_input] output_components = _reaction_elements(c_output) try: input_weights = [_reaction_weight(rxn) for rxn in c_input] output_weight = _reaction_weight(c_output) except ValueError: input_weights = [] output_weight = [] else: input_components = [] output_components = [] input_weights = [] output_weight = [] for direction in ("minimum", "maximum"): with model: model.objective_direction = direction for i in range(len(grid)): with model: for rxn in reactions: point = grid.at[i, rxn.id] rxn.bounds = point, point obj_val = model.slim_optimize() if model.solver.status != OPTIMAL: continue grid.at[i, f"flux_{direction}"] = ( 0.0 if np.abs(obj_val) < threshold else obj_val ) if c_input is not None: grid.at[i, f"carbon_yield_{direction}"] = _total_yield( [rxn.flux for rxn in c_input], input_components, obj_val, output_components, ) grid.at[i, f"mass_yield_{direction}"] = _total_yield( [rxn.flux for rxn in c_input], input_weights, obj_val, output_weight, ) def _total_yield( input_fluxes: List[float], input_elements: List[float], output_flux: List[float], output_elements: List[float], ) -> float: """Compute total output per input unit. Units are typically mol carbon atoms or gram of source and product. Parameters ---------- input_fluxes : list of float A list of input reaction fluxes in the same order as the `input_components`. input_elements : list of float A list of reaction components which are in turn list of numbers. output_flux : float The output flux value. output_elements : list A list of stoichiometrically weighted output reaction components. Returns ------- float The ratio between output (mol carbon atoms or grams of product) and input (mol carbon atoms or grams of source compounds). If input flux of carbon sources is zero then numpy.nan is returned. """ carbon_input_flux = sum( _total_components_flux(flux, components, consumption=True) for flux, components in zip(input_fluxes, input_elements) ) carbon_output_flux = _total_components_flux( output_flux, output_elements, consumption=False ) try: return carbon_output_flux / carbon_input_flux except ZeroDivisionError: return np.nan def _reaction_elements(reaction: "Reaction") -> List[float]: """Split metabolites into atoms times their stoichiometric coefficients. Parameters ---------- reaction : cobra.Reaction The reaction whose metabolite components are desired. Returns ------- list of float Each of the reaction's metabolites' desired carbon elements (if any) times that metabolite's stoichiometric coefficient. """ c_elements = [ coeff * met.elements.get("C", 0) for met, coeff in reaction.metabolites.items() ] return [elem for elem in c_elements if elem != 0] def _reaction_weight(reaction: "Reaction") -> List[float]: """Return the metabolite weight times its stoichiometric coefficient. Parameters ---------- reaction : cobra.Reaction The reaction whose metabolite component weights is desired. Returns ------- list of float Each of reaction's metabolite components' weights. Raises ------ ValueError If more than one metabolite comprises the `reaction`. """ if len(reaction.metabolites) != 1: raise ValueError( "Reaction weight is only defined for single " "metabolite products or educts." ) met, coeff = next(iter(reaction.metabolites.items())) return [coeff * met.formula_weight] def _total_components_flux( flux: float, components: List[float], consumption: bool = True ) -> float: """Compute the total components consumption or production flux. Parameters ---------- flux : float The reaction flux for the components. components : list of float List of stoichiometrically weighted components. consumption : bool, optional Whether to sum up consumption or production fluxes (default True). Returns ------- float The total consumption or production flux of `components`. """ direction = 1 if consumption else -1 c_flux = [elem * flux * direction for elem in components] return sum([flux for flux in c_flux if flux > 0]) def _find_carbon_sources(model: "Model") -> List["Reaction"]: """Find all active carbon source reactions. Parameters ---------- model : Model The model whose active carbon sources need to found. Returns ------- list of cobra.Reaction The medium reactions with carbon input flux. """ try: model.slim_optimize(error_value=None) except OptimizationError: return [] reactions = model.reactions.get_by_any(list(model.medium)) reactions_fluxes = [ ( rxn, _total_components_flux(rxn.flux, _reaction_elements(rxn), consumption=True), ) for rxn in reactions ] return [rxn for rxn, c_flux in reactions_fluxes if c_flux > 0] cobrapy-0.29.1/src/cobra/flux_analysis/reaction.py000066400000000000000000000233171467301562300221730ustar00rootroot00000000000000"""Provide functions for analyzing / creating objective functions.""" from operator import attrgetter from typing import TYPE_CHECKING, Dict, Union from warnings import warn from ..core import Reaction if TYPE_CHECKING: from cobra import Model def assess( model: "Model", reaction: Union[Reaction, str], flux_coefficient_cutoff: float = 0.001, solver: str = None, ) -> Union[bool, Dict]: """Assess production capacity. Assess the capacity of the `model` to produce the precursors for the `reaction` and absorb the production of the `reaction` while the `reaction` is operating at, or above, the specified `flux_coefficient_cutoff`. .. deprecated:: 0.10.0a1 `solver` argument will be removed in cobrapy 1.0.0, it is replaced by globally setting the solver via cobra.Configuration . Parameters ---------- model : cobra.Model The cobra model to assess production capacity for. reaction : str or cobra.Reaction The reaction to assess. flux_coefficient_cutoff : float, optional The minimum flux that reaction must carry to be considered active (default 0.001). solver : str, optional The solver name. If None, the default solver will be used (default None). Returns ------- bool or dict True if the model can produce the precursors and absorb the products for the reaction operating at, or above, `flux_coefficient_cutoff`. Otherwise, a dictionary of {'precursor': Status, 'product': Status}, where 'Status' is the results from `assess_precursors` and `assess_products`, respectively. """ reaction = model.reactions.get_by_any(reaction)[0] with model as m: m.objective = reaction if m.slim_optimize(error_value=0.0) >= flux_coefficient_cutoff: return True else: results = {} results["precursors"] = assess_component( model, reaction, "reactants", flux_coefficient_cutoff ) results["products"] = assess_component( model, reaction, "products", flux_coefficient_cutoff ) return results def assess_component( model: "Model", reaction: Union[Reaction, str], side: str, flux_coefficient_cutoff: float = 0.001, solver: str = None, ) -> Union[bool, Dict]: """Assess production capacity of components. Assess the ability of the `model` to provide sufficient precursors, or absorb products, for a `reaction` operating at, or beyond, the specified `flux_coefficient_cutoff`. .. deprecated:: 0.10.0a1 `solver` argument will be removed in cobrapy 1.0.0, it is replaced by globally setting the solver via cobra.Configuration . Parameters ---------- model : cobra.Model The cobra model to assess production capacity for. reaction : str or cobra.Reaction The reaction to assess. side : {"products", "reactants"} The side of the reaction to assess. flux_coefficient_cutoff : float, optional The minimum flux that reaction must carry to be considered active (default 0.001). solver : str, optional The solver name. If None, the default solver will be used (default None). Returns ------- bool or dict True if the precursors can be simultaneously produced at the specified cutoff. False, if the model has the capacity to produce each individual precursor at the specified threshold but not all precursors at the required level simultaneously. Otherwise, a dictionary of the required and the produced fluxes for each reactant that is not produced in sufficient quantities. """ reaction = model.reactions.get_by_any(reaction)[0] result_key = {"reactants": "produced", "products": "capacity"}[side] get_components = attrgetter(side) with model as m: m.objective = reaction if m.slim_optimize(error_value=0.0) >= flux_coefficient_cutoff: return True simulation_results = {} # build the demand reactions and add all at once demand_reactions = {} for component in get_components(reaction): coeff = reaction.metabolites[component] demand = m.add_boundary(component, type="demand") demand.metabolites[component] = coeff demand_reactions[demand] = (component, coeff) # First assess whether all precursors can be produced simultaneously joint_demand = Reaction("joint_demand") for demand_reaction in demand_reactions: joint_demand += demand_reaction m.add_reactions([joint_demand]) m.objective = joint_demand if m.slim_optimize(error_value=0.0) >= flux_coefficient_cutoff: return True # Otherwise assess the ability of the model to produce each precursor # individually. Now assess the ability of the model to produce each # reactant for a reaction for demand_reaction, (component, coeff) in demand_reactions.items(): # Calculate the maximum amount of the with m: m.objective = demand_reaction flux = m.slim_optimize(error_value=0.0) # metabolite that can be produced. if flux_coefficient_cutoff > flux: # Scale the results to a single unit simulation_results.update( { component: { "required": flux_coefficient_cutoff / abs(coeff), result_key: flux / abs(coeff), } } ) if len(simulation_results) == 0: simulation_results = False return simulation_results def _optimize_or_value(model: "Model", value: float = 0.0) -> float: """Perform quick optimization and return the objective value. The objective value is returned at `value` error value. .. deprecated:: 0.22.0 `_optimize_or_value` will be removed in cobrapy 1.0.0, its functionality can be achieved by using `cobra.Model.slim_optimize`. Parameters ---------- model: cobra.Model The model to optimize. value: float The error value to consider. Returns ------- float The optimized value of the model's objective. """ return model.slim_optimize(error_value=value) def assess_precursors( model: "Model", reaction: Reaction, flux_coefficient_cutoff: float = 0.001, solver: str = None, ) -> Union[bool, Dict]: """Assess production capacity of precursors. Assess the ability of the model to provide sufficient precursors for a reaction operating at, or beyond, the `flux_coefficient_cutoff`. .. deprecated:: 0.7.0 `assess_precursors` will be removed in cobrapy 1.0.0, it is replaced by `assess_component`. Parameters ---------- model : cobra.Model The cobra model to assess production capacity for. reaction : str or cobra.Reaction The reaction to assess. flux_coefficient_cutoff : float, optional The minimum flux that reaction must carry to be considered active (default 0.001). solver : str, optional The solver name. If None, the default solver will be used (default None). Returns ------- bool or dict True if the precursors can be simultaneously produced at the specified cutoff. False, if the model has the capacity to produce each individual precursor at the specified threshold but not all precursors at the required level simultaneously. Otherwise, a dictionary of the required and the produced fluxes for each reactant that is not produced in sufficient quantities. """ warn("Use cobra.sampling.reaction.assess_component() instead.", DeprecationWarning) return assess_component( model, reaction, "reactants", flux_coefficient_cutoff, solver ) def assess_products( model: "Model", reaction: Reaction, flux_coefficient_cutoff: float = 0.001, solver: str = None, ) -> Union[bool, Dict]: """Assesses absorption capacity of products. Assess the ability of the model to to absorb the products of a reaction at a given flux rate. Useful for identifying which components might be blocking a reaction from achieving a specific flux rate. .. deprecated:: 0.7.0 `assess_products` will be removed in cobrapy 1.0.0, it is replaced by `assess_component`. Parameters ---------- model : cobra.Model The cobra model to assess production capacity for. reaction : str or cobra.Reaction The reaction to assess. flux_coefficient_cutoff : float, optional The minimum flux that reaction must carry to be considered active (default 0.001). solver : str, optional The solver name. If None, the default solver will be used (default None). Returns ------- bool or dict True if the model has the capacity to absorb all the reaction products being simultaneously given the specified cutoff. False, if the model has the capacity to absorb each individual product but not all products at the required level simultaneously. Otherwise, a dictionary of the required and the capacity fluxes for each product that is not absorbed in sufficient quantities. """ warn("Use cobra.sampling.reaction.assess_component() instead.", DeprecationWarning) return assess_component( model, reaction, "products", flux_coefficient_cutoff, solver ) cobrapy-0.29.1/src/cobra/flux_analysis/room.py000066400000000000000000000123471467301562300213440ustar00rootroot00000000000000"""Provide regulatory on/off minimization (ROOM).""" from typing import TYPE_CHECKING, Optional from optlang.symbolics import Zero from .parsimonious import pfba if TYPE_CHECKING: from cobra import Model, Solution def room( model: "Model", solution: Optional["Solution"] = None, linear: bool = False, delta: float = 0.03, epsilon: float = 1e-03, ) -> "Solution": """Compute a solution based on regulatory on/off minimization (ROOM). Compute a new flux distribution that minimizes the number of active reactions needed to accommodate a previous reference solution. Regulatory on/off minimization (ROOM) is generally used to assess the impact of knock-outs. Thus, the typical usage is to provide a wild-type flux distribution as reference and a `model` in knock-out state. Parameters ---------- model : cobra.Model The model state to compute a ROOM-based solution for. solution : cobra.Solution, optional A (wild-type) reference solution (default None). linear : bool, optional Whether to use the linear ROOM formulation or not (default False). delta: float, optional The relative tolerance range (additive) (default 0.03). epsilon: float, optional The absolute tolerance range (multiplicative) (default 0.001). Returns ------- cobra.Solution A flux distribution with minimal active reaction changes compared to the reference. See Also -------- add_room : add ROOM constraints and objective """ with model: add_room( model=model, solution=solution, linear=linear, delta=delta, epsilon=epsilon ) solution = model.optimize() return solution def add_room( model: "Model", solution: Optional["Solution"] = None, linear: bool = False, delta: float = 0.03, epsilon: float = 1e-03, ) -> None: r""" Add constraints and objective for ROOM. This function adds variables and constraints for applying regulatory on/off minimization (ROOM) to the model. Parameters ---------- model : cobra.Model The model to add ROOM constraints and objective to. solution : cobra.Solution, optional A previous solution to use as a reference. If no solution is given, one will be computed using pFBA (default None). linear : bool, optional Whether to use the linear ROOM formulation or not (default False). delta: float, optional The relative tolerance range which is additive in nature (default 0.03). epsilon: float, optional The absolute range of tolerance which is multiplicative (default 0.001). Notes ----- The formulation used here is the same as stated in the original paper [1]_. The mathematical expression is given below: minimize: \sum_{i=1}^m y^i s.t. : Sv = 0 v_min \le v \le v_max v_j = 0 j \in A for 1 \le i \le m v_i - y_i(v_{max,i} - w_i^u) \le w_i^u (1) v_i - y_i(v_{min,i} - w_i^l) \le w_i^l (2) y_i \in {0,1} (3) w_i^u = w_i + \delta|w_i| + \epsilon w_i^l = w_i - \delta|w_i| - \epsilon So, for the linear version of the ROOM , constraint (3) is relaxed to 0 \le y_i \le 1. See Also -------- pfba : parsimonious FBA References ---------- .. [1] Tomer Shlomi, Omer Berkman and Eytan Ruppin, "Regulatory on/off minimization of metabolic flux changes after genetic perturbations", PNAS 2005 102 (21) 7695-7700; doi:10.1073/pnas.0406346102 """ if "room_old_objective" in model.solver.variables: raise ValueError("Model is already adjusted for ROOM.") # optimizes if no reference solution is provided if solution is None: solution = pfba(model) prob = model.problem variable = prob.Variable("room_old_objective", ub=solution.objective_value) constraint = prob.Constraint( model.solver.objective.expression - variable, ub=0.0, lb=0.0, name="room_old_objective_constraint", ) model.objective = prob.Objective(Zero, direction="min", sloppy=True) vars_and_cons = [variable, constraint] obj_vars = [] for rxn in model.reactions: flux = solution.fluxes[rxn.id] if linear: y = prob.Variable("y_" + rxn.id, lb=0, ub=1) delta = epsilon = 0.0 else: y = prob.Variable("y_" + rxn.id, type="binary") # upper constraint w_u = flux + (delta * abs(flux)) + epsilon upper_const = prob.Constraint( rxn.flux_expression - y * (rxn.upper_bound - w_u), ub=w_u, name="room_constraint_upper_" + rxn.id, ) # lower constraint w_l = flux - (delta * abs(flux)) - epsilon lower_const = prob.Constraint( rxn.flux_expression - y * (rxn.lower_bound - w_l), lb=w_l, name="room_constraint_lower_" + rxn.id, ) vars_and_cons.extend([y, upper_const, lower_const]) obj_vars.append(y) model.add_cons_vars(vars_and_cons) model.objective.set_linear_coefficients({v: 1.0 for v in obj_vars}) cobrapy-0.29.1/src/cobra/flux_analysis/variability.py000066400000000000000000000352151467301562300227060ustar00rootroot00000000000000"""Provide variability based methods such as flux variability or gene essentiality.""" import logging from typing import TYPE_CHECKING, List, Optional, Set, Tuple, Union from warnings import warn import numpy as np import pandas as pd from optlang.symbolics import Zero from ..core import Configuration, get_solution from ..util import ProcessPool from ..util import solver as sutil from .deletion import single_gene_deletion, single_reaction_deletion from .helpers import normalize_cutoff from .loopless import loopless_fva_iter from .parsimonious import add_pfba if TYPE_CHECKING: from cobra import Gene, Model, Reaction logger = logging.getLogger(__name__) configuration = Configuration() def _init_worker(model: "Model", loopless: bool, sense: str) -> None: """Initialize a global model object for multiprocessing. Parameters ---------- model: cobra.Model The model to operate on. loopless: bool Whether to use loopless version. sense: {"max", "min"} Whether to maximise or minimise objective. """ global _model global _loopless _model = model _model.solver.objective.direction = sense _loopless = loopless def _fva_step(reaction_id: str) -> Tuple[str, float]: """Take a step for calculating FVA. Parameters ---------- reaction_id: str The ID of the reaction. Returns ------- tuple of (str, float) The reaction ID with the flux value. """ global _model global _loopless rxn = _model.reactions.get_by_id(reaction_id) # The previous objective assignment already triggers a reset # so directly update coefs here to not trigger redundant resets # in the history manager which can take longer than the actual # FVA for small models _model.solver.objective.set_linear_coefficients( {rxn.forward_variable: 1, rxn.reverse_variable: -1} ) _model.slim_optimize() sutil.check_solver_status(_model.solver.status) if _loopless: value = loopless_fva_iter(_model, rxn) else: value = _model.solver.objective.value # handle infeasible case if value is None: value = float("nan") logger.warning( f"Could not get flux for reaction {rxn.id}, setting it to NaN. " "This is usually due to numerical instability." ) _model.solver.objective.set_linear_coefficients( {rxn.forward_variable: 0, rxn.reverse_variable: 0} ) return reaction_id, value def flux_variability_analysis( model: "Model", reaction_list: Optional[List[Union["Reaction", str]]] = None, loopless: bool = False, fraction_of_optimum: float = 1.0, pfba_factor: Optional[float] = None, processes: Optional[int] = None, ) -> pd.DataFrame: """Determine the minimum and maximum flux value for each reaction. Parameters ---------- model : cobra.Model The model for which to run the analysis. It will *not* be modified. reaction_list : list of cobra.Reaction or str, optional The reactions for which to obtain min/max fluxes. If None will use all reactions in the model (default None). loopless : bool, optional Whether to return only loopless solutions. This is significantly slower. Please also refer to the notes (default False). fraction_of_optimum : float, optional Must be <= 1.0. Requires that the objective value is at least the fraction times maximum objective value. A value of 0.85 for instance means that the objective has to be at least at 85% percent of its maximum (default 1.0). pfba_factor : float, optional Add an additional constraint to the model that requires the total sum of absolute fluxes must not be larger than this value times the smallest possible sum of absolute fluxes, i.e., by setting the value to 1.1 the total sum of absolute fluxes must not be more than 10% larger than the pFBA solution. Since the pFBA solution is the one that optimally minimizes the total flux sum, the `pfba_factor` should, if set, be larger than one. Setting this value may lead to more realistic predictions of the effective flux bounds (default None). processes : int, optional The number of parallel processes to run. If not explicitly passed, will be set from the global configuration singleton (default None). Returns ------- pandas.DataFrame A data frame with reaction identifiers as the index and two columns: - maximum: indicating the highest possible flux - minimum: indicating the lowest possible flux Notes ----- This implements the fast version as described in [1]_. Please note that the flux distribution containing all minimal/maximal fluxes does not have to be a feasible solution for the model. Fluxes are minimized/maximized individually and a single minimal flux might require all others to be sub-optimal. Using the loopless option will lead to a significant increase in computation time (about a factor of 100 for large models). However, the algorithm used here (see [2]_) is still more than 1000x faster than the "naive" version using `add_loopless(model)`. Also note that if you have included constraints that force a loop (for instance by setting all fluxes in a loop to be non-zero) this loop will be included in the solution. References ---------- .. [1] Computationally efficient flux variability analysis. Gudmundsson S, Thiele I. BMC Bioinformatics. 2010 Sep 29;11:489. doi: 10.1186/1471-2105-11-489, PMID: 20920235 .. [2] CycleFreeFlux: efficient removal of thermodynamically infeasible loops from flux distributions. Desouki AA, Jarre F, Gelius-Dietrich G, Lercher MJ. Bioinformatics. 2015 Jul 1;31(13):2159-65. doi: 10.1093/bioinformatics/btv096. """ if reaction_list is None: reaction_ids = [r.id for r in model.reactions] else: reaction_ids = [r.id for r in model.reactions.get_by_any(reaction_list)] if processes is None: processes = configuration.processes num_reactions = len(reaction_ids) processes = min(processes, num_reactions) fva_result = pd.DataFrame( { "minimum": np.zeros(num_reactions, dtype=float), "maximum": np.zeros(num_reactions, dtype=float), }, index=reaction_ids, ) prob = model.problem with model: # Safety check before setting up FVA. model.slim_optimize( error_value=None, message="There is no optimal solution for the chosen objective!", ) # Add the previous objective as a variable to the model then set it to # zero. This also uses the fraction to create the lower/upper bound for # the old objective. # TODO: Use utility function here (fix_objective_as_constraint)? if model.solver.objective.direction == "max": fva_old_objective = prob.Variable( "fva_old_objective", lb=fraction_of_optimum * model.solver.objective.value, ) else: fva_old_objective = prob.Variable( "fva_old_objective", ub=fraction_of_optimum * model.solver.objective.value, ) fva_old_obj_constraint = prob.Constraint( model.solver.objective.expression - fva_old_objective, lb=0, ub=0, name="fva_old_objective_constraint", ) model.add_cons_vars([fva_old_objective, fva_old_obj_constraint]) if pfba_factor is not None: if pfba_factor < 1.0: warn( "The 'pfba_factor' should be larger or equal to 1.", UserWarning, ) with model: add_pfba(model, fraction_of_optimum=0) ub = model.slim_optimize(error_value=None) flux_sum = prob.Variable("flux_sum", ub=pfba_factor * ub) flux_sum_constraint = prob.Constraint( model.solver.objective.expression - flux_sum, lb=0, ub=0, name="flux_sum_constraint", ) model.add_cons_vars([flux_sum, flux_sum_constraint]) model.objective = Zero # This will trigger the reset as well for what in ("minimum", "maximum"): if processes > 1: # We create and destroy a new pool here in order to set the # objective direction for all reactions. This creates a # slight overhead but seems the most clean. chunk_size = len(reaction_ids) // processes with ProcessPool( processes, initializer=_init_worker, initargs=(model, loopless, what[:3]), ) as pool: for rxn_id, value in pool.imap_unordered( _fva_step, reaction_ids, chunksize=chunk_size ): fva_result.at[rxn_id, what] = value else: _init_worker(model, loopless, what[:3]) for rxn_id, value in map(_fva_step, reaction_ids): fva_result.at[rxn_id, what] = value return fva_result[["minimum", "maximum"]] def find_blocked_reactions( model: "Model", reaction_list: Optional[List[Union["Reaction", str]]] = None, zero_cutoff: Optional[float] = None, open_exchanges: bool = False, processes: Optional[int] = None, ) -> List["Reaction"]: """Find reactions that cannot carry any flux. The question whether or not a reaction is blocked is highly dependent on the current exchange reaction settings for a COBRA model. Hence an argument is provided to open all exchange reactions. Parameters ---------- model : cobra.Model The model to analyze. reaction_list : list of cobra.Reaction or str, optional List of reactions to consider, the default includes all model reactions (default None). zero_cutoff : float, optional Flux value which is considered to effectively be zero. The default is set to use `model.tolerance` (default None). open_exchanges : bool, optional Whether or not to open all exchange reactions to very high flux ranges (default False). processes : int, optional The number of parallel processes to run. Can speed up the computations if the number of reactions is large. If not explicitly passed, it will be set from the global configuration singleton (default None). Returns ------- list of cobra.Reaction List with the identifiers of blocked reactions. Notes ----- Sink and demand reactions are left untouched. Please modify them manually. """ zero_cutoff = normalize_cutoff(model, zero_cutoff) with model: if open_exchanges: for reaction in model.exchanges: reaction.bounds = ( min(reaction.lower_bound, -1000), max(reaction.upper_bound, 1000), ) if reaction_list is None: reaction_list = model.reactions # Limit the search space to reactions which have zero flux. If the # reactions already carry flux in this solution, # then they cannot be blocked. model.slim_optimize() solution = get_solution(model, reactions=reaction_list) reaction_list = solution.fluxes[ solution.fluxes.abs() < zero_cutoff ].index.tolist() # Run FVA to find reactions where both the minimal and maximal flux # are zero (below the cut off). flux_span = flux_variability_analysis( model, fraction_of_optimum=0.0, reaction_list=reaction_list, processes=processes, ) return flux_span[flux_span.abs().max(axis=1) < zero_cutoff].index.tolist() def find_essential_genes( model: "Model", threshold: Optional[float] = None, processes: Optional[int] = None, ) -> Set["Gene"]: """Return a set of essential genes. A gene is considered essential if restricting the flux of all reactions that depend on it to zero causes the objective, e.g., the growth rate, to also be zero, below the threshold, or infeasible. Parameters ---------- model : cobra.Model The model to find the essential genes for. threshold : float, optional Minimal objective flux to be considered viable. By default this is 1% of the maximal objective (default None). processes : int, optional The number of parallel processes to run. Can speed up the computations if the number of knockouts to perform is large. If not explicitly passed, it will be set from the global configuration singleton (default None). Returns ------- set of cobra.Gene Set of essential genes. """ if threshold is None: threshold = model.slim_optimize(error_value=None) * 1e-02 deletions = single_gene_deletion(model, method="fba", processes=processes) essential = deletions.loc[ deletions["growth"].isna() | (deletions["growth"] < threshold), : ].ids return {model.genes.get_by_id(g) for ids in essential for g in ids} def find_essential_reactions( model: "Model", threshold: Optional[float] = None, processes: Optional[int] = None, ) -> Set["Reaction"]: """Return a set of essential reactions. A reaction is considered essential if restricting its flux to zero causes the objective, e.g., the growth rate, to also be zero, below the threshold, or infeasible. Parameters ---------- model : cobra.Model The model to find the essential reactions for. threshold : float, optional Minimal objective flux to be considered viable. By default this is 1% of the maximal objective (default None). processes : int, optional The number of parallel processes to run. Can speed up the computations if the number of knockouts to perform is large. If not explicitly passed, it will be set from the global configuration singleton (default None). Returns ------- set of cobra.Reaction Set of essential reactions. """ if threshold is None: threshold = model.slim_optimize(error_value=None) * 1e-02 deletions = single_reaction_deletion(model, method="fba", processes=processes) essential = deletions.loc[ deletions["growth"].isna() | (deletions["growth"] < threshold), : ].ids return {model.reactions.get_by_id(r) for ids in essential for r in ids} cobrapy-0.29.1/src/cobra/io/000077500000000000000000000000001467301562300155355ustar00rootroot00000000000000cobrapy-0.29.1/src/cobra/io/__init__.py000066400000000000000000000007741467301562300176560ustar00rootroot00000000000000"""Provide functions for loading and saving metabolic models.""" from cobra.io.dict import model_from_dict, model_to_dict from cobra.io.json import from_json, load_json_model, save_json_model, to_json from cobra.io.mat import load_matlab_model, save_matlab_model from cobra.io.sbml import read_sbml_model, write_sbml_model, validate_sbml_model from cobra.io.yaml import from_yaml, load_yaml_model, save_yaml_model, to_yaml from cobra.io.web import AbstractModelRepository, BiGGModels, BioModels, load_model cobrapy-0.29.1/src/cobra/io/dict.py000066400000000000000000000261741467301562300170440ustar00rootroot00000000000000"""Provide functions for cobrapy objects to generic Python objects and vice-versa.""" from collections import OrderedDict from operator import attrgetter, itemgetter from typing import TYPE_CHECKING, Dict, List, Sequence, Set, Union import numpy as np from ..core import Gene, Metabolite, Model, Reaction from ..util.solver import set_objective if TYPE_CHECKING: from cobra import Object _REQUIRED_REACTION_ATTRIBUTES = [ "id", "name", "metabolites", "lower_bound", "upper_bound", "gene_reaction_rule", ] _ORDERED_OPTIONAL_REACTION_KEYS = [ "objective_coefficient", "subsystem", "notes", "annotation", ] _OPTIONAL_REACTION_ATTRIBUTES = { "objective_coefficient": 0, "subsystem": "", "notes": {}, "annotation": {}, } _REQUIRED_METABOLITE_ATTRIBUTES = ["id", "name", "compartment"] _ORDERED_OPTIONAL_METABOLITE_KEYS = [ "charge", "formula", "_bound", "notes", "annotation", ] _OPTIONAL_METABOLITE_ATTRIBUTES = { "charge": None, "formula": None, "_bound": 0, "notes": {}, "annotation": {}, } _REQUIRED_GENE_ATTRIBUTES = ["id", "name"] _ORDERED_OPTIONAL_GENE_KEYS = ["notes", "annotation"] _OPTIONAL_GENE_ATTRIBUTES = { "notes": {}, "annotation": {}, } _ORDERED_OPTIONAL_MODEL_KEYS = ["name", "compartments", "notes", "annotation"] _OPTIONAL_MODEL_ATTRIBUTES = { "name": None, # "description": None, should not actually be included "compartments": [], "notes": {}, "annotation": {}, } def _fix_type( value: Union[str, float, bool, Set, Dict] ) -> Union[str, float, bool, List, OrderedDict]: """Convert possible types to correct Python types. Parameters ---------- value : str, float, bool, set, dict The value to fix type for. Returns ------- str, float, bool, list, dict The fixed type for the value. """ # Because numpy floats can not be pickled to json if isinstance(value, str): return str(value) if isinstance(value, float): return float(value) if isinstance(value, bool): return bool(value) if isinstance(value, set): return list(value) if isinstance(value, dict): return OrderedDict((key, value[key]) for key in sorted(value)) # handle legacy Formula type if value.__class__.__name__ == "Formula": return str(value) if value is None: return "" return value def _update_optional( cobra_object: "Object", new_dict: Dict, optional_attribute_dict: Dict, ordered_keys: Sequence, ) -> None: """Update `new_dict` with optional attributes from `cobra_object`. Parameters ---------- cobra_object : cobra.Object The cobra Object to update optional attributes from. new_dict : dict The dictionary to update optional attributes for. optional_attribute_dict : dict The dictionary to use as default value lookup store. ordered_keys : list, tuple The keys to update values for. Raises ------ IndexError If key in `ordered_keys` is not found in `optional_attribute_dict`. AttributeError If key in `ordered_keys` is not found in `cobra_object`. """ for key in ordered_keys: default = optional_attribute_dict[key] value = getattr(cobra_object, key) if value is None or value == default: continue new_dict[key] = _fix_type(value) def _metabolite_to_dict(metabolite: Metabolite) -> OrderedDict: """Convert a cobra Metabolite object to dictionary. Parameters ---------- metabolite : cobra.Metabolite The cobra.Metabolite to convert to dictionary. Returns ------- dict The converted dictionary object. See Also -------- _metabolite_from_dict : Convert a dictionary to cobra Metabolite object. """ new_metabolite = OrderedDict() for key in _REQUIRED_METABOLITE_ATTRIBUTES: new_metabolite[key] = _fix_type(getattr(metabolite, key)) _update_optional( metabolite, new_metabolite, _OPTIONAL_METABOLITE_ATTRIBUTES, _ORDERED_OPTIONAL_METABOLITE_KEYS, ) return new_metabolite def _metabolite_from_dict(metabolite: Dict) -> Metabolite: """Convert a dictionary to cobra Metabolite object. Parameters ---------- metabolite : dict The dictionary to convert to cobra.Metabolite . Returns ------- cobra.Metabolite See Also -------- _metabolite_to_dict : Convert a cobra Metabolite object to dictionary. """ new_metabolite = Metabolite() for k, v in metabolite.items(): setattr(new_metabolite, k, v) return new_metabolite def _gene_to_dict(gene: Gene) -> OrderedDict: """Convert a cobra Gene object to dictionary. Parameters ---------- gene : cobra.Gene The cobra.Gene to convert to dictionary. Returns ------- dict The converted dictionary object. See Also -------- _gene_from_dict : Convert a dictionary to cobra Gene object. """ new_gene = OrderedDict() for key in _REQUIRED_GENE_ATTRIBUTES: new_gene[key] = _fix_type(getattr(gene, key)) _update_optional( gene, new_gene, _OPTIONAL_GENE_ATTRIBUTES, _ORDERED_OPTIONAL_GENE_KEYS ) return new_gene def gene_from_dict(gene: Dict) -> Gene: """Convert a dictionary to cobra Gene object. Parameters ---------- gene : dict The dictionary to convert to cobra.Gene . Returns ------- cobra.Gene The converted cobra.Gene object. See Also -------- _gene_to_dict : Convert a cobra Gene object to a dictionary. """ new_gene = Gene(gene["id"]) for k, v in gene.items(): setattr(new_gene, k, v) return new_gene def _reaction_to_dict(reaction: Reaction) -> OrderedDict: """Convert a cobra Reaction object to a dictionary. Parameters ---------- reaction : cobra.Reaction The cobra.Reaction to convert to dictionary. Returns ------- dict The converted dictionary object. See Also -------- _reaction_from_dict : Convert a dictionary to a cobra Reaction object. """ new_reaction = OrderedDict() for key in _REQUIRED_REACTION_ATTRIBUTES: if key != "metabolites": if key == "lower_bound" and ( np.isnan(reaction.lower_bound) or np.isinf(reaction.lower_bound) ): new_reaction[key] = str(_fix_type(getattr(reaction, key))) elif key == "upper_bound" and ( np.isnan(reaction.upper_bound) or np.isinf(reaction.upper_bound) ): new_reaction[key] = str(_fix_type(getattr(reaction, key))) else: new_reaction[key] = _fix_type(getattr(reaction, key)) continue mets = OrderedDict() for met in sorted(reaction.metabolites, key=attrgetter("id")): mets[str(met)] = reaction.metabolites[met] new_reaction["metabolites"] = mets _update_optional( reaction, new_reaction, _OPTIONAL_REACTION_ATTRIBUTES, _ORDERED_OPTIONAL_REACTION_KEYS, ) return new_reaction def _reaction_from_dict(reaction: Dict, model: Model) -> Reaction: """Convert a dictionary to a cobra Reaction object. Parameters ---------- reaction : dict The dictionary to convert to cobra.Reaction . model : cobra.Model The model to which the reaction should associate with. Returns ------- cobra.Reaction The converted cobra.Reaction object. See Also -------- _reaction_to_dict : Convert a cobra Reaction object to a dictionary. """ new_reaction = Reaction() for k, v in reaction.items(): if k in {"objective_coefficient", "reversibility", "reaction"}: continue elif k == "metabolites": new_reaction.add_metabolites( OrderedDict( (model.metabolites.get_by_id(str(met)), coeff) for met, coeff in v.items() ) ) else: if k == "lower_bound" or k == "upper_bound": setattr(new_reaction, k, float(v)) else: setattr(new_reaction, k, v) return new_reaction def model_to_dict(model: Model, sort: bool = False) -> OrderedDict: """Convert a cobra Model to a dictionary. Parameters ---------- model : cobra.Model The model to reformulate as a dict. sort : bool, optional Whether to sort the metabolites, reactions, and genes or maintain the order defined in the model (default False). Returns ------- OrderedDict A dictionary with keys: 'genes', 'compartments', 'id', 'metabolites', 'notes' and 'reactions'; where 'metabolites', 'genes' and 'metabolites' are in turn lists of dictionaries holding all attributes to form the corresponding object. See Also -------- model_from_dict : Convert a dictionary to a cobra Model. """ obj = OrderedDict() obj["metabolites"] = list(map(_metabolite_to_dict, model.metabolites)) obj["reactions"] = list(map(_reaction_to_dict, model.reactions)) obj["genes"] = list(map(_gene_to_dict, model.genes)) obj["id"] = model.id _update_optional( model, obj, _OPTIONAL_MODEL_ATTRIBUTES, _ORDERED_OPTIONAL_MODEL_KEYS ) if sort: get_id = itemgetter("id") obj["metabolites"].sort(key=get_id) obj["reactions"].sort(key=get_id) obj["genes"].sort(key=get_id) return obj def model_from_dict(obj: Dict) -> Model: """Build a cobra Model from a dictionary. Models stored in JSON are first formulated as a dictionary that can be read to a cobra Model using this function. Parameters ---------- obj : dict A dictionary with keys: 'genes', 'compartments', 'id', 'metabolites', 'notes' and 'reactions'; where 'metabolites', 'genes' and 'metabolites' are in turn lists of dictionaries holding all attributes to form the corresponding object. Returns ------- cobra.Model The generated model. Raises ------ ValueError If `obj` has no 'reactions' attribute. See Also -------- model_to_dict : Convert a cobra Model to a dictionary. """ if "reactions" not in obj: raise ValueError("Object has no .reactions attribute. Cannot load.") model = Model() model.add_metabolites( [_metabolite_from_dict(metabolite) for metabolite in obj["metabolites"]] ) model.genes.extend([gene_from_dict(gene) for gene in obj["genes"]]) model.add_reactions( [_reaction_from_dict(reaction, model) for reaction in obj["reactions"]] ) objective_reactions = [ rxn for rxn in obj["reactions"] if rxn.get("objective_coefficient", 0) != 0 ] coefficients = { model.reactions.get_by_id(rxn["id"]): rxn["objective_coefficient"] for rxn in objective_reactions } set_objective(model, coefficients) for k, v in obj.items(): if k in {"id", "name", "notes", "compartments", "annotation"}: setattr(model, k, v) return model cobrapy-0.29.1/src/cobra/io/json.py000066400000000000000000000072551467301562300170710ustar00rootroot00000000000000"""Provide functions for I/O in JSON format.""" import json from pathlib import Path from typing import IO, TYPE_CHECKING, Any, Union from .dict import model_from_dict, model_to_dict if TYPE_CHECKING: from cobra import Model JSON_SPEC = "1" def to_json(model: "Model", sort: bool = False, **kwargs: Any) -> str: """Return the model as a JSON string. Parameters ---------- model : cobra.Model The cobra model to represent. sort : bool, optional Whether to sort the metabolites, reactions, and genes or maintain the order defined in the model (default False). **kwargs : Any Keyword arguments passed on to `json.dumps`. Returns ------- str JSON string representation of the cobra model. See Also -------- save_json_model : Write directly to a file. json.dumps : Base function. """ obj = model_to_dict(model, sort=sort) obj["version"] = JSON_SPEC return json.dumps(obj, allow_nan=False, **kwargs) def from_json(document: str) -> "Model": """Load a cobra model from a JSON string. Parameters ---------- document : str The JSON string representation of a cobra model. Returns ------- cobra.Model The cobra model as interpreted from the JSON string. See Also -------- load_json_model : Load directly from a file. """ return model_from_dict(json.loads(document)) def save_json_model( model: "Model", filename: Union[str, Path, IO], sort: bool = False, pretty: bool = False, **kwargs: Any, ) -> None: """Write the cobra model to a file in JSON format. Parameters ---------- model : cobra.Model The cobra model to represent. filename : str or file-like File path or descriptor that the JSON representation should be written to. sort : bool, optional Whether to sort the metabolites, reactions, and genes or maintain the order defined in the model (default False). pretty : bool, optional Whether to format the JSON more compactly (default) or in a more verbose but easier to read fashion. Can be partially overwritten by the `**kwargs` (default False). **kwargs : Any Keyword arguments passed to `json.dump`. See Also -------- to_json : Return a string representation. json.dump : Base function. """ obj = model_to_dict(model, sort=sort) obj["version"] = JSON_SPEC if pretty: dump_opts = { "indent": 4, "separators": (",", ": "), "sort_keys": True, "allow_nan": False, } else: dump_opts = { "indent": 0, "separators": (",", ":"), "sort_keys": False, "allow_nan": False, } dump_opts.update(**kwargs) if isinstance(filename, (str, Path)): with open(filename, "w") as file_handle: json.dump(obj, file_handle, **dump_opts) else: json.dump(obj, filename, **dump_opts) def load_json_model(filename: Union[str, Path, IO]) -> "Model": """Load a cobra model from a file in JSON format. Parameters ---------- filename : str or file-like File path or descriptor that contains the JSON document describing the cobra model. Returns ------- cobra.Model The cobra model as represented in the JSON document. See Also -------- from_json : Load from a JSON string. """ if isinstance(filename, (str, Path)): with open(filename, "r") as file_handle: return model_from_dict(json.load(file_handle)) else: return model_from_dict(json.load(filename)) cobrapy-0.29.1/src/cobra/io/mat.py000066400000000000000000001060701467301562300166740ustar00rootroot00000000000000"""Provide functions for I/O in MATLAB (.mat) format.""" import logging import re from collections import OrderedDict from pathlib import Path from typing import IO, Dict, Iterable, List, Optional, Pattern, Union import numpy as np from ..core import Gene, Group, Metabolite, Model, Object, Reaction from ..util import create_stoichiometric_matrix from ..util.solver import set_objective try: import scipy.io as scipy_io import scipy.sparse as scipy_sparse except ImportError: scipy_sparse = None scipy_io = None logger = logging.getLogger(__name__) # The following dictionaries are based on # https://github.com/opencobra/cobratoolbox/blob/docs/source/notes/COBRAModelFields.md # at commit 83d26938a9babff79289d40e20f5f50dd5b710fa # Which is the most updated master as of April 4th, 2022 MET_MATLAB_TO_PROVIDERS = { "metHMDBID": "hmdb", "metInChIString": "inchi", "metKEGGID": "kegg.compound", "metKEGGGlycanID": "kegg.glycan", "metKEGGDrugID": "kegg.drug", "metUniPathway": "unipathway.compound", "metPubChemID": "pubchem.compound", "metPubChemSubstance": "pubchem.substance", "metCHEBIID": "chebi", "metMetaNetXID": "metanetx.chemical", "metSEEDID": "seed.compound", "metBiGGID": "bigg.metabolite", "metBioCycID": "biocyc", "metEnviPathID": "envipath", "metLIPIDMAPSID": "lipidmaps", "metReactomeID": "reactome", "metSABIORKID": "sabiork.compound", "metSLMID": "slm", "metSMILES": "SMILES", "metSBOTerms": "sbo", "metCasNumber": "cas", } MET_PROVIDERS_TO_MATLAB = { MET_MATLAB_TO_PROVIDERS[k]: k for k in MET_MATLAB_TO_PROVIDERS.keys() } MET_NOTES_TO_MATLAB = { "metNotes": "metNotes", } MET_MATLAB_TO_NOTES = {MET_NOTES_TO_MATLAB[k]: k for k in MET_NOTES_TO_MATLAB.keys()} RXN_MATLAB_TO_PROVIDERS = { "rxnECNumbers": "ec-code", "rxnReferences": "pubmed", "rxnKEGGID": "kegg.reaction", "rxnKEGGPathways": "kegg.pathway", "rxnMetaNetXID": "metanetx.reaction", "rxnSEEDID": "seed.reaction", "rxnBiGGID": "bigg.reaction", "rxnBioCycID": "biocyc", "rxnRheaID": "rhea", "rxnReactomeID": "reactome", "rxnSABIORKID": "sabiork.reaction", "rxnBRENDAID": "brenda", "rxnSBOTerms": "sbo", } RXN_PROVIDERS_TO_MATLAB = { RXN_MATLAB_TO_PROVIDERS[k]: k for k in RXN_MATLAB_TO_PROVIDERS.keys() } CONFIDENCE_STR = "Confidence Level" RXN_MATLAB_TO_NOTES = { "rxnReferences": "References", "rxnNotes": "NOTES", "rxnConfidenceScores": CONFIDENCE_STR, } RXN_NOTES_TO_MATLAB = { CONFIDENCE_STR: "rxnConfidenceScores", "NOTES": "rxnNotes", "References": "rxnNotes", } GENE_MATLAB_TO_PROVIDERS = { "geneEntrezID": "ncbigene", "geneRefSeqID": "refseq", "geneUniprotID": "uniprot", "geneEcoGeneID": "ecogene", "geneKEGGID": "kegg.gene", "geneHPRDID": "hprd", "geneASAPID": "asap", "geneCCDSID": "ccds", "geneNCBIProteinID": "ncbiprotein", } GENE_PROVIDERS_TO_MATLAB = { GENE_MATLAB_TO_PROVIDERS[k]: k for k in GENE_MATLAB_TO_PROVIDERS.keys() } DICT_GENE = "DICT_GENE" DICT_GENE_REV = "DICT_GENE_REV" DICT_MET = "DICT_MET" DICT_MET_REV = "DICT_MET_REV" DICT_MET_NOTES = "DICT_MET_NOTES" DICT_MET_NOTES_REV = "DICT_MET_NOTES_REV" DICT_REACTION = "DICT_REACTION" DICT_REACTION_REV = "DICT_REACTION_REV" DICT_REACTION_NOTES = "DICT_REACTION_NOTES" DICT_REACTION_NOTES_REV = "DICT_REACTION_NOTES_REV" DICT_REPLACE: dict = { DICT_GENE: GENE_MATLAB_TO_PROVIDERS, DICT_GENE_REV: GENE_PROVIDERS_TO_MATLAB, DICT_MET: MET_MATLAB_TO_PROVIDERS, DICT_MET_REV: MET_PROVIDERS_TO_MATLAB, DICT_MET_NOTES: MET_MATLAB_TO_NOTES, DICT_MET_NOTES_REV: MET_NOTES_TO_MATLAB, DICT_REACTION: RXN_MATLAB_TO_PROVIDERS, DICT_REACTION_REV: RXN_PROVIDERS_TO_MATLAB, DICT_REACTION_NOTES: RXN_MATLAB_TO_NOTES, DICT_REACTION_NOTES_REV: RXN_NOTES_TO_MATLAB, } # precompiled regular expressions (kept globally for caching) _bracket_re = re.compile(r"[\[(](?P[a-zA-Z]+)[])]$") # Some older models have _boundary for (some) exchange reactions _underscore_re = re.compile(r"_(?P[a-zA-Z]+)(_boundary)?$") _pubmed_re = re.compile("PMID: ?(\\d+),?") _punctuation_re = re.compile(r"^[;,.'\"]+$") _double_punctuation_re = re.compile(r"[;,.'\"]{2,}") _ec_re = re.compile(r"([\d\-]+.[\d\-]+.[\d\-]+.[\d-]+)") _chebi_re = re.compile(r"\D*(\d+),?") _sbo_re = re.compile(r"\D*(\d+),?") def _get_id_compartment(_id: str) -> str: """Extract the compartment from the `id` string. Parameters ---------- _id : str The ID string to extract component from. Returns ------- str The extracted component string. """ bracket_search = _bracket_re.search(_id) if bracket_search: return bracket_search.group("compartment") underscore_search = _underscore_re.search(_id) if underscore_search: return underscore_search.group("compartment") def _cell(x: Iterable[str]) -> np.ndarray: """Translate an iterable `x` into a MATLAB cell array. Parameters ---------- x : iterable of str The data iterable to convert to cell array. Returns ------- numpy.ndarray The converted cell array compatible with MATLAB. """ x_no_none = [i if i is not None else "" for i in x] return np.array(x_no_none, dtype=object) def _cell_to_str_list( m_cell: np.ndarray, empty_value: Optional[str] = None, pattern_split: Optional[Pattern] = None, str_prefix: str = "", ) -> List: """Turn an ndarray (cell) to a list of strings. Parameters ---------- m_cell: np.ndarray empty_value: str, optional What value to replace empty cells with. Default None. pattern_split: Pattern, optional Regular expression to use to split the expression. Used for annotations. Default None. str_prefix: str, optional A prefix that will be added to each value in the list if present. Default "". Returns ------- List A list of processed strings. """ if str_prefix and pattern_split: return [ ( [ str_prefix + str_found if str_prefix not in str_found else str_found for str_found in pattern_split.findall(str(each_cell[0][0])) ] if np.size(each_cell[0]) else empty_value ) for each_cell in m_cell ] elif pattern_split: return [ ( pattern_split.findall(str(each_cell[0][0])) if np.size(each_cell[0]) else empty_value ) for each_cell in m_cell ] else: return [ str(each_cell[0][0]).strip() if np.size(each_cell[0]) else empty_value for each_cell in m_cell ] def _cell_to_float_list( m_cell: np.ndarray, empty_value: Optional[float] = None, inf_value: Optional[float] = None, ) -> List: """Turn an ndarray (cell) to a list of floats. Parameters ---------- m_cell: np.ndarray empty_value: float, optional What value to replace empty cells with. Default None. inf_value: float, optional Replace infinite values with defined inf. Default None (don't replace), will be replaced by given value if given. Returns ------- List A list of processed floats. """ def fix_inf(val: str, _inf_value: float): """Fix inf value, used for rxn.lb and rxn.ub. Parameters ---------- val: str the str to process into float _inf_value: float A value to replace infite values with. """ val = float(val) if np.isinf(val) and val < 0: return -_inf_value elif np.isinf(val) and val > 0: return _inf_value else: return val if inf_value: return [ fix_inf(x[0], _inf_value=inf_value) if np.size(x[0]) else empty_value for x in m_cell ] else: return [float(x[0]) if np.size(x[0]) else empty_value for x in m_cell] def load_matlab_model( infile_path: Union[str, Path, IO], variable_name: Optional[str] = None, inf: float = np.inf, ) -> Model: """Load a cobra model stored as a .mat file. Parameters ---------- infile_path : str or Path or filehandle File path or descriptor of the .mat file describing the cobra model. variable_name : str, optional The variable name of the model in the .mat file. If None, then the first MATLAB variable which looks like a COBRA model will be used (default None). inf: float, optional The value to use for infinite bounds. Some solvers do not handle infinite values so for using those, set this to a high numeric value (default `numpy.inf`). Returns ------- cobra.Model The cobra model as represented in the .mat file. Raises ------ ImportError If scipy is not found in the Python environment. IOError If no COBRA model is found in the .mat file. """ if not scipy_io: raise ImportError("load_matlab_model() requires scipy.") if isinstance(infile_path, str): data = scipy_io.loadmat(infile_path) elif isinstance(infile_path, Path): data = scipy_io.loadmat(infile_path.open("rb")) # noqa W9018 else: data = scipy_io.loadmat(infile_path) # noqa W9018 possible_names = [] if variable_name is None: # skip meta variables meta_vars = {"__globals__", "__header__", "__version__"} possible_names = sorted(i for i in data if i not in meta_vars) if len(possible_names) == 1: variable_name = possible_names[0] elif variable_name is not None: return from_mat_struct(data[variable_name], model_id=variable_name, inf=inf) for possible_name in possible_names: try: return from_mat_struct(data[possible_name], model_id=possible_name, inf=inf) except ValueError as e: print(f"Some problem with the model, causing error {e}") # TODO: use custom cobra exception to handle exception # If code here is executed, then no model was found. raise IOError(f"No COBRA model found at {infile_path}.") def save_matlab_model( model: Model, file_name: Union[str, Path, IO], varname: Optional[str] = None ) -> None: """Save the cobra model as a .mat file. This .mat file can be used directly in cobratoolbox. Parameters ---------- model : cobra.Model The cobra model to represent. file_name : str or file-like or Path File path or descriptor that the MATLAB representation should be written to. varname : str, optional The name of the variable within the MATLAB workspace. Model ID is used if available, else 'exported_model' is used (default None). """ if not scipy_io: raise ImportError("save_matlab_model() requires scipy.") if varname is None: varname = ( str(model.id) if model.id is not None and len(model.id) > 0 else "exported_model" ) mat = create_mat_dict(model) scipy_io.savemat(file_name, {varname: mat}, appendmat=True, oned_as="column") def mat_parse_annotations( target_list: List[Object], mat_struct: np.ndarray, d_replace: str = DICT_MET ) -> None: """Process mat structure annotations in place. Will process mat structured annotations and add them to a list of new entities (metabolites, reactions, genes) in a format based on identifiers.org. Parameters ---------- target_list: list[cobra.Object] A list of cobra objects, including metabolites, reactions or genes. The annotations will be added to these lists. mat_struct: np.ndarray A darray that includes the data imported from matlab file. d_replace: str A string that points to the dictionary of converstions between MATLAB and providers. Default DICT_MET (for metabolite). """ struct_names = [x.casefold() for x in mat_struct.dtype.names] matlab_field_dict = { x.casefold(): DICT_REPLACE[d_replace][x] for x in DICT_REPLACE[d_replace].keys() } caseunfold = {x.casefold(): x for x in mat_struct.dtype.names} annotation_matlab = list(set(struct_names).intersection(matlab_field_dict.keys())) providers = [matlab_field_dict[x] for x in annotation_matlab] annotations = dict.fromkeys(providers, None) for name, mat_key in zip(providers, annotation_matlab): if mat_key == "rxnReferences".casefold(): # This only picks up PMID: style references. Sometimes there are other # things like PMC or OMIM, but those are ignored for now, annotations[name] = _cell_to_str_list( mat_struct[caseunfold[mat_key]][0, 0], None, _pubmed_re ) elif mat_key == "rxnECNumbers".casefold(): # turn EC codes to a list annotations[name] = _cell_to_str_list( mat_struct[caseunfold[mat_key]][0, 0], None, _ec_re ) elif mat_key == "metCHEBIID".casefold(): annotations[name] = _cell_to_str_list( mat_struct[caseunfold[mat_key]][0, 0], None, _chebi_re, "CHEBI:" ) elif mat_key == "metSBOTerms".casefold() or mat_key == "rxnSBOTerms".casefold(): annotations[name] = _cell_to_str_list( mat_struct[caseunfold[mat_key]][0, 0], None, _sbo_re, "SBO:" ) elif mat_key == "metSLMID".casefold(): annotations[name] = _cell_to_str_list( mat_struct[caseunfold[mat_key]][0, 0], None, _sbo_re, "SLM:" ) else: # If it something else, which may have commas, turn it into a list annotations[name] = [ [y.strip() for y in each_cell.split(", ")] if each_cell else None for each_cell in _cell_to_str_list( mat_struct[caseunfold[mat_key]][0, 0] ) ] for i, obj in enumerate(target_list): obj.annotation = { prov: annotations[prov][i] for prov in providers if annotations[prov][i] } # TODO - When cobrapy.notes are revised not be a dictionary (possibly when # annotations are fully SBML compliant, revise this function. def mat_parse_notes( target_list: List[Object], mat_struct: np.ndarray, d_replace: str = DICT_REACTION_NOTES, ) -> None: """Process mat structure notes in place. Will process mat structured notes and add them to a list of new entities (metabolites, reactions, genes) in a format based on identifiers.org. Parameters ---------- target_list: list[cobra.Object] A list of cobra objects, including metabolites, reactions or genes. The notes will be added to these lists. mat_struct: np.ndarray A darray that includes the data imported from matlab file. d_replace: str A string that points to the dictionary of converstions between MATLAB and notes. Default DICT_REACTION_NOTES (for reactions). """ struct_names = [x.casefold() for x in mat_struct.dtype.names] matlab_field_dict = { x.casefold(): DICT_REPLACE[d_replace][x] for x in DICT_REPLACE[d_replace].keys() } caseunfold = {x.casefold(): x for x in mat_struct.dtype.names} annotation_matlab = list(set(struct_names).intersection(matlab_field_dict.keys())) note_providers = [matlab_field_dict[x] for x in annotation_matlab] notes = dict.fromkeys(note_providers, None) for name, mat_key in zip(note_providers, annotation_matlab): if mat_key == "rxnReferences".casefold(): # This only removes PMID: style references. Sometimes there are other # things like PMC or OMIM, but those are placed as string in notes. _notes = _cell_to_str_list(mat_struct[caseunfold[mat_key]][0, 0]) notes[name] = [ ( _pubmed_re.sub("", x).strip() if x and len(_pubmed_re.sub("", x).strip()) else None ) for x in _notes ] elif mat_key == "rxnConfidenceScores".casefold(): notes[name] = [ str(confidence) if confidence is not None else "" for confidence in _cell_to_float_list( mat_struct[caseunfold[mat_key]][0, 0] ) ] else: # If it something else, which may have commas, turn it into a list notes[name] = _cell_to_str_list(mat_struct[caseunfold[mat_key]][0, 0]) notes[name] = [ _punctuation_re.sub("", _double_punctuation_re.sub("", x)) if x else None for x in notes[name] ] for i, obj in enumerate(target_list): obj.notes = {prov: notes[prov][i] for prov in note_providers if notes[prov][i]} def annotations_to_mat( mat_dict: OrderedDict, annotation_list: List[Dict], d_replace: str = DICT_MET_REV ) -> None: """Process mat structure annotations in place. Will process mat structured annotations and add them to a list of new entities (metabolites, reactions, genes) in a format based on identifiers.org. Parameters ---------- mat_dict: OrderedDict An ordered dictionary having model attributes as keys and their respective values represented as arrays, as the values. Annotations will be inserted into this OrderdDict. annotation_list: list[Dict] A list of cobra annotations, in the form of a dictionary. d_replace: str A string that points to the dictionary of converstions between MATLAB and providers. Default DICT_MET_REV (for metabolite). """ providers_used = set() for i in range(len(annotation_list)): providers_used.update(annotation_list[i].keys()) providers_used = providers_used.intersection(DICT_REPLACE[d_replace].keys()) providers_used = list(providers_used) annotation_matlab = {prov: DICT_REPLACE[d_replace][prov] for prov in providers_used} empty_lists = [[""] * len(annotation_list) for _ in annotation_matlab] annotation_cells_to_be = dict(zip(annotation_matlab.values(), empty_lists)) for i in range(len(annotation_list)): for provider_key, obj_annotation in annotation_list[i].items(): if isinstance(obj_annotation, str): obj_annotation = [obj_annotation] if provider_key == "pubmed": obj_annotation = ", ".join( [ "PMID:" + annot if "PMID:" not in annot else annot for annot in obj_annotation ] ) elif provider_key == "CHEBI": obj_annotation = ", ".join( [ "CHEBI:" + annot if "CHBEI:" not in annot else annot for annot in obj_annotation ] ) elif provider_key == "ec-code": obj_annotation = " or ".join(obj_annotation) else: obj_annotation = ", ".join(obj_annotation) if provider_key not in providers_used: continue annotation_cells_to_be[annotation_matlab[provider_key]][i] = obj_annotation for annotation_key, item_list in annotation_cells_to_be.items(): mat_dict[annotation_key] = _cell(item_list) # TODO - When cobrapy.notes are revised not be a dictionary (possibly when # annotations are fully SBML compliant, revise this function. def notes_to_mat( mat_dict: OrderedDict, note_list: List[Dict], d_replace: str = DICT_MET_REV ) -> None: """Process mat structure notes in place. Will process mat structured annotations and add them to a list of new entities (metabolites, reactions, genes) in a format based on identifiers.org. Parameters ---------- mat_dict: OrderedDict An ordered dictionary having model attributes as keys and their respective values represented as arrays, as the values. Annotations will be inserted into this OrderdDict. note_list: list[Dict] A list of cobra annotations, in the form of a dictionary. d_replace: str A string that points to the dictionary of converstions between MATLAB and providers. Default DICT_MET_REV (for metabolite). """ providers_used = set() for i in range(len(note_list)): providers_used.update(note_list[i].keys()) providers_used = providers_used.intersection(DICT_REPLACE[d_replace].keys()) providers_used = list(providers_used) annotation_matlab = {prov: DICT_REPLACE[d_replace][prov] for prov in providers_used} empty_lists = [[""] * len(note_list) for _ in annotation_matlab] annotation_cells_to_be = dict(zip(annotation_matlab.values(), empty_lists)) for i in range(len(note_list)): for provider_key, object_note in note_list[i].items(): if provider_key not in providers_used: continue if provider_key == CONFIDENCE_STR: object_note = float(object_note) if not len(annotation_cells_to_be[annotation_matlab[provider_key]][i]): annotation_cells_to_be[annotation_matlab[provider_key]][i] = object_note else: # References that aren't MIRIAM compliant will go to rxnNotes annotation_cells_to_be[annotation_matlab[provider_key]][i] = ( annotation_cells_to_be[annotation_matlab[provider_key]][i] + f"; {object_note}" ) for annotation_key, item_list in annotation_cells_to_be.items(): mat_dict[annotation_key] = _cell(item_list) def create_mat_dict(model: Model) -> OrderedDict: """Create a dictionary mapping model attributes to arrays. Parameters ---------- model : cobra.Model The model to create dictionary for. Returns ------- OrderedDict The ordered dictionary having model attributes as keys and their respective values represented as arrays, as the values. """ rxns = model.reactions mets = model.metabolites mat = OrderedDict() mat["mets"] = _cell(mets.list_attr("id")) model_has_compartment_names = False if list(model.compartments.keys()) != list(model.compartments.values()): model_has_compartment_names = True if {_get_id_compartment(met_id) for met_id in mets.list_attr("id")} == { None } or model_has_compartment_names: comps = list(model.compartments.keys()) mat["comps"] = _cell(comps) mat["compNames"] = _cell([model.compartments[comp] for comp in comps]) mat["metComps"] = [comps.index(x) + 1 for x in mets.list_attr("compartment")] mat["metNames"] = _cell(mets.list_attr("name")) mat["metFormulas"] = _cell([str(m.formula) if m.formula else "" for m in mets]) try: mat["metCharges"] = np.array(mets.list_attr("charge")) * 1.0 except (TypeError, AttributeError): # can't have any None entries for charge, or this will fail # TODO: use custom cobra exception to handle exception pass annotations_to_mat(mat, mets.list_attr("annotation"), DICT_MET_REV) # TODO - When cobrapy.notes are revised not be a dictionary (possibly when # annotations are fully SBML compliant, revise this function. notes_to_mat(mat, mets.list_attr("notes"), DICT_MET_NOTES_REV) mat["genes"] = _cell(model.genes.list_attr("id")) gene_names = model.genes.list_attr("name") if not all(_name == "" for _name in gene_names): mat["geneNames"] = _cell(gene_names) annotations_to_mat(mat, model.genes.list_attr("annotation"), DICT_GENE_REV) # make a matrix for rxnGeneMat # reactions are rows, genes are columns rxn_gene = scipy_sparse.dok_matrix((len(model.reactions), len(model.genes))) if min(rxn_gene.shape) > 0: for i, reaction in enumerate(model.reactions): for gene in reaction.genes: rxn_gene[i, model.genes.index(gene)] = 1 mat["rxnGeneMat"] = rxn_gene mat["grRules"] = _cell(rxns.list_attr("gene_reaction_rule")) mat["rxns"] = _cell(rxns.list_attr("id")) mat["rxnNames"] = _cell(rxns.list_attr("name")) annotations_to_mat(mat, rxns.list_attr("annotation"), DICT_REACTION_REV) notes_to_mat(mat, rxns.list_attr("notes"), DICT_REACTION_NOTES_REV) rxn_subsystems = _cell(rxns.list_attr("subsystem")) group_ids = model.groups.list_attr("id") group_names = model.groups.list_attr("name") group_ids = [ _id if not _name else _name for _id, _name in zip(group_ids, group_names) ] group_members_list = model.groups.list_attr("members") if set(rxn_subsystems) == {""} and set(group_ids) != {""}: subsystems = [[] for _ in rxns] for group_id, group_members in zip(group_ids, group_members_list): group = model.groups.get_by_id(group_id) if group.kind == "partonomy": for member in group_members: if isinstance(member, Reaction): rxn_ind = model.reactions.index(member) # noinspection PyTypeChecker subsystems[rxn_ind].append(group_id) mat["subSystems"] = _cell( [", ".join(subsystem_list) for subsystem_list in subsystems] ) else: mat["subSystems"] = _cell(rxns.list_attr("subsystem")) stoich_mat = create_stoichiometric_matrix(model, array_type="dok") mat["S"] = stoich_mat if stoich_mat is not None else [[]] # multiply by 1 to convert to float, working around scipy bug # https://github.com/scipy/scipy/issues/4537 mat["lb"] = np.array(rxns.list_attr("lower_bound")) * 1.0 mat["ub"] = np.array(rxns.list_attr("upper_bound")) * 1.0 mat["b"] = np.array(mets.list_attr("_bound")) * 1.0 mat["c"] = np.array(rxns.list_attr("objective_coefficient")) * 1.0 mat["rev"] = np.array(rxns.list_attr("reversibility")) * 1 if model.name: mat["modelName"] = str(model.name) mat["description"] = str(model.id) return mat def from_mat_struct( mat_struct: np.ndarray, model_id: Optional[str] = None, inf: float = np.inf, ) -> Model: """Create a model from the cobratoolbox struct. Parameters ---------- mat_struct : numpy.ndarray The `numpy.ndarray` that most likely contains the model, being chosen by load_matlab_file after loading the matlab structure via scipy_io.loadmat. model_id : str, optional The ID of the model generated. If None, will try to look for ID in model's description. If multiple IDs are found, the first one is used. If no IDs are found, will use 'imported_model' (default None). inf : float, optional The value to use for infinite bounds. Some solvers do not handle infinite values so for using those, set this to a high numeric value (default `numpy.inf`). Returns ------- cobra.Model The model as represented in .mat file. """ m = mat_struct if m.dtype.names is None or {"rxns", "mets", "S", "lb", "ub"} > set(m.dtype.names): raise ValueError("Invalid MATLAB struct.") old_cobratoolbox_fields = [ "confidenceScores", "metCharge", "ecNumbers", "KEGGID", "metSmile", "metHMDB", ] new_cobratoolbox_fields = [ "rxnConfidenceScores", "metCharges", "rxnECNumbers", "metKEGGID", "metSmiles", "metHMDBID", ] for old_field, new_field in zip(old_cobratoolbox_fields, new_cobratoolbox_fields): if old_field in m.dtype.names and new_field not in m.dtype.names: logger.warning( f"This model seems to have {old_field} instead of {new_field} field. " f"Will use {old_field} for what {new_field} represents." ) new_names = list(m.dtype.names) new_names[new_names.index(old_field)] = new_field m.dtype.names = new_names model = Model() if model_id is not None: model.id = model_id elif "description" in m.dtype.names: description = m["description"][0, 0][0] if not isinstance(description, str) and len(description) > 1: model.id = description[0] logger.warning("Several IDs detected, only using the first.") else: model.id = description else: model.id = "imported_model" if "modelName" in m.dtype.names and np.size(m["modelName"][0, 0]): model.name = m["modelName"][0, 0][0] met_ids = _cell_to_str_list(m["mets"][0, 0]) if {"metComps", "comps", "compNames"}.issubset(m.dtype.names): met_comp_index = [x[0] - 1 for x in m["metComps"][0][0]] comps = _cell_to_str_list(m["comps"][0, 0]) comp_names = _cell_to_str_list(m["compNames"][0][0]) met_comps = [comps[i] for i in met_comp_index] met_comp_names = [comp_names[i] for i in met_comp_index] else: logger.warning( f"No defined compartments in model {model.id}. " f"Compartments will be deduced heuristically " f"using regular expressions." ) met_comps = [_get_id_compartment(x) for x in met_ids] met_comp_names = met_comps if None in met_comps or "" in met_comps: raise ValueError("Some compartments were empty. Check the model!") logger.warning( f"Using regular expression found the following compartments:" f"{', '.join(sorted(set(met_comps)))}" ) if None in met_comps or "" in met_comps: raise ValueError("Some compartments were empty. Check the model!") model.compartments = dict(zip(met_comps, met_comp_names)) met_names, met_formulas, met_charges = None, None, None try: met_names = _cell_to_str_list(m["metNames"][0, 0], "") except (IndexError, ValueError): # TODO: use custom cobra exception to handle exception pass try: met_formulas = _cell_to_str_list(m["metFormulas"][0, 0]) except (IndexError, ValueError): # TODO: use custom cobra exception to handle exception pass try: met_charges = _cell_to_float_list(m["metCharges"][0, 0]) except (IndexError, ValueError): # TODO: use custom cobra exception to handle exception pass new_metabolites = [] for i in range(len(met_ids)): new_metabolite = Metabolite(met_ids[i], compartment=met_comps[i]) if met_names: new_metabolite.name = met_names[i] if met_charges: new_metabolite.charge = met_charges[i] if met_formulas: new_metabolite.formula = met_formulas[i] new_metabolites.append(new_metabolite) mat_parse_annotations(new_metabolites, m, d_replace=DICT_MET) mat_parse_notes(new_metabolites, m, d_replace=DICT_MET_NOTES) model.add_metabolites(new_metabolites) if "genes" in m.dtype.names: gene_names = None gene_ids = _cell_to_str_list(m["genes"][0, 0]) try: gene_names = _cell_to_str_list(m["geneNames"][0, 0]) except (IndexError, ValueError): # TODO: use custom cobra exception to handle exception pass new_genes = [ Gene(gene_ids[i], name=gene_names[i]) if gene_names else Gene(gene_ids[i]) for i in range(len(gene_ids)) ] mat_parse_annotations(new_genes, m, d_replace=DICT_GENE) for current_gene in new_genes: current_gene._model = model model.genes += new_genes new_reactions = [] rxn_ids = _cell_to_str_list(m["rxns"][0, 0]) rxn_lbs = _cell_to_float_list(m["lb"][0, 0], empty_value=None, inf_value=inf) rxn_ubs = _cell_to_float_list(m["ub"][0, 0], empty_value=None, inf_value=inf) rxn_gene_rules, rxn_names, rxn_subsystems = None, None, None try: rxn_gene_rules = _cell_to_str_list(m["grRules"][0, 0], "") except (IndexError, ValueError): # TODO: use custom cobra exception to handle exception pass try: rxn_names = _cell_to_str_list(m["rxnNames"][0, 0], "") except (IndexError, ValueError): # TODO: use custom cobra exception to handle exception pass try: # RECON3.0 mat has an array within an array for subsystems. # If we find a model that has multiple subsytems per reaction, this should be # modified if m["subSystems"][0, 0][0][0].dtype.char == "O" and isinstance( m["subSystems"][0, 0][0][0][0], np.ndarray ): rxn_subsystems = [ each_cell[0][0][0][0] if each_cell else "" for each_cell in m["subSystems"][0, 0] ] # Other matlab files seem normal. else: rxn_subsystems = _cell_to_str_list(m["subSystems"][0, 0], "") except (IndexError, ValueError): # TODO: use custom cobra exception to handle exception pass for i in range(len(rxn_ids)): new_reaction = Reaction( id=rxn_ids[i], lower_bound=rxn_lbs[i], upper_bound=rxn_ubs[i] ) if rxn_names: new_reaction.name = rxn_names[i] if rxn_subsystems: new_reaction.subsystem = rxn_subsystems[i] if rxn_gene_rules: new_reaction.gene_reaction_rule = rxn_gene_rules[i] new_reactions.append(new_reaction) mat_parse_annotations(new_reactions, m, d_replace=DICT_REACTION) # TODO - When cobrapy.notes are revised not be a dictionary (possibly when # annotations are fully SBML compliant, revise this function. mat_parse_notes(new_reactions, m, d_replace=DICT_REACTION_NOTES) csc = scipy_sparse.csc_matrix(m["S"][0, 0]) for i in range(csc.shape[1]): stoic_dict = { model.metabolites[j]: csc[j, i] for j in csc.getcol(i).nonzero()[0] } new_reactions[i].add_metabolites(stoic_dict) model.add_reactions(new_reactions) # Make subsystems into groups if rxn_subsystems: rxn_group_names = set(rxn_subsystems).difference({None}) new_groups = [] for g_name in sorted(rxn_group_names): group_members = model.reactions.query( lambda x: x.subsystem == g_name # noqa: B023 ) new_group = Group( id=g_name, name=g_name, members=group_members, kind="partonomy" ) new_group.annotation["sbo"] = "SBO:0000633" new_groups.append(new_group) model.add_groups(new_groups) if "c" in m.dtype.names: c_vec = _cell_to_float_list(m["c"][0, 0]) coefficients = dict(zip(new_reactions, c_vec)) set_objective(model, coefficients) else: logger.warning("Objective vector `c` not found.") if "osenseStr" in m.dtype.names: if isinstance(m["osenseStr"][0, 0][0], np.str_): model.objective_direction = str(m["osenseStr"][0, 0][0]) elif isinstance(m["osenseStr"][0, 0][0], np.ndarray): model.objective_direction = str(m["osenseStr"][0, 0][0][0]) elif "osense" in m.dtype.names: osense = float(m["osense"][0, 0][0][0]) objective_direction_str = "max" if osense == 1: objective_direction_str = "min" model.objective_direction = objective_direction_str return model cobrapy-0.29.1/src/cobra/io/sbml.py000066400000000000000000002142221467301562300170470ustar00rootroot00000000000000""" SBML import and export using python-libsbml. - The SBML importer supports all versions of SBML and the fbc package. - The SBML exporter writes SBML L3 models. - Annotation information is stored on the cobrapy objects - Information from the group package is read Parsing of fbc models was implemented as efficient as possible, whereas (discouraged) fallback solutions are not optimized for efficiency. Notes are only supported in a minimal way relevant for constraint-based models. I.e., structured information from notes in the form

key: value

is read into the Object.notes dictionary when reading SBML files. On writing the Object.notes dictionary is serialized to the SBML notes information. Annotations are read in the Object.annotation fields. Some SBML related issues are still open, please refer to the respective issue: - update annotation format and support qualifiers (depends on decision for new annotation format; https://github.com/opencobra/cobrapy/issues/684) - write compartment annotations and notes (depends on updated first-class compartments; see https://github.com/opencobra/cobrapy/issues/760) - support compression on file handles (depends on solution for https://github.com/opencobra/cobrapy/issues/812) """ import datetime import logging import re from ast import And, BoolOp, Module, Name, Or from collections import defaultdict, namedtuple from copy import deepcopy from io import StringIO from pathlib import Path from sys import platform from typing import IO, Match, Optional, Pattern, Tuple, Type, Union import libsbml import cobra from ..core import GPR, Gene, Group, Metabolite, Model, Reaction from ..manipulation.validate import check_metabolite_compartment_formula from ..util.solver import linear_reaction_coefficients, set_objective class CobraSBMLError(Exception): """SBML error class.""" pass LOGGER = logging.getLogger(__name__) # ----------------------------------------------------------------------------- # Defaults and constants for writing SBML # ----------------------------------------------------------------------------- config = cobra.Configuration() # for default bounds LOWER_BOUND_ID = "cobra_default_lb" UPPER_BOUND_ID = "cobra_default_ub" ZERO_BOUND_ID = "cobra_0_bound" BOUND_MINUS_INF = "minus_inf" BOUND_PLUS_INF = "plus_inf" SBO_FBA_FRAMEWORK = "SBO:0000624" SBO_DEFAULT_FLUX_BOUND = "SBO:0000626" SBO_FLUX_BOUND = "SBO:0000625" SBO_EXCHANGE_REACTION = "SBO:0000627" LONG_SHORT_DIRECTION = {"maximize": "max", "minimize": "min"} SHORT_LONG_DIRECTION = {"min": "minimize", "max": "maximize"} Unit = namedtuple("Unit", ["kind", "scale", "multiplier", "exponent"]) UNITS_FLUX = ( "mmol_per_gDW_per_hr", [ Unit(kind=libsbml.UNIT_KIND_MOLE, scale=-3, multiplier=1, exponent=1), Unit(kind=libsbml.UNIT_KIND_GRAM, scale=0, multiplier=1, exponent=-1), Unit(kind=libsbml.UNIT_KIND_SECOND, scale=0, multiplier=3600, exponent=-1), ], ) # ----------------------------------------------------------------------------- # Functions for id replacements (import/export) # ----------------------------------------------------------------------------- SBML_DOT = "__SBML_DOT__" # ----------------------------------------------------------------------------- # Precompiled note pattern # ----------------------------------------------------------------------------- pattern_notes: Pattern = re.compile( r"<(?P(\w+:)?)p[^>]*>(?P.*?)", re.IGNORECASE | re.DOTALL, ) pattern_to_sbml: Pattern = re.compile(r"([^0-9_a-zA-Z])") pattern_from_sbml: Pattern = re.compile(r"__(\d+)__") def _escape_non_alphanum(nonASCII: Match) -> str: """Convert non alphanumeric to string representation of ascii number. Converts a non alphanumeric character to a string representation of its ascii number using ord. Parameters --------- nonASCII: Match Match object, identified by pattern_from_sbml Returns ------- str: The ascii code, surronded by __. """ return "__" + str(ord(nonASCII.group())) + "__" def _number_to_chr(numberStr: Match) -> str: """Convert an ascii number to a character. Parameters --------- numberStr: Match Match object, identified by pattern_from_sbml Returns ------- str: The first match between the underscores, converted to character. """ return chr(int(numberStr.group(1))) def _clip(sid: str, prefix: str) -> str: """Clip a prefix from the beginning of a string if it exists. Parameters ---------- sid: str String to clip. prefix: str Prefix to remove. Returns ------- str The string with prefix clipped if it existed. Otherwise the original string. """ return sid[len(prefix) :] if sid.startswith(prefix) else sid def _f_gene(sid: str, prefix: str = "G_") -> str: """Clip gene prefix from id. Parameters ---------- sid: str String to process prefix: str, optional Prefix for gene (default "G_"). Returns ------- str Returns modified str with prefix removed, SBML_DOT (see above) replaced with ".", __(NUMBER)__ replaced with the character value of NUMBER. See Also -------- pattern_from_sbml _number_to_chr SBML_DOT """ sid = sid.replace(SBML_DOT, ".") sid = pattern_from_sbml.sub(_number_to_chr, sid) return _clip(sid, prefix) def _f_gene_rev(sid: str, prefix: str = "G_") -> str: """Add gene prefix to id. Parameters ---------- sid: str String to process prefix: str, optional Prefix for gene (default "G_"). Returns ------- str Returns prefix prepended to modified str, with "." replaced with SBML_DOT, non alphanumeric repalced with a string representation of the unicode number. See Also -------- pattern_to_sbml _escape_non_alphanum SBML_DOT """ sid = pattern_to_sbml.sub(_escape_non_alphanum, sid) return prefix + sid.replace(".", SBML_DOT) def _f_specie(sid: str, prefix: str = "M_") -> str: """Clip specie/metabolite prefix from id. Parameters ---------- sid: str String to process prefix: str, optional Prefix for species/metabolite (default "M_"). Returns ------- str Returns modified str with prefix removed, SBML_DOT (see above) replaced with ".", __(NUMBER)__ replaced with the character value of NUMBER. See Also -------- pattern_from_sbml _number_to_chr SBML_DOT """ sid = pattern_from_sbml.sub(_number_to_chr, sid) return _clip(sid, prefix) def _f_specie_rev(sid: str, prefix: str = "M_") -> str: """Add specie/metabolite prefix to id. Parameters ---------- sid: str String to process prefix: str, optional Prefix for metabolite (default "M_"). Returns ------- str Returns prefix prepended to modified str, with "." replaced with SBML_DOT, non alphanumeric repalced with a string representation of the unicode number. See Also -------- pattern_to_sbml _escape_non_alphanum SBML_DOT """ sid = pattern_to_sbml.sub(_escape_non_alphanum, sid) return prefix + sid def _f_reaction(sid: str, prefix: str = "R_") -> str: """Clip reaction prefix from id. Parameters ---------- sid: str String to process prefix: str, optional Prefix for reaction (default "R_"). Returns ------- str Returns modified str with prefix removed, SBML_DOT (see above) replaced with ".", __(NUMBER)__ replaced with the character value of NUMBER. See Also -------- pattern_from_sbml _number_to_chr SBML_DOT """ sid = pattern_from_sbml.sub(_number_to_chr, sid) return _clip(sid, prefix) def _f_reaction_rev(sid: str, prefix: str = "R_") -> str: """Add reaction prefix to id. Parameters ---------- sid: str String to process prefix: str, optional Prefix for reaction (default "R_"). Returns ------- str Returns prefix prepended to modified str, with "." replaced with SBML_DOT, non alphanumeric repalced with a string representation of the unicode number. See Also -------- pattern_to_sbml _escape_non_alphanum SBML_DOT """ sid = pattern_to_sbml.sub(_escape_non_alphanum, sid) return prefix + sid def _f_group(sid: str, prefix: str = "G_") -> str: """Clip group prefix from id. Parameters ---------- sid: str String to process prefix: str, optional Prefix for group (default "G_"). Returns ------- str Returns modified str with prefix removed, SBML_DOT (see above) replaced with ".", __(NUMBER)__ replaced with the character value of NUMBER. See Also -------- pattern_from_sbml _number_to_chr SBML_DOT """ sid = pattern_from_sbml.sub(_number_to_chr, sid) return _clip(sid, prefix) def _f_group_rev(sid: str, prefix: str = "G_") -> str: """Add group prefix to id. Parameters ---------- sid: str String to process prefix: str, optional Prefix for group (default "G_"). Returns ------- str Returns prefix prepended to modified str, with "." replaced with SBML_DOT, non alphanumeric repalced with a string representation of the unicode number. See Also -------- pattern_to_sbml _escape_non_alphanum SBML_DOT """ sid = pattern_to_sbml.sub(_escape_non_alphanum, sid) return prefix + sid F_GENE = "F_GENE" F_GENE_REV = "F_GENE_REV" F_SPECIE = "F_SPECIE" F_SPECIE_REV = "F_SPECIE_REV" F_REACTION = "F_REACTION" F_REACTION_REV = "F_REACTION_REV" F_GROUP = "F_GROUP" F_GROUP_REV = "F_GROUP_REV" F_REPLACE: dict = { F_GENE: _f_gene, F_GENE_REV: _f_gene_rev, F_SPECIE: _f_specie, F_SPECIE_REV: _f_specie_rev, F_REACTION: _f_reaction, F_REACTION_REV: _f_reaction_rev, F_GROUP: _f_group, F_GROUP_REV: _f_group_rev, } # ----------------------------------------------------------------------------- # Read SBML # ----------------------------------------------------------------------------- # noinspection PyDefaultArgument def read_sbml_model( filename: Union[str, IO, Path], number: Type = float, f_replace: dict = F_REPLACE, **kwargs, ) -> Model: """Read SBML model from given filename. If the given filename ends with the suffix ''.gz'' (for example, ''myfile.xml.gz'),' the file is assumed to be compressed in gzip format and will be automatically decompressed upon reading. Similarly, if the given filename ends with ''.zip'' or ''.bz2',' the file is assumed to be compressed in zip or bzip2 format (respectively). Files whose names lack these suffixes will be read uncompressed. Note that if the file is in zip format but the archive contains more than one file, only the first file in the archive will be read and the rest ignored. To read a gzip/zip file, libSBML needs to be configured and linked with the zlib library at compile time. It also needs to be linked with the bzip2 library to read files in bzip2 format. (Both of these are the default configurations for libSBML.) This function supports SBML with FBC-v1 and FBC-v2. FBC-v1 models are converted to FBC-v2 models before reading. The parser tries to fall back to information in notes dictionaries if information is not available in the FBC packages, e.g., CHARGE, FORMULA on species, or GENE_ASSOCIATION, SUBSYSTEM on reactions. Parameters ---------- filename : path to SBML file, or SBML string, or SBML file handle SBML which is read into cobra model number: data type of stoichiometry: {float, int} In which data type should the stoichiometry be parsed. f_replace : dict of replacement functions for id replacement Dictionary of replacement functions for gene, specie, and reaction. By default the following id changes are performed on import: clip G_ from genes, clip M_ from species, clip R_ from reactions If no replacements should be performed, set f_replace={}, None **kwargs: Further keyword arguments are passed on to the called function (_sbml_to_model) Returns ------- cobra.core.Model Raises ------ IOError if file not read All other errors are wrapped around in a message pointing to SBML validator. Notes ----- Provided file handles cannot be opened in binary mode, i.e., use with open(path, "r" as f): read_sbml_model(f) File handles to compressed files are not supported yet. """ try: doc = _get_doc_from_filename(filename) return _sbml_to_model(doc, number=number, f_replace=f_replace, **kwargs) except IOError as e: raise e except Exception as original_error: cobra_error = CobraSBMLError( "Something went wrong reading the SBML model. Most likely the SBML" " model is not valid. Please check that your model is valid using " "the `cobra.io.sbml.validate_sbml_model` function or via the " "online validator at https://sbml.org/validator_servlet/ .\n" "\t`(model, errors) = validate_sbml_model(filename)`" "\nIf the model is valid and cannot be read please open an issue " "at https://github.com/opencobra/cobrapy/issues ." ) raise cobra_error from original_error def _get_doc_from_filename(filename: Union[str, IO, Path]) -> "libsbml.SBMLDocument": """Get SBMLDocument from given filename. Parameters ---------- filename : path to SBML, or SBML string, or filehandle Returns ------- libsbml.SBMLDocument Raises ------ IOError if file not readable or does not contain SBML. CobraSBMLError if input type is not valid. """ if isinstance(filename, Path) and {".bz2", ".gz"}.isdisjoint(filename.suffixes): doc: libsbml.SBMLDocument = libsbml.readSBMLFromString(filename.read_text()) elif isinstance(filename, Path) and {".bz2", ".gz"}.intersection(filename.suffixes): doc: libsbml.SBMLDocument = libsbml.readSBMLFromFile(str(filename)) elif isinstance(filename, str): if " Model: """Create cobra model from SBMLDocument. Parameters ---------- doc: libsbml.SBMLDocument number: data type of stoichiometry: {float, int} In which data type should the stoichiometry be parsed. f_replace : dict dict of replacement functions for id replacement set_missing_bounds : bool flag to set missing bounds. Looks like it will be ignored. **kwargs: Further keyword arguments are passed on. Returns ------- cobra.core.Model Raises ------ CobraSBMLError if no SBML model detected in file. Exception if fbc coversion from v1 to v2 needed and not successful. CobraSBMLError if upper or lower bound are missing from a reaction. CobraSBMLError if objective reaction declared and not found. Objective reaction not declared will lead to an ERROR being logged. """ if f_replace is None: f_replace = {} # SBML model model: "libsbml.Model" = doc.getModel() if model is None: raise CobraSBMLError("No SBML model detected in file.") model_fbc: "libsbml.FbcModelPlugin" = model.getPlugin("fbc") if not model_fbc: LOGGER.warning("Model does not contain SBML fbc package information.") else: if not model_fbc.isSetStrict(): LOGGER.warning('Loading SBML model without fbc:strict="true"') # fbc-v1 (legacy) doc_fbc: "libsbml.FbcSBMLDocumentPlugin" = doc.getPlugin("fbc") fbc_version = doc_fbc.getPackageVersion() if fbc_version == 1: LOGGER.warning( "Loading SBML with fbc-v1 (models should be encoded using fbc-v2)" ) conversion_properties = libsbml.ConversionProperties() conversion_properties.addOption( "convert fbc v1 to fbc v2", True, "Convert FBC-v1 model to FBC-v2" ) result = doc.convert(conversion_properties) if result != libsbml.LIBSBML_OPERATION_SUCCESS: raise Exception("Conversion of SBML fbc v1 to fbc v2 failed") # Model model_id = model.getIdAttribute() if not libsbml.SyntaxChecker.isValidSBMLSId(model_id): LOGGER.error(f"'{model_id}' is not a valid SBML 'SId'.") cobra_model = Model(model_id) cobra_model.name = model.getName() or None # meta information meta = { "model.id": model_id, "level": model.getLevel(), "version": model.getVersion(), "packages": [], } # History creators = [] created = None if model.isSetModelHistory(): history: "libsbml.ModelHistory" = model.getModelHistory() if history.isSetCreatedDate(): created = history.getCreatedDate().getDateAsString() c: "libsbml.ModelCreator" for c in history.getListCreators(): creators.append( { "familyName": c.getFamilyName() if c.isSetFamilyName() else None, "givenName": c.getGivenName() if c.isSetGivenName() else None, "organisation": ( c.getOrganisation() if c.isSetOrganisation() else None ), "email": c.getEmail() if c.isSetEmail() else None, } ) meta["creators"] = creators meta["created"] = created meta["notes"] = _parse_notes_dict(doc) meta["annotation"] = _parse_annotations(doc) info = f"<{model_id}> SBML L{model.getLevel()}V{model.getVersion()}" packages = {} for k in range(doc.getNumPlugins()): plugin: "libsbml.SBasePlugin" = doc.getPlugin(k) key, value = plugin.getPackageName(), plugin.getPackageVersion() packages[key] = value info += f", {key}-v{value}" if key not in ["fbc", "groups", "l3v2extendedmath"]: LOGGER.warning( f"SBML package '{key}' not supported by cobrapy, " f"information is not parsed" ) meta["info"] = info meta["packages"] = packages cobra_model._sbml = meta # notes and annotations cobra_model.notes = _parse_notes_dict(model) cobra_model.annotation = _parse_annotations(model) # Compartments # FIXME: update with new compartments compartments = {} for ( compartment ) in model.getListOfCompartments(): # noqa: E501 type: libsbml.Compartment cid = _check_required(compartment, compartment.getIdAttribute(), "id") compartments[cid] = compartment.getName() cobra_model.compartments = compartments # Species metabolites = [] boundary_metabolites = [] if model.getNumSpecies() == 0: LOGGER.warning("No metabolites in model") specie: "libsbml.Species" for specie in model.getListOfSpecies(): sid = _check_required(specie, specie.getIdAttribute(), "id") if f_replace and F_SPECIE in f_replace: sid = f_replace[F_SPECIE](sid) met = Metabolite(sid) met.name = specie.getName() met.notes = _parse_notes_dict(specie) met.annotation = _parse_annotations(specie) met.compartment = specie.getCompartment() specie_fbc: "libsbml.FbcSpeciesPlugin" = specie.getPlugin("fbc") if specie_fbc: met.charge = specie_fbc.getCharge() met.formula = specie_fbc.getChemicalFormula() or None else: if specie.isSetCharge(): LOGGER.warning( f"Use of the species charge attribute is " f"discouraged, use fbc:charge instead: {specie}" ) met.charge = specie.getCharge() else: if "CHARGE" in met.notes: LOGGER.warning( f"Use of CHARGE in the notes element is " f"discouraged, use fbc:charge instead: {specie}" ) try: met.charge = int(met.notes["CHARGE"]) except ValueError: # handle nan, na, NA, ... pass if "FORMULA" in met.notes: LOGGER.warning( f"Use of FORMULA in the notes element is " f"discouraged, use fbc:chemicalFormula instead: {specie}" ) met.formula = met.notes["FORMULA"] or None # Detect boundary metabolites if specie.getBoundaryCondition() is True: boundary_metabolites.append(met) metabolites.append(met) cobra_model.add_metabolites(metabolites) # Add exchange reactions for boundary metabolites ex_reactions = [] for met in boundary_metabolites: ex_rid = f"EX_{met.id}" ex_reaction = Reaction(ex_rid) ex_reaction.name = ex_rid ex_reaction.annotation = {"sbo": SBO_EXCHANGE_REACTION} ex_reaction.lower_bound = config.lower_bound ex_reaction.upper_bound = config.upper_bound LOGGER.warning( f"Adding exchange reaction {ex_reaction.id} with default bounds for " f"boundary metabolite: {met.id}." ) # species is reactant ex_reaction.add_metabolites({met: -1}) ex_reactions.append(ex_reaction) cobra_model.add_reactions(ex_reactions) # Genes if model_fbc: for ( gp ) in model_fbc.getListOfGeneProducts(): # noqa: E501 type: libsbml.GeneProduct gid = _check_required(gp, gp.getIdAttribute(), "id") if f_replace and F_GENE in f_replace: gid = f_replace[F_GENE](gid) cobra_gene = Gene(gid) cobra_gene.name = gp.getName() if cobra_gene.name is None: cobra_gene.name = gid cobra_gene.annotation = _parse_annotations(gp) cobra_gene.notes = _parse_notes_dict(gp) cobra_model.genes.append(cobra_gene) else: for ( cobra_reaction ) in model.getListOfReactions(): # noqa: E501 type: libsbml.Reaction # fallback to notes information notes = _parse_notes_dict(cobra_reaction) if "GENE ASSOCIATION" in notes: gpr = notes["GENE ASSOCIATION"] elif "GENE_ASSOCIATION" in notes: gpr = notes["GENE_ASSOCIATION"] else: gpr = "" if len(gpr) > 0: gpr = gpr.replace("(", ";") gpr = gpr.replace(")", ";") gpr = gpr.replace("or", ";") gpr = gpr.replace("and", ";") # Interaction of the above replacements can lead to multiple # ;, which results in empty gids gids = [t.strip() for t in gpr.split(";")] gids = set(gids).difference({""}) # create missing genes for gid in gids: if f_replace and F_GENE in f_replace: gid = f_replace[F_GENE](gid) if gid not in cobra_model.genes: cobra_gene = Gene(gid) cobra_gene.name = gid cobra_model.genes.append(cobra_gene) # GPR rules def process_association(ass: "libsbml.FbcAssociation") -> Union[BoolOp, Name]: """Recursively convert gpr association to a GPR class. Defined as inline functions to not pass the replacement dict around. Parameters ---------- ass: libsbml.FbcAssociation Returns ------- BoolOp or Name AST formatted of the FbcAssociation, which will be processed by GPR(). """ if ass.isFbcOr(): return BoolOp( Or(), [process_association(c) for c in ass.getListOfAssociations()] ) elif ass.isFbcAnd(): return BoolOp( And(), [process_association(c) for c in ass.getListOfAssociations()] ) elif ass.isGeneProductRef(): g_id = ass.getGeneProduct() if f_replace and F_GENE in f_replace: return Name(id=f_replace[F_GENE](g_id)) else: return Name(id=g_id) # Reactions missing_bounds = False reactions = [] if model.getNumReactions() == 0: LOGGER.warning("No reactions in model") reaction: "libsbml.Reaction" for reaction in model.getListOfReactions(): rid = _check_required(reaction, reaction.getIdAttribute(), "id") if f_replace and F_REACTION in f_replace: rid = f_replace[F_REACTION](rid) cobra_reaction = Reaction(rid) cobra_reaction.name = reaction.getName().strip() cobra_reaction.annotation = _parse_annotations(reaction) cobra_reaction.notes = _parse_notes_dict(reaction) # set bounds p_ub, p_lb = None, None r_fbc: "libsbml.FbcReactionPlugin" = reaction.getPlugin("fbc") if r_fbc: # bounds in fbc lb_id = r_fbc.getLowerFluxBound() if lb_id: p_lb: "libsbml.Parameter" = model.getParameter(lb_id) if p_lb and p_lb.getConstant() and (p_lb.getValue() is not None): cobra_reaction.lower_bound = p_lb.getValue() else: raise CobraSBMLError( f"No constant bound '{p_lb}' for reaction: {reaction}" ) ub_id = r_fbc.getUpperFluxBound() if ub_id: p_ub: "libsbml.Parameter" = model.getParameter(ub_id) if p_ub and p_ub.getConstant() and (p_ub.getValue() is not None): cobra_reaction.upper_bound = p_ub.getValue() else: raise CobraSBMLError( f"No constant bound '{p_ub}' for reaction: {reaction}" ) elif reaction.isSetKineticLaw(): # some legacy models encode bounds in kinetic laws klaw: "libsbml.KineticLaw" = reaction.getKineticLaw() p_lb = klaw.getParameter( "LOWER_BOUND" ) # noqa: E501 type: libsbml.LocalParameter if p_lb: cobra_reaction.lower_bound = p_lb.getValue() p_ub = klaw.getParameter( "UPPER_BOUND" ) # noqa: E501 type: libsbml.LocalParameter if p_ub: cobra_reaction.upper_bound = p_ub.getValue() if p_ub is not None or p_lb is not None: LOGGER.warning( f"Encoding LOWER_BOUND and UPPER_BOUND in " f"KineticLaw is discouraged, " f"use fbc:fluxBounds instead: {reaction}" ) if p_lb is None: missing_bounds = True lower_bound = config.lower_bound cobra_reaction.lower_bound = lower_bound LOGGER.warning( f"Missing lower flux bound set to '{lower_bound}' for " f"reaction: '{reaction}'" ) if p_ub is None: missing_bounds = True upper_bound = config.upper_bound cobra_reaction.upper_bound = upper_bound LOGGER.warning( f"Missing upper flux bound set to '{upper_bound}' for " f"reaction: '{reaction}'" ) # add reaction reactions.append(cobra_reaction) # parse equation stoichiometry = defaultdict(lambda: 0) for ( sref ) in reaction.getListOfReactants(): # noqa: E501 type: libsbml.SpeciesReference sid = _check_required(sref, sref.getSpecies(), "species") if f_replace and F_SPECIE in f_replace: sid = f_replace[F_SPECIE](sid) stoichiometry[sid] -= number( _check_required(sref, sref.getStoichiometry(), "stoichiometry") ) for ( sref ) in reaction.getListOfProducts(): # noqa: E501 type: libsbml.SpeciesReference sid = _check_required(sref, sref.getSpecies(), "species") if f_replace and F_SPECIE in f_replace: sid = f_replace[F_SPECIE](sid) stoichiometry[sid] += number( _check_required(sref, sref.getStoichiometry(), "stoichiometry") ) # convert to metabolite objects object_stoichiometry = {} for met_id in stoichiometry: metabolite = cobra_model.metabolites.get_by_id(met_id) object_stoichiometry[metabolite] = stoichiometry[met_id] cobra_reaction.add_metabolites(object_stoichiometry) # GPR if r_fbc: gpr = None gpa = ( r_fbc.getGeneProductAssociation() ) # noqa: E501 type: libsbml.GeneProductAssociation if gpa is not None: association = ( gpa.getAssociation() ) # noqa: E501 type: libsbml.FbcAssociation gpr = Module(process_association(association)) cobra_reaction.gpr = GPR(gpr_from=gpr) else: # fallback to notes information notes = cobra_reaction.notes if "GENE ASSOCIATION" in notes: gpr = notes["GENE ASSOCIATION"] elif "GENE_ASSOCIATION" in notes: gpr = notes["GENE_ASSOCIATION"] else: gpr = "" if len(gpr) > 0: LOGGER.warning( f"Use of GENE ASSOCIATION or GENE_ASSOCIATION " f"in the notes element is discouraged, use " f"fbc:gpr instead: {reaction}" ) if f_replace and F_GENE in f_replace: gpr = " ".join(f_replace[F_GENE](t) for t in gpr.split(" ")) cobra_reaction.gpr = GPR.from_string(gpr) cobra_model.add_reactions(reactions) # Objective obj_direction = "max" coefficients = {} if model_fbc: obj_list = ( model_fbc.getListOfObjectives() ) # noqa: E501 type: libsbml.ListOfObjectives if obj_list is None: LOGGER.warning("listOfObjectives element not found") elif obj_list.size() == 0: LOGGER.warning("No objective in listOfObjectives") elif not obj_list.getActiveObjective(): LOGGER.warning("No active objective in listOfObjectives") else: obj_id = obj_list.getActiveObjective() obj: "libsbml.Objective" = model_fbc.getObjective(obj_id) obj_direction = LONG_SHORT_DIRECTION[obj.getType()] for ( flux_obj ) in ( obj.getListOfFluxObjectives() ): # noqa: E501 type: libsbml.FluxObjective rid = flux_obj.getReaction() if f_replace and F_REACTION in f_replace: rid = f_replace[F_REACTION](rid) try: objective_reaction = cobra_model.reactions.get_by_id(rid) except KeyError: raise CobraSBMLError(f"Objective reaction '{rid}' not found") try: coefficients[objective_reaction] = number(flux_obj.getCoefficient()) except ValueError as e: LOGGER.warning(str(e)) else: # some legacy models encode objective coefficients in kinetic laws reaction: "libsbml.Reaction" for reaction in model.getListOfReactions(): if reaction.isSetKineticLaw(): klaw: "libsbml.KineticLaw" = reaction.getKineticLaw() p_oc: "libsbml.LocalParameter" = klaw.getParameter( "OBJECTIVE_COEFFICIENT" ) if p_oc: rid = _check_required(reaction, reaction.getIdAttribute(), "id") if f_replace and F_REACTION in f_replace: rid = f_replace[F_REACTION](rid) try: objective_reaction = cobra_model.reactions.get_by_id(rid) except KeyError: raise CobraSBMLError(f"Objective reaction '{rid}' not found") try: coefficients[objective_reaction] = number(p_oc.getValue()) except ValueError as e: LOGGER.warning(str(e)) LOGGER.warning( "Encoding OBJECTIVE_COEFFICIENT in KineticLaw is discouraged, " f"use fbc:fluxObjective instead: {reaction}" ) if len(coefficients) == 0: LOGGER.error( "No objective coefficients in model. Unclear what should be optimized" ) set_objective(cobra_model, coefficients) cobra_model.solver.objective.direction = obj_direction # parse groups model_groups: "libsbml.GroupsModelPlugin" = model.getPlugin("groups") groups = [] if model_groups: # calculate hashmaps to lookup objects in O(1) sid_map = {} metaid_map = {} for obj_list in [ model.getListOfCompartments(), model.getListOfSpecies(), model.getListOfReactions(), model_groups.getListOfGroups(), ]: sbase: "libsbml.SBase" for sbase in obj_list: if sbase.isSetId(): sid_map[sbase.getIdAttribute()] = sbase if sbase.isSetMetaId(): metaid_map[sbase.getMetaId()] = sbase # create groups group: "libsbml.Group" for group in model_groups.getListOfGroups(): gid = _check_required(group, group.getIdAttribute(), "id") if f_replace and F_GROUP in f_replace: gid = f_replace[F_GROUP](gid) cobra_group = Group(gid) cobra_group.name = group.getName() if group.isSetKind(): cobra_group.kind = group.getKindAsString() cobra_group.annotation = _parse_annotations(group) cobra_group.notes = _parse_notes_dict(group) cobra_members = [] member: "libsbml.Member" for member in group.getListOfMembers(): if member.isSetIdRef(): obj = sid_map[member.getIdRef()] elif member.isSetMetaIdRef(): obj = metaid_map[member.getMetaIdRef()] # noinspection PyUnboundLocalVariable typecode = obj.getTypeCode() obj_id = _check_required(obj, obj.getIdAttribute(), "id") # id replacements cobra_member = None if typecode == libsbml.SBML_SPECIES: if f_replace and F_SPECIE in f_replace: obj_id = f_replace[F_SPECIE](obj_id) cobra_member = cobra_model.metabolites.get_by_id(obj_id) elif typecode == libsbml.SBML_REACTION: if f_replace and F_REACTION in f_replace: obj_id = f_replace[F_REACTION](obj_id) cobra_member = cobra_model.reactions.get_by_id(obj_id) cobra_member.subsystem = group.getName() elif typecode == libsbml.SBML_FBC_GENEPRODUCT: if f_replace and F_GENE in f_replace: obj_id = f_replace[F_GENE](obj_id) cobra_member = cobra_model.genes.get_by_id(obj_id) else: LOGGER.warning( f"Member {member} could not be added to group {group}." f"unsupported type code: {typecode}" ) if cobra_member: cobra_members.append(cobra_member) cobra_group.add_members(cobra_members) groups.append(cobra_group) else: # parse deprecated subsystems on reactions groups_dict = {} for cobra_reaction in cobra_model.reactions: if "SUBSYSTEM" in cobra_reaction.notes: g_name = cobra_reaction.notes["SUBSYSTEM"] cobra_reaction.subsystem = g_name if g_name in groups_dict: groups_dict[g_name].append(cobra_reaction) else: groups_dict[g_name] = [cobra_reaction] for gid, cobra_members in groups_dict.items(): if f_replace and F_GROUP in f_replace: gid = f_replace[F_GROUP](gid) cobra_group = Group(gid, name=gid, kind="partonomy") cobra_group.annotation["sbo"] = "SBO:0000633" cobra_group.add_members(cobra_members) groups.append(cobra_group) cobra_model.add_groups(groups) # general hint for missing flux bounds if missing_bounds: LOGGER.warning( "Missing flux bounds on reactions set to default bounds." "As best practise and to avoid confusion flux bounds " "should be set explicitly on all reactions." ) return cobra_model # ----------------------------------------------------------------------------- # Write SBML # ----------------------------------------------------------------------------- # noinspection PyDefaultArgument def write_sbml_model( cobra_model: Model, filename: Union[str, IO, Path], f_replace: dict = F_REPLACE, **kwargs, ) -> None: """Write cobra model to filename. The created model is SBML level 3 version 1 (L1V3) with fbc package v2 (fbc-v2). If the given filename ends with the suffix ".gz" (for example, "myfile.xml.gz"), libSBML assumes the caller wants the file to be written compressed in gzip format. Similarly, if the given filename ends with ".zip" or ".bz2", libSBML assumes the caller wants the file to be compressed in zip or bzip2 format (respectively). Files whose names lack these suffixes will be written uncompressed. Special considerations for the zip format: If the given filename ends with ".zip", the file placed in the zip archive will have the suffix ".xml" or ".sbml". For example, the file in the zip archive will be named "test.xml" if the given filename is "test.xml.zip" or "test.zip". Similarly, the filename in the archive will be "test.sbml" if the given filename is "test.sbml.zip". Parameters ---------- cobra_model : cobra.core.Model Model instance which is written to SBML filename : string or filehandle path to which the model is written f_replace: dict dictionary of replacement functions for id replacement **kwargs: Further keyword arguments are passed on. """ doc = _model_to_sbml(cobra_model, f_replace=f_replace, **kwargs) if isinstance(filename, str): # write to path libsbml.writeSBMLToFile(doc, filename) elif isinstance(filename, Path): libsbml.writeSBMLToFile(doc, str(filename)) elif hasattr(filename, "write"): # write to file handle sbml_str = libsbml.writeSBMLToString(doc) filename.write(sbml_str) def _model_to_sbml( cobra_model: Model, f_replace: Optional[dict] = None, units: bool = True ) -> libsbml.SBMLDocument: """Convert Cobra model to SBMLDocument. Parameters ---------- cobra_model : cobra.core.Model Cobra model instance f_replace : dict of replacement functions Replacement to apply on identifiers. units : boolean Should the FLUX_UNITS be written in the SBMLDocument. Returns ------- libsbml.SBMLDocument """ if f_replace is None: f_replace = {} sbml_ns = libsbml.SBMLNamespaces(3, 1) # SBML L3V1 sbml_ns.addPackageNamespace("fbc", 2) # fbc-v2 doc: "libsbml.SBMLDocument" = libsbml.SBMLDocument(sbml_ns) doc.setPackageRequired("fbc", False) doc.setSBOTerm(SBO_FBA_FRAMEWORK) model: "libsbml.Model" = doc.createModel() model_fbc: "libsbml.FbcModelPlugin" = model.getPlugin("fbc") model_fbc.setStrict(True) if cobra_model.id is not None: model.setId(cobra_model.id) model.setMetaId("meta_" + cobra_model.id) else: model.setMetaId("meta_model") if cobra_model.name is not None: model.setName(cobra_model.name) # for parsing annotation corresponding to the model _sbase_annotations(model, cobra_model.annotation) # for parsing notes corresponding to the model _sbase_notes_dict(model, cobra_model.notes) # Meta information (ModelHistory) related to SBMLDocument if hasattr(cobra_model, "_sbml"): meta = cobra_model._sbml if "annotation" in meta: _sbase_annotations(doc, meta["annotation"]) if "notes" in meta: _sbase_notes_dict(doc, meta["notes"]) history: "libsbml.ModelHistory" = libsbml.ModelHistory() if "created" in meta and meta["created"]: history.setCreatedDate(libsbml.Date(meta["created"])) else: time = datetime.datetime.now() timestr = time.strftime("%Y-%m-%dT%H:%M:%S") date = libsbml.Date(timestr) _check(history.setCreatedDate(date), "set creation date") _check(history.setModifiedDate(date), "set modified date") if "creators" in meta: for cobra_creator in meta[ "creators" ]: # noqa: E501 type: libsbml.ModelCreator creator = libsbml.ModelCreator() if cobra_creator.get("familyName", None): creator.setFamilyName(cobra_creator["familyName"]) if cobra_creator.get("givenName", None): creator.setGivenName(cobra_creator["givenName"]) if cobra_creator.get("organisation", None): creator.setOrganisation(cobra_creator["organisation"]) if cobra_creator.get("email", None): creator.setEmail(cobra_creator["email"]) _check(history.addCreator(creator), "adding creator to ModelHistory.") # TODO: Will be implemented as part of # https://github.com/opencobra/cobrapy/issues/810 # _check(model.setModelHistory(history), 'set model history') # Units flux_udef = None if units: flux_udef: "libsbml.UnitDefinition" = model.createUnitDefinition() flux_udef.setId(UNITS_FLUX[0]) for u in UNITS_FLUX[1]: unit: "libsbml.Unit" = flux_udef.createUnit() unit.setKind(u.kind) unit.setExponent(u.exponent) unit.setScale(u.scale) unit.setMultiplier(u.multiplier) min_value = config.lower_bound max_value = config.upper_bound _create_parameter( model, pid=LOWER_BOUND_ID, value=min_value, sbo=SBO_DEFAULT_FLUX_BOUND ) _create_parameter( model, pid=UPPER_BOUND_ID, value=max_value, sbo=SBO_DEFAULT_FLUX_BOUND ) _create_parameter(model, pid=ZERO_BOUND_ID, value=0, sbo=SBO_DEFAULT_FLUX_BOUND) _create_parameter( model, pid=BOUND_MINUS_INF, value=-float("Inf"), sbo=SBO_DEFAULT_FLUX_BOUND ) _create_parameter( model, pid=BOUND_PLUS_INF, value=float("Inf"), sbo=SBO_DEFAULT_FLUX_BOUND ) # Compartments # FIXME: use first class compartment model (and write notes & annotations) # (https://github.com/opencobra/cobrapy/issues/811) for cid, name in cobra_model.compartments.items(): compartment: "libsbml.Compartment" = model.createCompartment() compartment.setId(cid) compartment.setName(name) compartment.setConstant(True) # FIXME: write annotations and notes # _sbase_notes(c, com.notes) # _sbase_annotations(c, com.annotation) # Species for metabolite in cobra_model.metabolites: specie: "libsbml.Species" = model.createSpecies() mid = metabolite.id if f_replace and F_SPECIE_REV in f_replace: mid = f_replace[F_SPECIE_REV](mid) specie.setId(mid) specie.setConstant(False) specie.setBoundaryCondition(False) specie.setHasOnlySubstanceUnits(False) specie.setName(metabolite.name) specie.setCompartment(metabolite.compartment) s_fbc: "libsbml.FbcSpeciesPlugin" = specie.getPlugin("fbc") if metabolite.charge is not None: s_fbc.setCharge(metabolite.charge) if metabolite.formula is not None: s_fbc.setChemicalFormula(metabolite.formula) _sbase_annotations(specie, metabolite.annotation) _sbase_notes_dict(specie, metabolite.notes) # Genes for cobra_gene in cobra_model.genes: gp: "libsbml.GeneProduct" = model_fbc.createGeneProduct() gid = cobra_gene.id if f_replace and F_GENE_REV in f_replace: gid = f_replace[F_GENE_REV](gid) gp.setId(gid) gname = cobra_gene.name if gname is None or len(gname) == 0: gname = gid gp.setName(gname) gp.setLabel(gid) _sbase_annotations(gp, cobra_gene.annotation) _sbase_notes_dict(gp, cobra_gene.notes) # Objective objective: "libsbml.Objective" = model_fbc.createObjective() objective.setId("obj") objective.setType(SHORT_LONG_DIRECTION[cobra_model.objective.direction]) model_fbc.setActiveObjectiveId("obj") # Reactions reaction_coefficients = linear_reaction_coefficients(cobra_model) for cobra_reaction in cobra_model.reactions: rid = cobra_reaction.id if f_replace and F_REACTION_REV in f_replace: rid = f_replace[F_REACTION_REV](rid) reaction: "libsbml.Reaction" = model.createReaction() reaction.setId(rid) reaction.setName(cobra_reaction.name) reaction.setFast(False) reaction.setReversible((cobra_reaction.lower_bound < 0)) _sbase_annotations(reaction, cobra_reaction.annotation) _sbase_notes_dict(reaction, cobra_reaction.notes) # stoichiometry for metabolite, stoichiometry in cobra_reaction.metabolites.items(): sid = metabolite.id if f_replace and F_SPECIE_REV in f_replace: sid = f_replace[F_SPECIE_REV](sid) if stoichiometry < 0: sref = ( reaction.createReactant() ) # noqa: E501 type: libsbml.SpeciesReference sref.setSpecies(sid) sref.setStoichiometry(-stoichiometry) sref.setConstant(True) else: sref = ( reaction.createProduct() ) # noqa: E501 type: libsbml.SpeciesReference sref.setSpecies(sid) sref.setStoichiometry(stoichiometry) sref.setConstant(True) # bounds r_fbc: "libsbml.FbcReactionPlugin" = reaction.getPlugin("fbc") r_fbc.setLowerFluxBound( _create_bound( model, cobra_reaction, "lower_bound", f_replace=f_replace, units=units, flux_udef=flux_udef, ) ) r_fbc.setUpperFluxBound( _create_bound( model, cobra_reaction, "upper_bound", f_replace=f_replace, units=units, flux_udef=flux_udef, ) ) # GPR gpr = cobra_reaction.gpr if gpr is not None and gpr.body: # replace ids in string if f_replace and F_GENE_REV in f_replace: idmap = {gid: f_replace[F_GENE_REV](gid) for gid in gpr.genes} gpr_new = gpr.to_string(names=idmap) else: gpr_new = gpr.to_string() gpa = ( r_fbc.createGeneProductAssociation() ) # noqa: E501 type: libsbml.GeneProductAssociation # uses ids to identify GeneProducts (True), # does not create GeneProducts (False) _check(gpa.setAssociation(gpr_new, True, False), "set gpr: " + gpr_new) # objective coefficients if reaction_coefficients.get(cobra_reaction, 0) != 0: flux_obj = ( objective.createFluxObjective() ) # noqa: E501 type: libsbml.FluxObjective flux_obj.setReaction(rid) flux_obj.setCoefficient(cobra_reaction.objective_coefficient) # write groups if len(cobra_model.groups) > 0: doc.enablePackage( "http://www.sbml.org/sbml/level3/version1/groups/version1", "groups", True ) doc.setPackageRequired("groups", False) model_group = model.getPlugin( "groups" ) # noqa: E501 type: libsbml.GroupsModelPlugin for cobra_group in cobra_model.groups: group: "libsbml.Group" = model_group.createGroup() if f_replace and F_GROUP_REV in f_replace: gid = f_replace[F_GROUP_REV](cobra_group.id) else: gid = cobra_group.id group.setId(gid) group.setName(cobra_group.name) group.setKind(cobra_group.kind) _sbase_notes_dict(group, cobra_group.notes) _sbase_annotations(group, cobra_group.annotation) for cobra_member in cobra_group.members: member: "libsbml.Member" = group.createMember() mid = cobra_member.id m_type = str(type(cobra_member)) # id replacements if "Reaction" in m_type: if f_replace and F_REACTION_REV in f_replace: mid = f_replace[F_REACTION_REV](mid) if "Metabolite" in m_type: if f_replace and F_SPECIE_REV in f_replace: mid = f_replace[F_SPECIE_REV](mid) if "Gene" in m_type: if f_replace and F_GENE_REV in f_replace: mid = f_replace[F_GENE_REV](mid) member.setIdRef(mid) if cobra_member.name and len(cobra_member.name) > 0: member.setName(cobra_member.name) return doc def _create_bound( model: libsbml.Model, reaction: Reaction, bound_type: str, f_replace: dict, units: Optional[bool] = None, flux_udef: Optional[libsbml.UnitDefinition] = None, ) -> str: """Create bound in model for given reaction. Adds the parameters for the bounds to the SBML model. Parameters ---------- model : libsbml.Model SBML model instance reaction : cobra.core.Reaction Cobra reaction instance from which the bounds are read. bound_type : {LOWER_BOUND, UPPER_BOUND} Type of bound f_replace : dict of id replacement functions units : bool, optional, defualt None Whether or not to use flux units in the SBML document. flux_udef: libsbml.UnitDefinition, optional Unit definition if units are used. Returns ------- pid: str Id of bound parameter. """ value = getattr(reaction, bound_type) if value == config.lower_bound: return LOWER_BOUND_ID elif value == 0: return ZERO_BOUND_ID elif value == config.upper_bound: return UPPER_BOUND_ID elif value == -float("Inf"): return BOUND_MINUS_INF elif value == float("Inf"): return BOUND_PLUS_INF else: # new parameter rid = reaction.id if f_replace and F_REACTION_REV in f_replace: rid = f_replace[F_REACTION_REV](rid) pid = rid + "_" + bound_type _create_parameter( model, pid=pid, value=value, sbo=SBO_FLUX_BOUND, units=units, flux_udef=flux_udef, ) return pid def _create_parameter( model: libsbml.Model, pid: str, value: float, sbo: Optional[str] = None, constant: Optional[bool] = True, units: Optional[bool] = None, flux_udef: Optional[libsbml.UnitDefinition] = None, ) -> None: """Create parameter in SBML model. Parameters ---------- model : libsbml.Model SBML model instance pid : str Parameter id to create in the SBML model. value: float Value to set parameter sbo: str, optional SBO term for parameter. In COBRA, it seems to be SBO_FLUX_BOUND ( "SBO:0000625") or SBO_DEFAULT_FLUX_BOUND ("SBO:0000626") constant: bool, optional Flag if parameter is constant. units : bool, optional, defualt None Whether or not to use flux units in the SBML document. flux_udef: libsbml.UnitDefinition, optional Unit definition if units are used. """ parameter: "libsbml.Parameter" = model.createParameter() parameter.setId(pid) parameter.setValue(value) parameter.setConstant(constant) if sbo: parameter.setSBOTerm(sbo) if units: parameter.setUnits(flux_udef.getId()) def _check_required(sbase: "libsbml.Base", value: str, attribute: str) -> str: """Get required attribute from SBase. Parameters ---------- sbase : libsbml.SBase value : existing value attribute: name of attribute Returns ------- attribute value (or value if already set) Raises ------ CobraSBMLError if attribute not found or not parsed. """ if (value is None) or (value == ""): msg = ( f"Required attribute '{attribute}' cannot be " f"found or parsed in '{sbase}'." ) if hasattr(sbase, "getId") and sbase.getId(): msg += f" with id '{sbase.getId()}'" elif hasattr(sbase, "getName") and sbase.getName(): msg += f" with name '{sbase.getName()}'" elif hasattr(sbase, "getMetaId") and sbase.getMetaId(): msg += f" with metaId '{sbase.getName()}'" raise CobraSBMLError(msg) if attribute == "id" and not libsbml.SyntaxChecker.isValidSBMLSId(value): LOGGER.error(f"'{value}' is not a valid SBML 'SId'.") return value def _check(value: Union[None, int], message: str) -> None: """ Check the libsbml return value and logs error messages. Parameters ---------- value: None or int message: str If 'value' is None, logs an error message constructed using 'message' and then exits with status code 1. If 'value' is an integer, it assumes it is a libSBML return status code. If the code value is LIBSBML_OPERATION_SUCCESS, returns without further action; if it is not, prints an error message constructed using 'message' along with text from libSBML explaining the meaning of the code, and exits with status code 1. """ if value is None: LOGGER.error( "Error: LibSBML returned a null value trying to <" + message + ">." ) elif type(value) is int: if value == libsbml.LIBSBML_OPERATION_SUCCESS: return else: LOGGER.error("Error encountered trying to <" + message + ">.") LOGGER.error( f"LibSBML error code {str(value)}: " f"{libsbml.OperationReturnValue_toString(value).strip()}" ) else: return # ----------------------------------------------------------------------------- # Notes # ----------------------------------------------------------------------------- def _parse_notes_dict(sbase) -> dict: """Create dictionary of COBRA notes. Parameters ---------- sbase : libsbml.SBase Returns ------- dict of notes """ notes = sbase.getNotesString() if notes and len(notes) > 0: notes_store = {} for match in pattern_notes.finditer(notes): _content = match.group("content") try: # Python 2.7 does not allow keywords for split. # Python 3 can have (":", maxsplit=1) key, value = _content.split(":", maxsplit=1) except ValueError: LOGGER.debug(f"Unexpected content format '{_content}'.") continue notes_store[key.strip()] = value.strip() return {k: v for k, v in notes_store.items() if len(v) > 0} else: return {} def _sbase_notes_dict(sbase: libsbml.SBase, notes: dict) -> None: """Set SBase notes based on dictionary. Parameters ---------- sbase : libsbml.SBase SBML object to set notes on notes : dict Notes information from cobra object. """ if notes and len(notes) > 0: tokens = ( [''] + [f"

{k}: {v}

" for (k, v) in notes.items()] + [""] ) _check( sbase.setNotes("\n".join(tokens)), f"Setting notes on sbase: {sbase}", ) # ----------------------------------------------------------------------------- # Annotations # ----------------------------------------------------------------------------- """ cobra annotations will be dictionaries of the form: object.annotation = { 'provider' : [(qualifier, entity), ...] } A concrete example for a metabolite would look like the following metabolite.annotation = { 'chebi': [(isVersionOf, "CHEBI:17234), (is, "CHEBI:4167),], 'kegg.compound': [(is, "C00031")] } The providers are hereby MIRIAM registry keys for collections https://www.ebi.ac.uk/miriam/main/collections The qualifiers are biomodel qualifiers https://co.mbine.org/standards/qualifiers In the current stage the new annotation format is not completely supported yet. """ URL_IDENTIFIERS_PATTERN = re.compile(r"^https?://identifiers.org/(.+?)[:/](.+)") URL_IDENTIFIERS_PREFIX = "https://identifiers.org" QUALIFIER_TYPES = { "is": libsbml.BQB_IS, "hasPart": libsbml.BQB_HAS_PART, "isPartOf": libsbml.BQB_IS_PART_OF, "isVersionOf": libsbml.BQB_IS_VERSION_OF, "hasVersion": libsbml.BQB_HAS_VERSION, "isHomologTo": libsbml.BQB_IS_HOMOLOG_TO, "isDescribedBy": libsbml.BQB_IS_DESCRIBED_BY, "isEncodedBy": libsbml.BQB_IS_ENCODED_BY, "encodes": libsbml.BQB_ENCODES, "occursIn": libsbml.BQB_OCCURS_IN, "hasProperty": libsbml.BQB_HAS_PROPERTY, "isPropertyOf": libsbml.BQB_IS_PROPERTY_OF, "hasTaxon": libsbml.BQB_HAS_TAXON, "unknown": libsbml.BQB_UNKNOWN, "bqm_is": libsbml.BQM_IS, "bqm_isDescribedBy": libsbml.BQM_IS_DESCRIBED_BY, "bqm_isDerivedFrom": libsbml.BQM_IS_DERIVED_FROM, "bqm_isInstanceOf": libsbml.BQM_IS_INSTANCE_OF, "bqm_hasInstance": libsbml.BQM_HAS_INSTANCE, "bqm_unknown": libsbml.BQM_UNKNOWN, } def _parse_annotations(sbase: libsbml.SBase) -> dict: """Parse cobra annotations from a given SBase object. Annotations are dictionaries with the providers as keys. Parameters ---------- sbase : libsbml.SBase SBase from which the SBML annotations are read Returns ------- dict (annotation dictionary) """ # FIXME: annotation format must be updated (this is a big collection of # fixes) - see: https://github.com/opencobra/cobrapy/issues/684) annotation = {} # SBO term if sbase.isSetSBOTerm(): # FIXME: correct handling of annotations annotation["sbo"] = sbase.getSBOTermID() # RDF annotation cvterms = sbase.getCVTerms() if cvterms is None: return annotation cvterm: "libsbml.CVTerm" for cvterm in cvterms: for k in range(cvterm.getNumResources()): # FIXME: read and store the qualifier uri = cvterm.getResourceURI(k) data = _parse_annotation_info(uri) if data is None: continue else: provider, identifier = data if provider in annotation: if isinstance(annotation[provider], str): annotation[provider] = [annotation[provider]] # FIXME: use a list if identifier not in annotation[provider]: annotation[provider].append(identifier) else: # FIXME: always in list annotation[provider] = identifier return annotation def _parse_annotation_info(uri: str) -> Union[None, Tuple[str, str]]: """Parse provider and term from given identifiers annotation uri. Parameters ---------- uri : str uri (identifiers.org url) Returns ------- (provider, identifier) if resolvable, None otherwise """ match = URL_IDENTIFIERS_PATTERN.match(uri) if match: provider, identifier = match.group(1), match.group(2) if provider.isupper(): identifier = f"{provider}:{identifier}" provider = provider.lower() else: LOGGER.warning( f"{uri} does not conform to " f"'http(s)://identifiers.org/collection/id' or" f"'http(s)://identifiers.org/COLLECTION:id" ) return None return provider, identifier def _sbase_annotations(sbase: libsbml.SBase, annotation: dict) -> None: """Set SBase annotations based on cobra annotations. Parameters ---------- sbase : libsbml.SBase SBML object to annotate annotation : dict, cobra annotation structure cobra object with annotation information Raises ------ CobraSBMLError for unsupported qualifier """ # FIXME: annotation format must be updated # (https://github.com/opencobra/cobrapy/issues/684) if not annotation or len(annotation) == 0: return # standardize annotations annotation_data = deepcopy(annotation) for key, value in annotation_data.items(): # handling of non-string annotations (e.g. integers) if isinstance(value, (float, int)): value = str(value) if isinstance(value, str): annotation_data[key] = [("is", value)] for _key, value in annotation_data.items(): for idx, item in enumerate(value): if isinstance(item, str): value[idx] = ("is", item) # set metaId meta_id = f"meta_{sbase.getId()}" sbase.setMetaId(meta_id) # rdf_items = [] for provider, data in annotation_data.items(): # set SBOTerm if provider in ["SBO", "sbo"]: if provider == "SBO": LOGGER.warning( "'SBO' provider is deprecated, use 'sbo' provider instead" ) sbo_term = data[0][1] _check(sbase.setSBOTerm(sbo_term), f"Setting SBOTerm: {sbo_term}") # FIXME: sbo should also be written as CVTerm continue for item in data: qualifier_str, entity = item[0], item[1] qualifier = QUALIFIER_TYPES.get(qualifier_str, None) if qualifier is None: qualifier = libsbml.BQB_IS LOGGER.error( f"Qualifier type is not supported on annotation: '{qualifier_str}'" ) qualifier_type = libsbml.BIOLOGICAL_QUALIFIER if qualifier_str.startswith("bqm_"): qualifier_type = libsbml.MODEL_QUALIFIER cv: "libsbml.CVTerm" = libsbml.CVTerm() cv.setQualifierType(qualifier_type) if qualifier_type == libsbml.BIOLOGICAL_QUALIFIER: cv.setBiologicalQualifierType(qualifier) elif qualifier_type == libsbml.MODEL_QUALIFIER: cv.setModelQualifierType(qualifier) else: raise CobraSBMLError(f"Unsupported qualifier: {qualifier}") resource = f"{URL_IDENTIFIERS_PREFIX}/{provider}/{entity}" cv.addResource(resource) _check( sbase.addCVTerm(cv), f"Setting cvterm: {cv}, resource: {resource}", ) # ----------------------------------------------------------------------------- # Validation # ----------------------------------------------------------------------------- def validate_sbml_model( filename: Union[str, IO, Path], check_model: bool = True, internal_consistency: bool = True, check_units_consistency: bool = False, check_modeling_practice: bool = False, **kwargs, ) -> Tuple[Optional[Model], dict]: """Validate SBML model and returns the model along with a list of errors. Parameters ---------- filename : str or filehandle The filename (or SBML string) of the SBML model to be validated. check_model: boolean {True, False}, default True Whether to also check some basic model properties such as reaction boundaries and compartment formulas. internal_consistency: bool, optional Check internal consistency (default True). check_units_consistency: bool, optional Check consistency of units (default True). check_modeling_practice: bool, optional Check modeling practise (defualt True). **kwargs: Further keyword arguments are passed on to the called function (_sbml_to_model). Returns ------- (model, errors) model : :class:`~cobra.core.Model.Model` object The cobra model if the file could be read successfully or None otherwise. errors : dict Warnings and errors grouped by their respective types. Notes ----- Errors and warnings are grouped based on their type. SBML_* types are from the libsbml validator. COBRA_* types are from the cobrapy SBML parser. """ keys = ( "SBML_FATAL", "SBML_ERROR", "SBML_SCHEMA_ERROR", "SBML_WARNING", "COBRA_FATAL", "COBRA_ERROR", "COBRA_WARNING", "COBRA_CHECK", ) errors = {key: [] for key in keys} # [1] libsbml validation doc: "libsbml.SBMLDocument" = _get_doc_from_filename(filename) # set checking of units & modeling practise doc.setConsistencyChecks( libsbml.LIBSBML_CAT_UNITS_CONSISTENCY, check_units_consistency ) doc.setConsistencyChecks( libsbml.LIBSBML_CAT_MODELING_PRACTICE, check_modeling_practice ) # check internal consistency if internal_consistency: doc.checkInternalConsistency() doc.checkConsistency() for k in range(doc.getNumErrors()): e: "libsbml.SBMLError" = doc.getError(k) msg = _error_string(e, k=k) sev = e.getSeverity() if sev == libsbml.LIBSBML_SEV_FATAL: errors["SBML_FATAL"].append(msg) elif sev == libsbml.LIBSBML_SEV_ERROR: errors["SBML_ERROR"].append(msg) elif sev == libsbml.LIBSBML_SEV_SCHEMA_ERROR: errors["SBML_SCHEMA_ERROR"].append(msg) elif sev == libsbml.LIBSBML_SEV_WARNING: errors["SBML_WARNING"].append(msg) # [2] cobrapy validation (check that SBML can be read into model) # all warnings generated while loading will be logged as errors log_stream = StringIO() stream_handler = logging.StreamHandler(log_stream) formatter = logging.Formatter("%(levelname)s:%(message)s") stream_handler.setFormatter(formatter) stream_handler.setLevel(logging.INFO) LOGGER.addHandler(stream_handler) LOGGER.propagate = False try: # read model and allow additional parser arguments model = _sbml_to_model(doc, **kwargs) except CobraSBMLError as e: errors["COBRA_ERROR"].append(str(e)) return None, errors except Exception as e: errors["COBRA_FATAL"].append(str(e)) return None, errors cobra_errors = log_stream.getvalue().split("\n") for cobra_error in cobra_errors: tokens = cobra_error.split(":") error_type = tokens[0] error_msg = ":".join(tokens[1:]) if error_type == "WARNING": errors["COBRA_WARNING"].append(error_msg) elif error_type == "ERROR": errors["COBRA_ERROR"].append(error_msg) # remove stream handler LOGGER.removeHandler(stream_handler) LOGGER.propagate = True # [3] additional model tests if check_model: errors["COBRA_CHECK"].extend(check_metabolite_compartment_formula(model)) for key in ["SBML_FATAL", "SBML_ERROR", "SBML_SCHEMA_ERROR"]: if len(errors[key]) > 0: LOGGER.error("SBML errors in validation, check error log for details.") break for key in ["SBML_WARNING"]: if len(errors[key]) > 0: LOGGER.error("SBML warnings in validation, check error log for details.") break for key in ["COBRA_FATAL", "COBRA_ERROR"]: if len(errors[key]) > 0: LOGGER.error("COBRA errors in validation, check error log for details.") break for key in ["COBRA_WARNING", "COBRA_CHECK"]: if len(errors[key]) > 0: LOGGER.error("COBRA warnings in validation, check error log for details.") break return model, errors def _error_string(error: "libsbml.SBMLError", k: Optional[int] = None): """Return string representation of SBMLError. Parameters ---------- error : libsbml.SBMLError k : int, optional index of error (default None). Returns ------- string representation of error """ package = error.getPackage() if package == "": package = "core" error_str = ( f"E{k} ({error.getSeverityAsString()}): " f"{error.getCategoryAsString()} " f"({package}, L{error.getLine()}); " f"{error.getShortMessage()}; {error.getMessage()}" ) return error_str cobrapy-0.29.1/src/cobra/io/schema_v1.json000066400000000000000000000061721467301562300203040ustar00rootroot00000000000000{ "$schema": "http://json-schema.org/draft-04/schema#", "title": "COBRA", "description": "JSON representation of COBRA model", "type": "object", "properties": { "id": { "type": "string" }, "name": { "type": "string" }, "version": { "type": "string", "default": "1" }, "reactions": { "type": "array", "items": { "type": "object", "properties": { "id": { "type": "string" }, "name": { "type": "string" }, "metabolites": { "type": "object", "patternProperties": { ".*": { "type": "number" } } }, "gene_reaction_rule": { "type": "string" }, "lower_bound": { "type": "number" }, "upper_bound": { "type": "number" }, "objective_coefficient": { "type": "number", "default": 0 }, "subsystem": { "type": "string" }, "notes": { "type": "object" }, "annotation": { "type": "object" } }, "required": [ "id", "name", "metabolites", "lower_bound", "upper_bound", "gene_reaction_rule" ], "additionalProperties": false } }, "metabolites": { "type": "array", "items": { "type": "object", "properties": { "id": { "type": "string" }, "name": { "type": "string" }, "compartment": { "type": "string", "pattern": "[a-z]{1,2}" }, "charge": { "type": "integer" }, "formula": { "type": "string" }, "_bound": { "type": "number", "default": 0 }, "notes": { "type": "object" }, "annotation": { "type": "object" } }, "required": [ "id", "name", "compartment" ], "additionalProperties": false } }, "genes": { "type": "array", "items": { "type": "object", "properties": { "id": { "type": "string" }, "name": { "type": "string" }, "notes": { "type": "object" }, "annotation": { "type": "object" } }, "required": [ "id", "name" ], "additionalProperties": false } }, "compartments": { "type": "object", "patternProperties": { "[a-z]{1,2}": { "type": "string" } } }, "notes": { "type": "object" }, "annotation": { "type": "object" } }, "required": [ "id", "reactions", "metabolites", "genes" ], "additionalProperties": false } cobrapy-0.29.1/src/cobra/io/web/000077500000000000000000000000001467301562300163125ustar00rootroot00000000000000cobrapy-0.29.1/src/cobra/io/web/__init__.py000066400000000000000000000003711467301562300204240ustar00rootroot00000000000000"""Provide functionality to access remote model repositories.""" from .abstract_model_repository import AbstractModelRepository from .bigg_models_repository import BiGGModels from .biomodels_repository import BioModels from .load import load_model cobrapy-0.29.1/src/cobra/io/web/abstract_model_repository.py000066400000000000000000000036571467301562300241610ustar00rootroot00000000000000"""Provide an abstract base class that describes a remote model repository.""" from abc import ABC, abstractmethod from typing import Union import httpx from rich.progress import ( BarColumn, DownloadColumn, Progress, TextColumn, TimeRemainingColumn, TransferSpeedColumn, ) class AbstractModelRepository(ABC): """ Define an abstract base class that describes a remote model repository. Attributes ---------- name : str The name of the remote repository. """ _progress = Progress( TextColumn("{task.fields[model_id]}", justify="right"), BarColumn(bar_width=None), "[progress.percentage]{task.percentage:>3.1f}%", DownloadColumn(), TransferSpeedColumn(), TimeRemainingColumn(), ) name: str = "Abstract" def __init__(self, *, url: Union[httpx.URL, str], **kwargs) -> None: """ Initialize the model repository. Parameters ---------- url : httpx.URL or str The base URL from where to load the models. Other Parameters ---------------- kwargs Passed to the parent constructor in order to enable multiple inheritance. """ super().__init__(**kwargs) self._url = httpx.URL(url=url) @property def url(self) -> httpx.URL: """Return the repository's URL.""" return self._url.copy_with() @abstractmethod def get_sbml(self, model_id: str) -> bytes: """ Attempt to download an SBML document from the repository. Parameters ---------- model_id : str The identifier of the desired metabolic model. This is typically repository specific. Returns ------- bytes A gzip-compressed, UTF-8 encoded SBML document. """ raise NotImplementedError("Implement `get_sbml` in a concrete child class.") cobrapy-0.29.1/src/cobra/io/web/bigg_models_repository.py000066400000000000000000000037131467301562300234420ustar00rootroot00000000000000"""Provide a concrete implementation of the BioModels repository interface.""" from io import BytesIO import httpx from .abstract_model_repository import AbstractModelRepository class BiGGModels(AbstractModelRepository): """ Define a concrete implementation of the BiGG Models repository. Attributes ---------- name : str The name of the BiGG Models repository. """ name: str = "BiGG Models" def __init__( self, **kwargs, ) -> None: """ Initialize a BiGG Models repository interface. Other Parameters ---------------- kwargs Passed to the parent constructor in order to enable multiple inheritance. """ super().__init__(url="http://bigg.ucsd.edu/static/models/", **kwargs) def get_sbml(self, model_id: str) -> bytes: """ Attempt to download an SBML document from the repository. Parameters ---------- model_id : str The identifier of the desired metabolic model. This is typically repository specific. Returns ------- bytes A gzip-compressed, UTF-8 encoded SBML document. Raises ------ httpx.HTTPError In case there are any connection problems. """ compressed = BytesIO() filename = f"{model_id}.xml.gz" with self._progress, httpx.stream( method="GET", url=self._url.join(filename) ) as response: response.raise_for_status() task_id = self._progress.add_task( description="download", total=int(response.headers["Content-Length"]), model_id=model_id, ) for chunk in response.iter_bytes(): compressed.write(chunk) self._progress.update(task_id=task_id, advance=len(chunk)) compressed.seek(0) return compressed.read() cobrapy-0.29.1/src/cobra/io/web/biomodels_repository.py000066400000000000000000000053561467301562300231510ustar00rootroot00000000000000"""Provide functions for loading metabolic models over the wire.""" import gzip from io import BytesIO from typing import List import httpx import pydantic from .abstract_model_repository import AbstractModelRepository class BioModelsFile(pydantic.BaseModel): """Define a single BioModels file description.""" name: str size: int = pydantic.Field(alias="fileSize") class BioModelsFilesResponse(pydantic.BaseModel): """Define the BioModels files JSON response.""" main: List[BioModelsFile] = [] class BioModels(AbstractModelRepository): """ Define a concrete implementation of the BioModels repository. Attributes ---------- name : str The name of the BioModels repository. """ name: str = "BioModels" def __init__( self, **kwargs, ) -> None: """ Initialize a BioModels repository interface. Other Parameters ---------------- kwargs Passed to the parent constructor in order to enable multiple inheritance. """ super().__init__(url="https://www.ebi.ac.uk/biomodels/model/", **kwargs) def get_sbml(self, model_id: str) -> bytes: """ Attempt to download an SBML document from the repository. Parameters ---------- model_id : str The identifier of the desired metabolic model. This is typically repository specific. Returns ------- bytes A gzip-compressed, UTF-8 encoded SBML document. Raises ------ httpx.HTTPError In case there are any connection problems. """ data = BytesIO() response = httpx.get( url=self._url.join(f"files/{model_id}"), headers={"Accept": "application/json"}, ) response.raise_for_status() files = BioModelsFilesResponse.parse_obj(response.json()) for model in files.main: if model.name.endswith("xml"): break else: RuntimeError(f"Could not find an SBML document for '{model_id}'.") with self._progress, httpx.stream( method="GET", url=self._url.join(f"download/{model_id}"), params={"filename": model.name}, follow_redirects=True, ) as response: response.raise_for_status() task_id = self._progress.add_task( description="download", total=model.size, model_id=model_id, ) for chunk in response.iter_bytes(): data.write(chunk) self._progress.update(task_id=task_id, advance=len(chunk)) data.seek(0) return gzip.compress(data.read()) cobrapy-0.29.1/src/cobra/io/web/cobrapy_repository.py000066400000000000000000000025221467301562300226230ustar00rootroot00000000000000"""Provide functions for loading metabolic models from local package data.""" import importlib_resources import cobra.data from .abstract_model_repository import AbstractModelRepository class Cobrapy(AbstractModelRepository): """ Define a concrete implementation of the cobrapy (local package) repository. Attributes ---------- name : str The name of the Cobrapy repository. """ name: str = "Cobrapy" def __init__( self, **kwargs, ) -> None: """ Initialize a local Cobrapy repository interface. Other Parameters ---------------- kwargs Passed to the parent constructor in order to enable multiple inheritance. """ super().__init__(url="file://", **kwargs) def get_sbml(self, model_id: str) -> bytes: """ Attempt to open an SBML document from the local repository. Parameters ---------- model_id : str The identifier of the desired metabolic model. This is typically repository specific. Returns ------- bytes A gzip-compressed, UTF-8 encoded SBML document. """ return ( importlib_resources.files(cobra.data) .joinpath(f"{model_id}.xml.gz") .read_bytes() ) cobrapy-0.29.1/src/cobra/io/web/load.py000066400000000000000000000130311467301562300176010ustar00rootroot00000000000000"""Provide a function ``load_model`` to access remote model repositories.""" import gzip import logging from typing import TYPE_CHECKING, Iterable import diskcache import httpx import libsbml from ...core import Configuration from ..sbml import _sbml_to_model from .abstract_model_repository import AbstractModelRepository from .bigg_models_repository import BiGGModels from .biomodels_repository import BioModels from .cobrapy_repository import Cobrapy if TYPE_CHECKING: from cobra.core import Model logger = logging.getLogger(__name__) configuration = Configuration() DEFAULT_REPOSITORIES = ( Cobrapy(), BiGGModels(), BioModels(), ) def load_model( model_id: str, repositories: Iterable[AbstractModelRepository] = DEFAULT_REPOSITORIES, cache: bool = True, ) -> "Model": """ Download an SBML model from a remote repository. Downloaded SBML documents are by default stored in a cache on disk such that future access is much faster. By default, models can be loaded from the following repositories: * BiGG Models * BioModels You can use the ``AbstractModelRepository`` class as a parent to implement your own repository accessor which you pass to the ``load_model`` function. In case you implement a new interface, please consider submitting a pull request to COBRApy. Parameters ---------- model_id : str The identifier of the desired metabolic model. This is typically repository specific. repositories : iterable, optional An iterable of repository accessor instances. The model_id is searched in order. cache : bool, optional Whether or not to use the local caching mechanism (default yes). Returns ------- Model A model instance generated from the SBML document. Raises ------ RuntimeError As with any internet connection, there are multiple errors that can occur. Examples -------- # Most of the time calling `load_model` with an identifier should be enough. >>> print(load_model("e_coli_core")) e_coli_core >>> print(load_model("MODEL1510010000")) MODEL1510010000 See Also -------- BiGGModels BioModels """ if cache: data = _cached_load( model_id=model_id, repositories=repositories, ) else: data = _fetch_model(model_id=model_id, repositories=repositories) return get_model_from_gzip_sbml(data) def _cached_load( model_id: str, repositories: Iterable[AbstractModelRepository], ) -> bytes: """ Attempt to load a gzip-compressed SBML document from the cache. If the given model identifier is not in the cache, the remote repositories are searched. Parameters ---------- model_id : str The identifier of the desired metabolic model. This is typically repository specific. repositories : iterable An iterable of repository accessor instances. The model_id is searched in order. Returns ------- bytes A gzip-compressed, UTF-8 encoded SBML document. """ with diskcache.Cache( directory=str(configuration.cache_directory), size_limit=configuration.max_cache_size, ) as cache: try: return cache[model_id] except KeyError: data = _fetch_model(model_id=model_id, repositories=repositories) cache.set(key=model_id, value=data, expire=configuration.cache_expiration) return data def _fetch_model( model_id: str, repositories: Iterable[AbstractModelRepository], ) -> bytes: """ Attempt to load a gzip-compressed SBML document from the given repositories. Parameters ---------- model_id : str The identifier of the desired metabolic model. This is typically repository specific. repositories : iterable An iterable of repository accessor instances. The model_id is searched in order. Returns ------- bytes A gzip-compressed, UTF-8 encoded SBML document. """ for repository in repositories: logger.info( f"Attempting to fetch '{model_id}' from the {repository.name} repository." ) try: return repository.get_sbml(model_id=model_id) except OSError: logger.debug( f"Model '{model_id} not found in the local " f"repository {repository.name}.'" ) except httpx.HTTPStatusError as error: if error.response.status_code == 404: logger.debug( f"Model '{model_id}' not found in the {repository.name} repository." ) continue raise RuntimeError( f"The connection to the {repository.name} repository failed." ) from error except httpx.RequestError as error: raise RuntimeError( f"The connection to the {repository.name} repository failed." ) from error raise RuntimeError( f"The model '{model_id}' could not be found in any of the repositories." ) def get_model_from_gzip_sbml(stream: bytes) -> "Model": """ Generate a model instance from a gzip-compressed, UTF-8 encoded SBML document. Parameters ---------- stream : bytes A gzip-compressed, UTF-8 encoded SBML document. Returns ------- Model A model instance generated from the SBML document. """ return _sbml_to_model( libsbml.readSBMLFromString(gzip.decompress(stream).decode("utf-8")) ) cobrapy-0.29.1/src/cobra/io/yaml.py000066400000000000000000000102231467301562300170470ustar00rootroot00000000000000"""Provide functions for I/O in YAML format.""" import io from pathlib import Path from typing import TYPE_CHECKING, Any, Dict, Optional, Union from ruamel.yaml.compat import StringIO from ruamel.yaml.main import YAML from .dict import model_from_dict, model_to_dict if TYPE_CHECKING: from io import TextIOBase from cobra import Model YAML_SPEC = "1.2" class CobraYAML(YAML): """Define custom subclass for YAML I/O.""" def dump( self, data: Dict, stream: Optional["TextIOBase"] = None, **kwargs: Any ) -> str: """Dump YAML data. Parameters ---------- data : dict A dictionary representing the cobra model and its components. stream : TextIOBase, optional A text stream object inheriting from `io.TextIOBase`. If None, `ruamel.yaml.compat.StringIO` is used (default None). **kwargs : Any Keyword arguments passed on to `ruamel.yaml.main.YAML.dump`. Returns ------- str YAML string representation of `data`. """ if stream is None: inefficient = True stream = StringIO() else: inefficient = False YAML.dump(self, data, stream, **kwargs) if inefficient: return stream.getvalue() yaml = CobraYAML(typ="rt") def to_yaml(model: "Model", sort: bool = False, **kwargs: Any) -> str: """Return the model as a YAML string. Parameters ---------- model : cobra.Model The cobra model to represent. sort : bool, optional Whether to sort the metabolites, reactions, and genes or maintain the order defined in the model (default False). **kwargs : Any Keyword arguments passed on to `CobraYAML.dump`. Returns ------- str YAML string representation of the cobra model. See Also -------- save_yaml_model : Write directly to a file. ruamel.yaml.dump : Base function. """ obj = model_to_dict(model, sort=sort) obj["version"] = YAML_SPEC return yaml.dump(obj, **kwargs) def from_yaml(document: str) -> "Model": """Load a cobra model from a YAML string. Parameters ---------- document : str The YAML string representation of a cobra model. Returns ------- cobra.Model The cobra model as interpreted from the YAML document. See Also -------- load_yaml_model : Load directly from a file. """ content = StringIO(document) return model_from_dict(yaml.load(content)) def save_yaml_model( model: "Model", filename: str, sort: bool = False, **kwargs: Any ) -> None: """Write the cobra model to a file in YAML format. Parameters ---------- model : cobra.Model The cobra model to represent. filename : str or file-like File path or descriptor that the YAML representation should be written to. sort : bool, optional Whether to sort the metabolites, reactions, and genes or maintain the order defined in the model (default False). **kwargs : Any Keyword arguments passed to `CobraYAML.dump`. See Also -------- to_yaml : Return a string representation. ruamel.yaml.dump : Base function. """ obj = model_to_dict(model, sort=sort) obj["version"] = YAML_SPEC if isinstance(filename, str): with io.open(filename, "w") as file_handle: yaml.dump(obj, file_handle, **kwargs) else: yaml.dump(obj, filename, **kwargs) def load_yaml_model(filename: Union[str, Path]) -> "Model": """Load a cobra model from a file in YAML format. Parameters ---------- filename : str or file-like File path or descriptor that contains the YAML document describing the cobra model. Returns ------- cobra.Model The cobra model as represented in the YAML document. See Also -------- from_yaml : Load from a string. """ if isinstance(filename, (str, Path)): with io.open(filename, "r") as file_handle: return model_from_dict(yaml.load(file_handle)) else: return model_from_dict(yaml.load(filename)) cobrapy-0.29.1/src/cobra/manipulation/000077500000000000000000000000001467301562300176265ustar00rootroot00000000000000cobrapy-0.29.1/src/cobra/manipulation/__init__.py000066400000000000000000000004601467301562300217370ustar00rootroot00000000000000from .annotate import add_SBO from .delete import ( delete_model_genes, knock_out_model_genes, prune_unused_metabolites, prune_unused_reactions, remove_genes, ) from .modify import escape_ID, rename_genes from .validate import check_mass_balance, check_metabolite_compartment_formula cobrapy-0.29.1/src/cobra/manipulation/annotate.py000066400000000000000000000021331467301562300220100ustar00rootroot00000000000000"""Provide function for annotating demand and exchange reactions.""" from typing import TYPE_CHECKING if TYPE_CHECKING: from cobra import Model def add_SBO(model: "Model") -> None: """Add SBO terms for demands and exchanges. This works for models which follow the standard convention for constructing and naming these reactions. The reaction should only contain the single metabolite being exchanged, and the id should be EX_ or DM_ . Parameters ---------- model: cobra.Model The model whose demand and exchange reactions need to be annotated. """ for r in model.reactions: # don't annotate already annotated reactions if r.annotation.get("sbo"): continue # only doing exchanges if len(r.metabolites) != 1: continue met_id = list(r._metabolites)[0].id if r.id.startswith("EX_") and r.id == "EX_" + met_id: r.annotation["sbo"] = "SBO:0000627" elif r.id.startswith("DM_") and r.id == "DM_" + met_id: r.annotation["sbo"] = "SBO:0000628" cobrapy-0.29.1/src/cobra/manipulation/delete.py000066400000000000000000000206761467301562300214550ustar00rootroot00000000000000"""Provide functions for pruning reactions, metabolites and genes.""" import logging from ast import And, BoolOp, Name, NodeTransformer from functools import partial from typing import TYPE_CHECKING, Iterable, List, Optional, Set, Tuple, Union from cobra.util import get_context if TYPE_CHECKING: from cobra import Gene, Metabolite, Model, Reaction logger = logging.getLogger(__name__) def prune_unused_metabolites(model: "Model") -> Tuple["Model", List["Metabolite"]]: """Remove metabolites not involved in any reactions. Parameters ---------- model: cobra.Model The model to remove unused metabolites from. Returns ------- cobra.Model Input model with unused metabolites removed. list of cobra.Metabolite List of metabolites that were removed. """ output_model = model.copy() inactive_metabolites = [ m for m in output_model.metabolites if len(m.reactions) == 0 ] output_model.remove_metabolites(inactive_metabolites) return output_model, inactive_metabolites def prune_unused_reactions(model: "Model") -> Tuple["Model", List["Reaction"]]: """Remove reactions with no assigned metabolites, returns pruned model. Parameters ---------- model: cobra.Model The model to remove unused reactions from. Returns ------- cobra.Model Input model with unused reactions removed. list of cobra.Reaction List of reactions that were removed. """ output_model = model.copy() reactions_to_prune = [r for r in output_model.reactions if len(r.metabolites) == 0] output_model.remove_reactions(reactions_to_prune) return output_model, reactions_to_prune def knock_out_model_genes( model: "Model", gene_list: Iterable[Union["Gene", int, str]], ) -> List["Reaction"]: """Disable the genes in `gene_list`. It sets the bounds to "zero" for reactions catalysed by the genes in `gene_list` if deleting the genes would stop the reactions from proceeding. The changes are reverted upon exit if executed within the model as context. Parameters ---------- model: cobra.Model The model whose reaction bounds are to be set. gene_list: list of cobra.Gene The list of genes to knock-out. Returns ------- list[cobra.Reaction] A list of cobra.Reactions that had the bounds turned to zero. """ rxn_set = set() for gene in model.genes.get_by_any(gene_list): gene.knock_out() rxn_set.update(gene.reactions) return [rxn for rxn in rxn_set if not rxn.functional] def delete_model_genes( model: "Model", gene_list: Union[List["Gene"], Set["Gene"], List[str], Set[str]], cumulative_deletions: bool = True, disable_orphans: bool = False, ) -> None: """Temporarily remove the effect of genes in `gene_list`. It sets the bounds to "zero" for reactions catalysed by the genes in `gene_list` if deleting the genes stops the reactions from proceeding. Parameters ---------- model: cobra.Model The model whose reaction bounds are to be set. gene_list: list of cobra.Gene The list of genes to knock-out. cumulative_deletions: bool, optional If True, then any previous deletions will be maintained in the model (default True). Unused, ignored. disable_orphans: bool, optional If True, then orphan reactions will be disabled. Currently, this is not implemented (default False). Unused, ignored. .. deprecated :: 0.25 Use cobra.manipulation.knock_out_model_genes to simulate knockouts and cobra.manipulation.remove_genes to remove genes from the model. See Also -------- knock_out_model_genes() remove_model_genes() """ logger.warning( "delete_model_genes and undelete_model_genes were deprecated " "and should not be used. undelete_model_genes was removed." "Use cobra.manipulation.remove_genes instead to remove genes " "from the model." ) logger.warning( f"Use cobra.manipulation.knock_out_model_genes({model}, {gene_list})" f" to cause knockouts." ) logger.warning( f"If you want to temporarily knock out genes (like undelete_model_genes) did, " f"run it in model context, for example" f"with({model}):" f" knocked_out_rxns = knock_out_model_genes({model}, {gene_list})" f" since it will be reset when leaving the context." ) class _GeneRemover(NodeTransformer): """ Class to represent a gene set remover. Parameters ---------- target_genes: list or set of cobra.Gene A set of genes to be removed. """ def __init__(self, target_genes: Set["Gene"], **kwargs) -> None: """Initialize a new object. Other Parameters ---------------- kwargs: Further keyword arguments are passed on to the parent class. """ super().__init__(**kwargs) self.target_genes = {str(i) for i in target_genes} def visit_Name(self, node: "Name") -> Optional["Name"]: """Remove a gene. Parameters ---------- node: ast.Name The gene to remove. Returns ------- cobra.Gene or None None if gene object is in `target_genes`. """ return None if node.id in self.target_genes else node def visit_BoolOp(self, node: "BoolOp") -> Optional[Union["BoolOp", "Name"]]: """Rules for boolean operations. Parameters ---------- node: ast.Name The gene to apply rules to. Returns ------- ast.Name or None None if size of Or node values is zero after applying rule, or size of And node values is lower after applying rule. """ original_n = len(node.values) self.generic_visit(node) if len(node.values) == 0: return None # AND with any entities removed if len(node.values) < original_n and isinstance(node.op, And): return None # if one entity in an OR was left, just that entity passed up if len(node.values) == 1: return node.values[0] return node def remove_genes( model: "Model", gene_list: Union[List["Gene"], Set["Gene"], List[str], Union[str]], remove_reactions: bool = True, ) -> None: """Remove genes entirely from the model. This will also simplify all gene-reaction rules with the genes inactivated. Parameters ---------- model: cobra.Model The model to remove genes from. gene_list: list of cobra.Gene or gene ids The list of gene objects to remove. remove_reactions: bool, optional Whether to remove reactions associated with genes in `gene_list` (default True). """ gene_set = {model.genes.get_by_id(str(i)) for i in gene_list} gene_id_set = {i.id for i in gene_set} remover = _GeneRemover(gene_id_set) target_reactions = [] rxns_to_revisit = set() context = get_context(model) for rxn in model.reactions: if rxn.gene_reaction_rule is None or len(rxn.gene_reaction_rule) == 0: continue # reactions to remove if remove_reactions and not rxn.gpr.eval(gene_id_set): target_reactions.append(rxn) else: # if the reaction is not removed, remove the gene # from its gpr old_gpr = rxn.gpr.copy() remover.visit(rxn.gpr) # If the remover completely removed the AST tree from the GPR, it will not # have body at all, which is why this isn't if body is None. if not hasattr(rxn.gpr, "body"): rxn.gpr.body = None rxn._genes = set() else: rxns_to_revisit.add(rxn) if context: context(partial(setattr, rxn, "gpr", old_gpr)) context(partial(rxn.update_genes_from_gpr)) for gene in gene_set: model.genes.remove(gene) gene._model = None if context: context(partial(model.genes.add, gene)) context(partial(setattr, gene, "_model", model)) # remove reference to the gene in all groups associated_groups = model.get_associated_groups(gene) for group in associated_groups: group.remove_members(gene) model.remove_reactions(target_reactions) for rxn in rxns_to_revisit: rxn.update_genes_from_gpr() cobrapy-0.29.1/src/cobra/manipulation/modify.py000066400000000000000000000122471467301562300214750ustar00rootroot00000000000000"""Provide functions to modify model components.""" from ast import NodeTransformer from functools import partial from itertools import chain from typing import TYPE_CHECKING, Dict from cobra.util import get_context if TYPE_CHECKING: from cobra import Gene, Model # Set of tuples of operators and their corresponding textual form _renames = ( (".", "_DOT_"), ("(", "_LPAREN_"), (")", "_RPAREN_"), ("-", "__"), ("[", "_LSQBKT"), ("]", "_RSQBKT"), (",", "_COMMA_"), (":", "_COLON_"), (">", "_GT_"), ("<", "_LT"), ("/", "_FLASH"), ("\\", "_BSLASH"), ("+", "_PLUS_"), ("=", "_EQ_"), (" ", "_SPACE_"), ("'", "_SQUOT_"), ('"', "_DQUOT_"), ) def _escape_str_id(id_str: str) -> str: """Make a single string ID SBML compliant. Parameters ---------- id_str: str The ID string to operate on. Returns ------- str The SBML compliant ID string. """ for c in ("'", '"'): if id_str.startswith(c) and id_str.endswith(c) and id_str.count(c) == 2: id_str = id_str.strip(c) for char, escaped_char in _renames: id_str = id_str.replace(char, escaped_char) return id_str class _GeneEscaper(NodeTransformer): """Class to represent a gene ID escaper.""" def visit_Name(self, node: "Gene") -> "Gene": """Escape string ID. Parameters ---------- node: cobra.Gene The gene object to work on. Returns ------- cobra.Gene The gene object whose ID has been escaped. """ node.id = _escape_str_id(node.id) return node def escape_ID(model: "Model") -> None: """Make all model component object IDs SBML compliant. Parameters ---------- model: cobra.Model The model to operate on. """ for x in chain([model], model.metabolites, model.reactions, model.genes): x.id = _escape_str_id(x.id) gene_renamer = _GeneEscaper() for rxn in model.reactions: if rxn.gpr is not None: gene_renamer.visit(rxn.gpr) model.repair() class _Renamer(NodeTransformer): """ Class to represent a gene renamer. Parameters ---------- rename_dict: dict of {str: str} The dictionary having keys as old gene names and value as new gene names. """ def __init__(self, rename_dict: Dict[str, str], **kwargs) -> None: """Initialize a new object. Other Parameters ---------------- **kwargs: Further keyword arguments are passed on to the parent class. """ super().__init__(**kwargs) self.rename_dict = rename_dict # That's not right def visit_Name(self, node: "Gene") -> "Gene": """Rename a gene. Parameters ---------- node: cobra.Gene The gene to rename. Returns ------- cobra.Gene The renamed gene object. """ node.id = self.rename_dict.get(node.id, node.id) return node def rename_genes(model: "Model", rename_dict: Dict[str, str]) -> None: """Rename genes in a model from the `rename_dict`. Parameters ---------- model: cobra.Model The model to operate on. rename_dict: dict of {str: str} The dictionary having keys as old gene names and value as new gene names. """ recompute_reactions = set() # need to recompute related genes remove_genes = set() context = get_context(model) # Needs to be added first since the history is executed from the tail and # this has to be run last to repair the Gene <-> Reaction mapping if context: for rxn in model.reactions: context(partial(rxn.update_genes_from_gpr)) for old_name, new_name in rename_dict.items(): # undefined if there a value matches a different key try: gene_index = model.genes.index(old_name) except ValueError: continue new_gene_present = new_name in model.genes if new_gene_present: old_gene = model.genes.get_by_id(old_name) # Added in case not renaming some genes: if old_gene is not model.genes.get_by_id(new_name): remove_genes.add(old_gene) recompute_reactions.update(old_gene.reactions) else: # rename old gene to new gene gene = model.genes[gene_index] gene.id = new_name model.genes._generate_index() recompute_reactions.update(gene.reactions) if context: context(model.genes._generate_index) context(partial(setattr, gene, "id", old_name)) gene_renamer = _Renamer(rename_dict) for rxn in recompute_reactions: if rxn.gpr is not None: old_gpr = rxn.gpr.copy() gene_renamer.visit(rxn.gpr) if context: context(partial(setattr, rxn, "_gpr", old_gpr)) model.repair() for i in remove_genes: model.genes.remove(i) i._model = None if context: context(partial(model.genes.add, i)) context(partial(setattr, i, "_model", model)) cobrapy-0.29.1/src/cobra/manipulation/validate.py000066400000000000000000000032621467301562300217740ustar00rootroot00000000000000"""Provide functions for model component validations.""" from typing import TYPE_CHECKING, Dict, List if TYPE_CHECKING: from cobra import Metabolite, Model, Reaction # Set of mass unbalanced SBO terms _NOT_MASS_BALANCED_TERMS = { "SBO:0000627", # EXCHANGE "SBO:0000628", # DEMAND "SBO:0000629", # BIOMASS "SBO:0000631", # PSEUDOREACTION "SBO:0000632", # SINK } def check_mass_balance(model: "Model") -> Dict["Reaction", Dict["Metabolite", float]]: """Check mass balance for reactions of `model` and return unbalanced ones. Parameters ---------- model: cobra.Model The model to perform check on. Returns ------- dict of {cobra.Reaction: dict of {cobra.Metabolite: float}} Returns an empty dict if all components are balanced. """ unbalanced = {} for reaction in model.reactions: if reaction.annotation.get("sbo") not in _NOT_MASS_BALANCED_TERMS: balance = reaction.check_mass_balance() if balance: unbalanced[reaction] = balance return unbalanced def check_metabolite_compartment_formula(model: "Model") -> List[str]: """Check metabolite formulae of `model`. Parameters ---------- model: cobra.Model The model to perform check on. Returns ------- list of str Returns an empty list if no errors are found. """ errors = [] for met in model.metabolites: if met.formula is not None and len(met.formula) > 0: if not met.formula.isalnum(): errors.append( f"Metabolite '{met.id}' formula '{met.formula}' not alphanumeric" ) return errors cobrapy-0.29.1/src/cobra/medium/000077500000000000000000000000001467301562300164065ustar00rootroot00000000000000cobrapy-0.29.1/src/cobra/medium/__init__.py000066400000000000000000000002501467301562300205140ustar00rootroot00000000000000from .boundary_types import ( find_boundary_types, find_external_compartment, is_boundary_type, sbo_terms, ) from .minimal_medium import minimal_medium cobrapy-0.29.1/src/cobra/medium/annotations.py000066400000000000000000000044521467301562300213220ustar00rootroot00000000000000"""Provide lists and annotations for compartment names and reactions. Please send a PR if you want to add something here :) """ # A dictionary having keys as reaction types and keys as prefixes of # reaction IDs that usually indicate that the reaction is *not* a reaction # of the specified type. excludes = { "demand": ["SN_", "SK_", "sink", "EX_", "exchange"], "exchange": [ "demand", "DM_", "biosynthesis", "transcription", "replication", "SN_", "SK_", "sink", ], "sink": [ "demand", "DM_", "biosynthesis", "transcription", "replication", "EX_", "exchange", ], } # A dictionary having SBO term identifiers as values and boundary types # as keys. sbo_terms = { "demand": "SBO:0000628", "exchange": "SBO:0000627", "sink": "SBO:0000632", "biomass": "SBO:0000629", "pseudoreaction": "SBO:0000631", } # A dictionary having keys as common compartment abbreviations and values # as alternative names. compartment_shortlist = { "ce": ["cell envelope"], "c": [ "cytoplasm", "cytosol", "default", "in", "intra cellular", "intracellular", "intracellular region", "intracellular space", "c0", # GAPSEQ "C_c", # CARVEME ], "er": ["endoplasmic reticulum"], "erm": ["endoplasmic reticulum membrane"], "e": [ "extracellular", "extraorganism", "out", "extracellular space", "extra organism", "extra cellular", "extra-organism", "external", "external medium", "e0", # GAPSEQ "C_e", # CARVEME ], "f": ["flagellum", "bacterial-type flagellum"], "g": ["golgi", "golgi apparatus"], "gm": ["golgi membrane"], "h": ["chloroplast"], "l": ["lysosome"], "im": ["mitochondrial intermembrane space"], "mm": ["mitochondrial membrane"], "m": ["mitochondrion", "mitochondria"], "n": ["nucleus"], "p": ["periplasm", "periplasmic space", "p0", "C_p"], # GAPSEQ # CARVEME "x": ["peroxisome", "glyoxysome"], "u": ["thylakoid"], "vm": ["vacuolar membrane"], "v": ["vacuole"], "w": ["cell wall"], "s": ["eyespot", "eyespot apparatus", "stigma"], } cobrapy-0.29.1/src/cobra/medium/boundary_types.py000066400000000000000000000140631467301562300220330ustar00rootroot00000000000000"""Provide functions to identify the type of boundary reactions. This module uses various heuristics to decide whether a boundary reaction is an exchange, demand or sink reaction. It mostly orientates on the following paper: Thiele, I., & Palsson, B. Ø. (2010, January). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. Nature Publishing Group. http://doi.org/10.1038/nprot.2009.203 """ import logging from typing import TYPE_CHECKING, List, Optional import pandas as pd from .annotations import compartment_shortlist, excludes, sbo_terms if TYPE_CHECKING: from cobra import Model, Reaction logger = logging.getLogger(__name__) def find_external_compartment(model: "Model") -> str: """Find the external compartment in the model. Uses a simple heuristic where the external compartment should be the one with the most exchange reactions. Parameters ---------- model : cobra.Model The cobra model whose external compartments are to be identified. Returns ------- str The putative external compartment. Raises ------ RuntimeError If several compartments are similar and thus difficult to identify, or, recognized names usually used for external compartment are absent. """ if model.boundary: counts = pd.Series(tuple(r.compartments)[0] for r in model.boundary) most = counts.value_counts() most = most.index[most == most.max()].to_series() else: most = None like_external = compartment_shortlist["e"] + ["e"] matches = pd.Series( [co in like_external for co in model.compartments], dtype=bool, index=model.compartments, ) if matches.sum() == 1: compartment = matches.index[matches][0] logger.info( f"Compartment `{compartment}` sounds like an external compartment. " "Using this one without counting boundary reactions." ) return compartment elif most is not None and matches.sum() > 1 and matches[most].sum() == 1: compartment = most[matches[most]][0] logger.warning( "There are several compartments that look like an " f"external compartment but `{compartment}` has the most boundary " "reactions, so using that as the external compartment." ) return compartment elif matches.sum() > 1: raise RuntimeError( "There are several compartments that look " "like external compartments but we can't tell " "which one to use. Consider renaming your " "compartments please." ) if most is not None: logger.warning( "Could not identify an external compartment by name and " "choosing one with the most boundary reactions. That " "might be complete nonsense or change suddenly. " "Consider renaming your compartments using " "`Model.compartments` to fix this." ) return most.iloc[0] # No info in the model, so give up raise RuntimeError( "The heuristic for discovering an external compartment " "relies on names and boundary reactions. Yet, there " "are neither compartments with recognized names nor " "boundary reactions in the model." ) def is_boundary_type( reaction: "Reaction", boundary_type: str, external_compartment: str ) -> bool: """Check whether a reaction is an exchange reaction. Parameters ---------- reaction : cobra.Reaction The reaction to check. boundary_type : {"exchange", "demand", "sink"} Boundary type to check for. external_compartment : str The ID for the external compartment. Returns ------- bool Whether the reaction looks like the requested type. Might be based on a heuristic. """ # Check if the reaction has an annotation. Annotations dominate everything. sbo_term = reaction.annotation.get("sbo", "") if isinstance(sbo_term, list): sbo_term = sbo_term[0] sbo_term = sbo_term.upper() if sbo_term == sbo_terms[boundary_type]: return True if sbo_term in [sbo_terms[k] for k in sbo_terms if k != boundary_type]: return False # Check if the reaction is in the correct compartment (exterior or inside) correct_compartment = external_compartment in reaction.compartments if boundary_type != "exchange": correct_compartment = not correct_compartment # Check if the reaction has the correct reversibility rev_type = True if boundary_type == "demand": rev_type = not reaction.reversibility elif boundary_type == "sink": rev_type = reaction.reversibility return ( reaction.boundary and not any(ex in reaction.id for ex in excludes[boundary_type]) and correct_compartment and rev_type ) def find_boundary_types( model: "Model", boundary_type: str, external_compartment: Optional[str] = None ) -> List["Reaction"]: """Find specific boundary reactions. Parameters ---------- model : cobra.Model The cobra model whose boundary reactions are to be found. boundary_type : {"exchange", "demand", "sink"} Boundary type to check for. external_compartment : str, optional The ID for the external compartment. If None, it will be detected automatically (default None). Returns ------- list of cobra.Reaction or an empty list A list of likely boundary reactions of a user defined type. """ if not model.boundary: logger.warning( "There are no boundary reactions in this model. " "Therefore, specific types of boundary reactions such " "as 'exchanges', 'demands' or 'sinks' cannot be " "identified." ) return [] if external_compartment is None: external_compartment = find_external_compartment(model) return model.reactions.query( lambda r: is_boundary_type(r, boundary_type, external_compartment) ) cobrapy-0.29.1/src/cobra/medium/minimal_medium.py000066400000000000000000000227531467301562300217570ustar00rootroot00000000000000"""Provide functions and helpers to obtain minimal growth media.""" import logging from typing import TYPE_CHECKING, Iterable, Union import pandas as pd from optlang.interface import OPTIMAL from optlang.symbolics import Zero from .boundary_types import find_boundary_types if TYPE_CHECKING: from cobra import Model, Reaction logger = logging.getLogger(__name__) def add_linear_obj(model: "Model") -> None: r"""Add a linear version of a minimal medium to the model solver. Changes the optimization objective to finding the growth medium requiring the smallest total import flux: ..math:: minimize \sum_{r_i in import_reactions} |r_i| Parameters ---------- model : cobra.Model The cobra model to modify. """ coefs = {} for rxn in find_boundary_types(model, "exchange"): export = len(rxn.reactants) == 1 if export: coefs[rxn.reverse_variable] = 1 else: coefs[rxn.forward_variable] = 1 model.objective.set_linear_coefficients(coefs) model.objective.direction = "min" def add_mip_obj(model: "Model") -> None: """Add a mixed-integer version of a minimal medium to the model. Changes the optimization objective to finding the medium with the least components: minimize size(R) where R part of import_reactions Arguments --------- model : cobra.model The model to modify. """ if len(model.variables) > 1e4: logger.warning( "The MIP version of minimal media is extremely slow for " "models that large :(" ) exchange_rxns = find_boundary_types(model, "exchange") big_m = max(abs(b) for r in exchange_rxns for b in r.bounds) prob = model.problem coefs = {} to_add = [] for rxn in exchange_rxns: export = len(rxn.reactants) == 1 indicator = prob.Variable("ind_" + rxn.id, lb=0, ub=1, type="binary") if export: vrv = rxn.reverse_variable indicator_const = prob.Constraint( vrv - indicator * big_m, ub=0, name="ind_constraint_" + rxn.id ) else: vfw = rxn.forward_variable indicator_const = prob.Constraint( vfw - indicator * big_m, ub=0, name="ind_constraint_" + rxn.id ) to_add.extend([indicator, indicator_const]) coefs[indicator] = 1 model.add_cons_vars(to_add) model.solver.update() model.objective.set_linear_coefficients(coefs) model.objective.direction = "min" def _as_medium( exchanges: Iterable["Reaction"], tolerance: float = 1e-6, exports: bool = False ) -> pd.Series: """Convert a solution to medium. Parameters ---------- exchanges : list of cobra.reaction The exchange reactions to consider. tolerance : float > 0, optional The absolute tolerance for fluxes. Fluxes with an absolute value smaller than this number will be ignored (default 1e-6). exports : bool, optional Whether to return export fluxes as well (default False). Returns ------- pandas.Series The "medium", meaning all active import fluxes in the solution. """ logger.debug("Formatting medium.") medium = pd.Series() for rxn in exchanges: export = len(rxn.reactants) == 1 flux = rxn.flux if abs(flux) < tolerance: continue if export: medium[rxn.id] = -flux elif not export: medium[rxn.id] = flux if not exports: medium = medium[medium > 0] return medium def minimal_medium( model: "Model", min_objective_value: float = 0.1, exports: bool = False, minimize_components: Union[bool, int] = False, open_exchanges: bool = False, ) -> Union[pd.Series, pd.DataFrame, None]: """Find the minimal growth medium for the `model`. Finds the minimal growth medium for the `model` which allows for model as well as individual growth. Here, a minimal medium can either be the medium requiring the smallest total import flux or the medium requiring the least components (ergo ingredients), which will be much slower due to being a mixed integer problem (MIP). Parameters ---------- model : cobra.model The model to modify. min_objective_value : float > 0 or array-like object, optional The minimum growth rate (objective) that has to be achieved (default 0.1). exports : bool, optional Whether to include export fluxes in the returned medium. Defaults to False which will only return import fluxes (default False). minimize_components : bool or int > 0, optional Whether to minimize the number of components instead of the total import flux. Might be more intuitive if set to True, but may also be slow to calculate for large communities. If set to a number `n` will return up to `n` alternative solutions all with the same number of components (default False). open_exchanges : bool or number, optional Whether to ignore currently set bounds and make all exchange reactions in the `model` possible. If set to a `number`, all exchange reactions will be opened with (-`number`, `number`) as bounds (default False). Returns ------- pandas.Series, pandas.DataFrame or None A pandas.Series giving the import flux for each required import reaction and (optionally) the associated export fluxes. All exchange fluxes are oriented into the import reaction e.g. positive fluxes denote imports and negative fluxes exports. If `minimize_components` is a number larger than 1, may return a pandas.DataFrame where each column is a minimal medium. Returns None, if the minimization is infeasible (for instance if min_growth > maximum growth rate). Notes ----- Due to numerical issues, the `minimize_components` option will usually only minimize the number of "large" import fluxes. Specifically, the detection limit is given by ``integrality_tolerance * max_bound`` where ``max_bound`` is the largest bound on an import reaction. Thus, if you are interested in small import fluxes as well you may have to adjust the solver tolerance at first with `model.tolerance = 1e-7` for instance. However, this will be *very* slow for large models especially with GLPK. """ exchange_rxns = find_boundary_types(model, "exchange") if isinstance(open_exchanges, bool): open_bound = 1000 else: open_bound = open_exchanges with model as mod: if open_exchanges: logger.debug(f"Opening exchanges for {len(exchange_rxns)} imports.") for rxn in exchange_rxns: rxn.bounds = (-open_bound, open_bound) logger.debug("Applying objective value constraints.") obj_const = mod.problem.Constraint( mod.objective.expression, lb=min_objective_value, name="medium_obj_constraint", ) mod.add_cons_vars([obj_const]) mod.solver.update() mod.objective = Zero logger.debug("Adding new media objective.") tol = mod.solver.configuration.tolerances.feasibility if minimize_components: add_mip_obj(mod) if isinstance(minimize_components, bool): minimize_components = 1 seen = set() best = num_components = mod.slim_optimize() if mod.solver.status != OPTIMAL: logger.warning("Minimization of medium was infeasible.") return None exclusion = mod.problem.Constraint(Zero, ub=0) mod.add_cons_vars([exclusion]) mod.solver.update() media = [] for i in range(minimize_components): logger.info(f"Finding alternative medium #{(i + 1)}.") vars = [mod.variables["ind_" + s] for s in seen] if len(seen) > 0: exclusion.set_linear_coefficients(dict.fromkeys(vars, 1)) exclusion.ub = best - 1 num_components = mod.slim_optimize() if mod.solver.status != OPTIMAL or num_components > best: if i == 0: logger.warning( "Could not get an optimal solution. " "This is usually due to numerical instability. " "Possible remedies are relaoding the model " "from scratch, switching to a different solver, " "or decreasing the solver tolerance. Please, " "carefully read the note on numerical instability " "in the function documentation." ) return None break medium = _as_medium(exchange_rxns, tol, exports=exports) media.append(medium) seen.update(medium[medium > 0].index) if len(media) > 1: medium = pd.concat(media, axis=1, sort=True).fillna(0.0) medium.sort_index(axis=1, inplace=True) else: medium = media[0] else: add_linear_obj(mod) mod.slim_optimize() if mod.solver.status != OPTIMAL: logger.warning("Minimization of medium was infeasible.") return None medium = _as_medium(exchange_rxns, tol, exports=exports) return medium cobrapy-0.29.1/src/cobra/sampling/000077500000000000000000000000001467301562300167405ustar00rootroot00000000000000cobrapy-0.29.1/src/cobra/sampling/__init__.py000066400000000000000000000002451467301562300210520ustar00rootroot00000000000000from .hr_sampler import HRSampler, shared_np_array from .achr import ACHRSampler from .core import step from .optgp import OptGPSampler from .sampling import sample cobrapy-0.29.1/src/cobra/sampling/achr.py000066400000000000000000000135131467301562300202320ustar00rootroot00000000000000"""Provide the ACHR sampler class.""" from typing import TYPE_CHECKING, Optional import numpy as np import pandas as pd from .core import step from .hr_sampler import HRSampler if TYPE_CHECKING: from cobra import Model class ACHRSampler(HRSampler): """ Artificial Centering Hit-and-Run sampler. A sampler with low memory footprint and good convergence. Parameters ---------- model : cobra.Model The cobra model from which to generate samples. thinning : int, optional The thinning factor of the generated sampling chain. A thinning of 10 means samples are returned every 10 steps (default 100). nproj : int > 0, optional How often to reproject the sampling point into the feasibility space. Avoids numerical issues at the cost of lower sampling. If you observe many equality constraint violations with `sampler.validate` you should lower this number (default None). seed : int > 0, optional Sets the random number seed. Initialized to the current time stamp if None (default None). Attributes ---------- n_samples : int The total number of samples that have been generated by this sampler instance. problem : typing.NamedTuple A NamedTuple whose attributes define the entire sampling problem in matrix form. warmup : numpy.matrix A numpy matrix with as many columns as reactions in the model and more than 3 rows containing a warmup sample in each row. None if no warmup points have been generated yet. retries : int The overall of sampling retries the sampler has observed. Larger values indicate numerical instabilities. fwd_idx : numpy.array A numpy array having one entry for each reaction in the model, containing the index of the respective forward variable. rev_idx : numpy.array A numpy array having one entry for each reaction in the model, containing the index of the respective reverse variable. prev : numpy.array The current/last flux sample generated. center : numpy.array The center of the sampling space as estimated by the mean of all previously generated samples. Notes ----- ACHR generates samples by choosing new directions from the sampling space's center and the warmup points. The implementation used here is the same as in the MATLAB COBRA Toolbox [2]_ and uses only the initial warmup points to generate new directions and not any other previous iterations. This usually gives better mixing, since the startup points are chosen to span the space in a wide manner. This also makes the generated sampling chain quasi-Markovian since the center converges rapidly. Memory usage is roughly in the order of (2 * number of reactions) ^ 2 due to the required nullspace matrices and warmup points. So, large models easily take up a few GBs of RAM. References ---------- .. [1] Direction Choice for Accelerated Convergence in Hit-and-Run Sampling David E. Kaufman, Robert L. Smith Operations Research 199846:1 , 84-95 https://doi.org/10.1287/opre.46.1.84 .. [2] https://github.com/opencobra/cobratoolbox """ def __init__( self, model: "Model", thinning: int = 100, nproj: Optional[int] = None, seed: Optional[int] = None, **kwargs, ) -> None: """Initialize a new ACHRSampler.""" super().__init__(model, thinning, nproj=nproj, seed=seed, **kwargs) self.generate_fva_warmup() self.prev = self.center = self.warmup.mean(axis=0) np.random.seed(self._seed) def __single_iteration(self) -> None: """Run a single iteration of the sampling.""" pi = np.random.randint(self.n_warmup) # mix in the original warmup points to not get stuck delta = self.warmup[pi, :] - self.center self.prev = step(self, self.prev, delta) if self.problem.homogeneous and ( self.n_samples * self.thinning % self.nproj == 0 ): self.prev = self._reproject(self.prev) self.center = self._reproject(self.center) self.center = (self.n_samples * self.center) / ( self.n_samples + 1 ) + self.prev / (self.n_samples + 1) self.n_samples += 1 def sample(self, n: int, fluxes: bool = True) -> pd.DataFrame: """Generate a set of samples. This is the basic sampling function for all hit-and-run samplers. Parameters ---------- n : int The number of samples that are generated at once. fluxes : bool, optional Whether to return fluxes or the internal solver variables. If set to False, will return a variable for each forward and backward flux as well as all additional variables you might have defined in the model (default True). Returns ------- pandas.DataFrame Returns a pandas DataFrame with `n` rows, each containing a flux sample. Notes ----- Performance of this function linearly depends on the number of reactions in your model and the thinning factor. """ samples = np.zeros((n, self.warmup.shape[1])) for i in range(1, self.thinning * n + 1): self.__single_iteration() if i % self.thinning == 0: samples[i // self.thinning - 1, :] = self.prev if fluxes: names = [r.id for r in self.model.reactions] return pd.DataFrame( samples[:, self.fwd_idx] - samples[:, self.rev_idx], columns=names, ) else: names = [v.name for v in self.model.variables] return pd.DataFrame(samples, columns=names) cobrapy-0.29.1/src/cobra/sampling/core.py000066400000000000000000000070571467301562300202530ustar00rootroot00000000000000"""Provide low-level sampling stepper functions and helpers.""" import logging from typing import Optional import numpy as np from .hr_sampler import HRSampler logger = logging.getLogger(__name__) # Maximum number of retries for sampling MAX_TRIES = 100 def step( sampler: HRSampler, x: np.ndarray, delta: np.ndarray, fraction: Optional[float] = None, tries: int = 0, ) -> np.ndarray: """Sample a new feasible point from the point `x` in direction `delta`. This is the low-level sampling stepper for samplers derived from `HRSampler`. Currently, it's used by `ACHRSampler` and `OptGPSampler`. It's declared outside of the base sampling class to facilitate use of multiprocessing. Parameters ---------- sampler : cobra.sampling.HRSampler The sampler to sample a step for. x : np.array A point in the sampling region. delta : np.array The direction to take the step in. fraction : float, optional A float controlling the part of alpha difference to contribute to the fraction of `delta` (default None). If None, alpha is obtained from a normal distribution. tries : int, optional Total number of tries (default 0). Returns ------- np.array The new numpy array obtained after a step of sampling. Raises ------ RuntimeError If `tries` exceeds `MAX_TRIES`. """ prob = sampler.problem valid = (np.abs(delta) > sampler.feasibility_tol) & np.logical_not( prob.variable_fixed ) # permissible alphas for staying in variable bounds valphas = ((1.0 - sampler.bounds_tol) * prob.variable_bounds - x)[:, valid] valphas = (valphas / delta[valid]).flatten() if prob.bounds.shape[0] > 0: # permissible alphas for staying in constraint bounds ineqs = prob.inequalities.dot(delta) valid = np.abs(ineqs) > sampler.feasibility_tol balphas = ((1.0 - sampler.bounds_tol) * prob.bounds - prob.inequalities.dot(x))[ :, valid ] balphas = (balphas / ineqs[valid]).flatten() # combined alphas alphas = np.hstack([valphas, balphas]) else: alphas = valphas pos_alphas = alphas[alphas > 0.0] neg_alphas = alphas[alphas <= 0.0] alpha_range = np.array( [ neg_alphas.max() if len(neg_alphas) > 0 else 0, pos_alphas.min() if len(pos_alphas) > 0 else 0, ] ) if fraction: alpha = alpha_range[0] + fraction * (alpha_range[1] - alpha_range[0]) else: alpha = np.random.uniform(alpha_range[0], alpha_range[1]) p = x + alpha * delta # Numerical instabilities may cause bounds invalidation # reset sampler and sample from one of the original warmup directions # if that occurs. Also reset if we got stuck. if ( np.any(sampler._bounds_dist(p) < -sampler.bounds_tol) or np.abs(np.abs(alpha_range).max() * delta).max() < sampler.bounds_tol ): if tries > MAX_TRIES: raise RuntimeError( "Cannot escape sampling region, model seems to be " "numerically unstable. Reporting the model to " "https://github.com/opencobra/cobrapy/issues " "will help us to fix this." ) logger.info("Found bounds infeasibility in sample, resetting to center.") newdir = sampler.warmup[np.random.randint(sampler.n_warmup)] sampler.retries += 1 return step(sampler, sampler.center, newdir - sampler.center, None, tries + 1) return p cobrapy-0.29.1/src/cobra/sampling/hr_sampler.py000066400000000000000000000465251467301562300214620ustar00rootroot00000000000000"""Provide the base class and associated functions for Hit-and-Run samplers.""" import ctypes import logging from abc import ABC, abstractmethod from multiprocessing import Array from time import time from typing import TYPE_CHECKING, NamedTuple, Optional, Tuple import numpy as np import pandas as pd from optlang.interface import OPTIMAL from optlang.symbolics import Zero from cobra.util import constraint_matrices, create_stoichiometric_matrix, nullspace if TYPE_CHECKING: from cobra import Model logger = logging.getLogger(__name__) Problem = NamedTuple( "Problem", [ ("equalities", np.ndarray), ("b", np.ndarray), ("inequalities", np.ndarray), ("bounds", np.ndarray), ("variable_fixed", np.ndarray), ("variable_bounds", np.ndarray), ("nullspace", np.matrix), ("homogeneous", bool), ], ) """Define the matrix representation of a sampling problem. A named tuple consisting of 6 arrays, 1 matrix and 1 boolean. Attributes ---------- equalities : numpy.array All equality constraints in the model. b : numpy.array The right side of the equality constraints. inequalities : numpy.array All inequality constraints in the model. bounds : numpy.array The lower and upper bounds for the inequality constraints. variable_fixed : numpy.array A boolean vector indicating whether the variable at that index is fixed i.e., whether `variable.lower_bound == variable.upper_bound`. variable_bounds : numpy.array The lower and upper bounds for the variables. nullspace : numpy.matrix A matrix containing the nullspace of the equality constraints. Each column is one basis vector. homogeneous: bool Indicates whether the sampling problem is homogeneous, e.g. whether there exist no non-zero fixed variables or constraints. """ def shared_np_array( shape: Tuple[int, int], data: Optional[np.ndarray] = None, integer: bool = False ) -> np.ndarray: """Create a new numpy array that resides in shared memory. Parameters ---------- shape : tuple of int The shape of the new array. data : numpy.array, optional Data to copy to the new array. Has to have the same shape (default None). integer : bool, optional Whether to use an integer array. By default, float array is used (default False). Returns ------- numpy.array The newly created shared numpy array. Raises ------ ValueError If the input `data` (if provided) size is not equal to the created array. """ size = np.prod(shape) if integer: array = Array(ctypes.c_int64, int(size)) np_array = np.frombuffer(array.get_obj(), dtype="int64") else: array = Array(ctypes.c_double, int(size)) np_array = np.frombuffer(array.get_obj()) np_array = np_array.reshape(shape) if data is not None: if len(shape) != len(data.shape): raise ValueError("`data` must have the same shape as the created array.") same = all(x == y for x, y in zip(shape, data.shape)) if not same: raise ValueError("`data` must have the same shape as the created array.") np_array[:] = data return np_array class HRSampler(ABC): """ The abstract base class for hit-and-run samplers. New samplers should derive from this class where possible to provide a uniform interface. Parameters ---------- model : cobra.Model The cobra model from which to generate samples. thinning : int The thinning factor of the generated sampling chain. A thinning of 10 means samples are returned every 10 steps. nproj : int > 0, optional How often to reproject the sampling point into the feasibility space. Avoids numerical issues at the cost of lower sampling. If you observe many equality constraint violations with `sampler.validate` you should lower this number (default None). seed : int > 0, optional Sets the random number seed. Initialized to the current time stamp if None (default None). Attributes ---------- feasibility_tol: float The tolerance used for checking equalities feasibility. bounds_tol: float The tolerance used for checking bounds feasibility. n_samples : int The total number of samples that have been generated by this sampler instance. retries : int The overall of sampling retries the sampler has observed. Larger values indicate numerical instabilities. problem : Problem A NamedTuple whose attributes define the entire sampling problem in matrix form. warmup : numpy.matrix A numpy matrix with as many columns as reactions in the model and more than 3 rows containing a warmup sample in each row. None if no warmup points have been generated yet. fwd_idx : numpy.array A numpy array having one entry for each reaction in the model, containing the index of the respective forward variable. rev_idx : numpy.array A numpy array having one entry for each reaction in the model, containing the index of the respective reverse variable. """ def __init__( self, model: "Model", thinning: int, nproj: Optional[int] = None, seed: Optional[int] = None, **kwargs, ) -> None: """Initialize a new sampler object. Other Parameters ---------------- kwargs : Further keyword arguments are passed on to the parent class. Raises ------ TypeError If integer problem is found. """ # This currently has to be done to reset the solver basis which is # required to get deterministic warmup point generation # (in turn required for a working `seed`) if model.solver.is_integer: raise TypeError("Sampling does not work with integer problems.") self.model = model.copy() self.feasibility_tol = model.tolerance self.bounds_tol = model.tolerance self.thinning = thinning if nproj is None: self.nproj = int(min(len(self.model.variables) ** 3, 1e6)) else: self.nproj = nproj self.n_samples = 0 self.retries = 0 self.problem = self.__build_problem() # Set up a map from reaction -> forward/reverse variable var_idx = {v: idx for idx, v in enumerate(self.model.variables)} self.fwd_idx = np.array( [var_idx[r.forward_variable] for r in self.model.reactions] ) self.rev_idx = np.array( [var_idx[r.reverse_variable] for r in self.model.reactions] ) self.warmup = None if seed is None: self._seed = int(time()) else: self._seed = seed # Avoid overflow self._seed = self._seed % np.iinfo(np.int32).max def __build_problem(self) -> Problem: """Build the matrix representation of the sampling problem. Returns ------- Problem The matrix representation in the form of a NamedTuple. """ # Set up the mathematical problem prob = constraint_matrices(self.model, zero_tol=self.feasibility_tol) # check if there any non-zero equality constraints equalities = prob.equalities b = prob.b bounds = np.atleast_2d(prob.bounds).T var_bounds = np.atleast_2d(prob.variable_bounds).T homogeneous = all(np.abs(b) < self.feasibility_tol) fixed_non_zero = np.abs(prob.variable_bounds[:, 1]) > self.feasibility_tol fixed_non_zero &= prob.variable_fixed # check if there are any non-zero fixed variables, add them as # equalities to the stoichiometric matrix if any(fixed_non_zero): n_fixed = fixed_non_zero.sum() rows = np.zeros((n_fixed, prob.equalities.shape[1])) rows[range(n_fixed), np.where(fixed_non_zero)] = 1.0 equalities = np.vstack([equalities, rows]) var_b = prob.variable_bounds[:, 1] b = np.hstack([b, var_b[fixed_non_zero]]) homogeneous = False # Set up a projection that can cast point into the nullspace nulls = nullspace(equalities) # convert bounds to a matrix and add variable bounds as well return Problem( equalities=shared_np_array(equalities.shape, equalities), b=shared_np_array(b.shape, b), inequalities=shared_np_array(prob.inequalities.shape, prob.inequalities), bounds=shared_np_array(bounds.shape, bounds), variable_fixed=shared_np_array( prob.variable_fixed.shape, prob.variable_fixed, integer=True ), variable_bounds=shared_np_array(var_bounds.shape, var_bounds), nullspace=shared_np_array(nulls.shape, nulls), homogeneous=homogeneous, ) def generate_fva_warmup(self) -> None: """Generate the warmup points for the sampler. Generates warmup points by setting each flux as the sole objective and minimizing/maximizing it. Also caches the projection of the warmup points into the nullspace for non-homogeneous problems (only if necessary). Raises ------ ValueError If flux cone contains a single point or the problem is inhomogeneous. """ self.n_warmup = 0 reactions = self.model.reactions self.warmup = np.zeros((2 * len(reactions), len(self.model.variables))) self.model.objective = Zero for sense in ("min", "max"): self.model.objective_direction = sense for i, r in enumerate(reactions): variables = ( self.model.variables[self.fwd_idx[i]], self.model.variables[self.rev_idx[i]], ) # Omit fixed reactions if they are non-homogeneous if r.upper_bound - r.lower_bound < self.bounds_tol: logger.info(f"Skipping fixed reaction {r.id}") continue self.model.objective.set_linear_coefficients( {variables[0]: 1, variables[1]: -1} ) self.model.slim_optimize() if not self.model.solver.status == OPTIMAL: logger.info(f"Cannot maximize reaction {r.id}, skipping it.") continue primals = self.model.solver.primal_values sol = [primals[v.name] for v in self.model.variables] self.warmup[self.n_warmup,] = sol self.n_warmup += 1 # Reset objective self.model.objective.set_linear_coefficients( {variables[0]: 0, variables[1]: 0} ) # Shrink to measure self.warmup = self.warmup[0 : self.n_warmup, :] # Remove redundant search directions keep = np.logical_not(self._is_redundant(self.warmup)) self.warmup = self.warmup[keep, :] self.n_warmup = self.warmup.shape[0] # Catch some special cases if len(self.warmup.shape) == 1 or self.warmup.shape[0] == 1: raise ValueError("Flux cone only consists a single point.") elif self.n_warmup == 2: if not self.problem.homogeneous: raise ValueError( "Cannot sample from an inhomogenous problem " "with only 2 search directions." ) logger.info("All search directions on a line, adding another one.") newdir = self.warmup.T.dot([0.25, 0.25]) self.warmup = np.vstack([self.warmup, newdir]) self.n_warmup += 1 # Shrink warmup points to measure self.warmup = shared_np_array( (self.n_warmup, len(self.model.variables)), self.warmup ) def _reproject(self, p: np.ndarray) -> np.ndarray: """Reproject a point into the feasibility region. This function is guaranteed to return a new feasible point. However, no guarantee can be made in terms of proximity to the original point. Parameters ---------- p : numpy.array The current sample point. Returns ------- numpy.array A new feasible point. If `p` is feasible, it will return `p`. """ nulls = self.problem.nullspace equalities = self.problem.equalities # don't reproject if point is feasible if np.allclose( equalities.dot(p), self.problem.b, rtol=0, atol=self.feasibility_tol ): new = p else: logger.info( f"Feasibility violated in sample {self.n_samples}, trying to reproject." ) new = nulls.dot(nulls.T.dot(p)) # Projections may violate bounds # set to random point in space in that case if any(new != p): logger.info( f"Re-projection failed in sample {self.n_samples}, " "using random point in space." ) new = self._random_point() return new def _random_point(self) -> np.ndarray: """Find an approximately random point in the flux cone.""" idx = np.random.randint( self.n_warmup, size=min(2, np.ceil(np.sqrt(self.n_warmup))) ) return self.warmup[idx, :].mean(axis=0) def _is_redundant(self, matrix: np.matrix, cutoff: Optional[float] = None) -> bool: """Identify redundant rows in a matrix that can be removed.""" cutoff = 1.0 - self.feasibility_tol # Avoid zero variances extra_col = matrix[:, 0] + 1 # Avoid zero rows being correlated with constant rows extra_col[matrix.sum(axis=1) == 0] = 2 corr = np.corrcoef(np.c_[matrix, extra_col]) corr = np.tril(corr, -1) return (np.abs(corr) > cutoff).any(axis=1) def _bounds_dist(self, p: np.ndarray) -> np.ndarray: """Get the lower and upper bound distances. Negative is bad.""" prob = self.problem lb_dist = (p - prob.variable_bounds[0,]).min() ub_dist = (prob.variable_bounds[1,] - p).min() if prob.bounds.shape[0] > 0: const = prob.inequalities.dot(p) const_lb_dist = (const - prob.bounds[0,]).min() const_ub_dist = (prob.bounds[1,] - const).min() lb_dist = min(lb_dist, const_lb_dist) ub_dist = min(ub_dist, const_ub_dist) return np.array([lb_dist, ub_dist]) @abstractmethod def sample(self, n: int, fluxes: bool = True) -> pd.DataFrame: """Abstract sampling function. Should be overwritten by child classes. Parameters ---------- n : int The number of samples that are generated at once. fluxes : bool, optional Whether to return fluxes or the internal solver variables. If set to False, will return a variable for each forward and backward flux as well as all additional variables you might have defined in the model (default True). Returns ------- pandas.DataFrame Returns a pandas DataFrame with `n` rows, each containing a flux sample. """ raise NotImplementedError( "This method needs to be implemented by the subclass." ) def batch( self, batch_size: int, batch_num: int, fluxes: bool = True ) -> pd.DataFrame: """Create a batch generator. This is useful to generate `batch_num` batches of `batch_size` samples each. Parameters ---------- batch_size : int The number of samples contained in each batch. batch_num : int The number of batches in the generator. fluxes : bool, optional Whether to return fluxes or the internal solver variables. If set to False, will return a variable for each forward and backward flux as well as all additional variables you might have defined in the model (default True). Yields ------ pandas.DataFrame A DataFrame with dimensions (batch_size x n_r) containing a valid flux sample for a total of n_r reactions (or variables if fluxes=False) in each row. """ for _ in range(batch_num): yield self.sample(batch_size, fluxes=fluxes) def validate(self, samples: np.matrix) -> np.ndarray: """Validate a set of samples for equality and inequality feasibility. Can be used to check whether the generated samples and warmup points are feasible. Parameters ---------- samples : numpy.matrix Must be of dimension (samples x n_reactions). Contains the samples to be validated. Samples must be from fluxes. Returns ------- numpy.array A one-dimensional numpy array containing a code of 1 to 3 letters denoting the validation result: - 'v' means feasible in bounds and equality constraints - 'l' means a lower bound violation - 'u' means a lower bound validation - 'e' means and equality constraint violation Raises ------ ValueError If wrong number of columns. """ samples = np.atleast_2d(samples) prob = self.problem if samples.shape[1] == len(self.model.reactions): S = create_stoichiometric_matrix(self.model) b = np.array( [self.model.constraints[m.id].lb for m in self.model.metabolites] ) bounds = np.array([r.bounds for r in self.model.reactions]).T elif samples.shape[1] == len(self.model.variables): S = prob.equalities b = prob.b bounds = prob.variable_bounds else: raise ValueError( "Wrong number of columns. Samples must have a " "column for each flux or variable defined in the " "model." ) feasibility = np.abs(S.dot(samples.T).T - b).max(axis=1) lb_error = (samples - bounds[0,]).min(axis=1) ub_error = (bounds[1,] - samples).min(axis=1) if samples.shape[1] == len(self.model.variables) and prob.inequalities.shape[0]: consts = prob.inequalities.dot(samples.T) lb_error = np.minimum( lb_error, (consts - prob.bounds[0,]).min(axis=1), ) ub_error = np.minimum( ub_error, (prob.bounds[1,] - consts).min(axis=1), ) valid = ( (feasibility < self.feasibility_tol) & (lb_error > -self.bounds_tol) & (ub_error > -self.bounds_tol) ) codes = np.repeat("", valid.shape[0]).astype(np.dtype((str, 3))) codes[valid] = "v" codes[lb_error <= -self.bounds_tol] = np.char.add( codes[lb_error <= -self.bounds_tol], "l" ) codes[ub_error <= -self.bounds_tol] = np.char.add( codes[ub_error <= -self.bounds_tol], "u" ) codes[feasibility > self.feasibility_tol] = np.char.add( codes[feasibility > self.feasibility_tol], "e" ) return codes cobrapy-0.29.1/src/cobra/sampling/optgp.py000066400000000000000000000212651467301562300204510ustar00rootroot00000000000000"""Provide the OptGP sampler class and helper functions.""" from typing import TYPE_CHECKING, Dict, Optional, Tuple import numpy as np import pandas as pd from ..core.configuration import Configuration from ..util import ProcessPool from .core import step from .hr_sampler import HRSampler, shared_np_array if TYPE_CHECKING: from cobra import Model __all__ = ("OptGPSampler",) configuration = Configuration() class OptGPSampler(HRSampler): """ Improved Artificial Centering Hit-and-Run sampler. A parallel sampler with fast convergence and parallel execution. See [1]_ for details. Parameters ---------- model : cobra.Model The cobra model from which to generate samples. processes: int, optional The number of processes used during sampling (default cobra.Configuration.processes). thinning : int, optional The thinning factor of the generated sampling chain. A thinning of 10 means samples are returned every 10 steps (default 100). nproj : int > 0, optional How often to reproject the sampling point into the feasibility space. Avoids numerical issues at the cost of lower sampling. If you observe many equality constraint violations with `sampler.validate` you should lower this number (default None). seed : int > 0, optional Sets the random number seed. Initialized to the current time stamp if None (default None). Attributes ---------- n_samples : int The total number of samples that have been generated by this sampler instance. problem : typing.NamedTuple A NamedTuple whose attributes define the entire sampling problem in matrix form. warmup : numpy.matrix A numpy matrix with as many columns as reactions in the model and more than 3 rows containing a warmup sample in each row. None if no warmup points have been generated yet. retries : int The overall of sampling retries the sampler has observed. Larger values indicate numerical instabilities. fwd_idx : numpy.array A numpy array having one entry for each reaction in the model, containing the index of the respective forward variable. rev_idx : numpy.array A numpy array having one entry for each reaction in the model, containing the index of the respective reverse variable. prev : numpy.array The current/last flux sample generated. center : numpy.array The center of the sampling space as estimated by the mean of all previously generated samples. Notes ----- The sampler is very similar to artificial centering where each process samples its own chain. Initial points are chosen randomly from the warmup points followed by a linear transformation that pulls the points a little bit towards the center of the sampling space. If the number of processes used is larger than the one requested, number of samples is adjusted to the smallest multiple of the number of processes larger than the requested sample number. For instance, if you have 3 processes and request 8 samples, you will receive 9. Memory usage is roughly in the order of (2 * number of reactions)^2 due to the required nullspace matrices and warmup points. So, large models easily take up a few GBs of RAM. However, most of the large matrices are kept in shared memory. So the RAM usage is independent of the number of processes. References ---------- .. [1] Megchelenbrink W, Huynen M, Marchiori E (2014) optGpSampler: An Improved Tool for Uniformly Sampling the Solution-Space of Genome-Scale Metabolic Networks. PLoS ONE 9(2): e86587. https://doi.org/10.1371/journal.pone.0086587 """ def __init__( self, model: "Model", thinning: int = 100, processes: Optional[int] = None, nproj: Optional[int] = None, seed: Optional[int] = None, **kwargs, ) -> None: """Initialize a new OptGPSampler.""" super().__init__(model, thinning, nproj=nproj, seed=seed, **kwargs) self.generate_fva_warmup() if processes is None: self.processes = configuration.processes else: self.processes = processes # This maps our saved center into shared memory, # meaning they are synchronized across processes self.center = shared_np_array( (len(self.model.variables),), self.warmup.mean(axis=0) ) def sample(self, n: int, fluxes: bool = True) -> pd.DataFrame: """Generate a set of samples. This is the basic sampling function for all hit-and-run samplers. Parameters ---------- n : int The minimum number of samples that are generated at once. fluxes : bool, optional Whether to return fluxes or the internal solver variables. If set to False, will return a variable for each forward and backward flux as well as all additional variables you might have defined in the model (default True). Returns ------- pandas.DataFrame Returns a pandas DataFrame with `n` rows, each containing a flux sample. Notes ----- Performance of this function linearly depends on the number of reactions in your model and the thinning factor. If the number of processes is larger than one, computation is split across the CPU cores of your machine. This may shorten computation time. However, there is also overhead in setting up parallel computation primitives so, we recommend to calculate large numbers of samples at once (`n` > 1000). """ if self.processes > 1: n_process = np.ceil(n / self.processes).astype(int) n = n_process * self.processes # The cast to list is weird but not doing it gives recursion # limit errors, something weird going on with multiprocessing args = list(zip([n_process] * self.processes, range(self.processes))) with ProcessPool( self.processes, initializer=mp_init, initargs=(self,) ) as pool: results = pool.map(_sample_chain, args, chunksize=1) chains = np.vstack([r[1] for r in results]) self.retries += sum(r[0] for r in results) else: mp_init(self) results = _sample_chain((n, 0)) chains = results[1] # Update the global center self.center = (self.n_samples * self.center + np.atleast_2d(chains).sum(0)) / ( self.n_samples + n ) self.n_samples += n if fluxes: names = [r.id for r in self.model.reactions] return pd.DataFrame( chains[:, self.fwd_idx] - chains[:, self.rev_idx], columns=names, ) else: names = [v.name for v in self.model.variables] return pd.DataFrame(chains, columns=names) # Models can be large so don't pass them around during multiprocessing def __getstate__(self) -> Dict: """Return the object for serialization.""" d = dict(self.__dict__) del d["model"] return d def mp_init(obj: OptGPSampler) -> None: """Initialize the multiprocessing pool.""" global sampler sampler = obj # Unfortunately this has to be outside the class to be usable with # multiprocessing :() def _sample_chain(args: Tuple[int, int]) -> Tuple[int, OptGPSampler]: """Sample a single chain for OptGPSampler. `center` and `n_samples` are updated locally and forgotten afterwards. """ n, idx = args # has to be this way to work in Python 2.7 center = sampler.center np.random.seed((sampler._seed + idx) % np.iinfo(np.int32).max) pi = np.random.randint(sampler.n_warmup) prev = sampler.warmup[pi, :] prev = step(sampler, center, prev - center, 0.95) n_samples = max(sampler.n_samples, 1) samples = np.zeros((n, center.shape[0])) for i in range(1, sampler.thinning * n + 1): pi = np.random.randint(sampler.n_warmup) delta = sampler.warmup[pi, :] - center prev = step(sampler, prev, delta) if sampler.problem.homogeneous and ( n_samples * sampler.thinning % sampler.nproj == 0 ): prev = sampler._reproject(prev) center = sampler._reproject(center) if i % sampler.thinning == 0: samples[i // sampler.thinning - 1, :] = prev center = (n_samples * center) / (n_samples + 1) + prev / (n_samples + 1) n_samples += 1 return (sampler.retries, samples) cobrapy-0.29.1/src/cobra/sampling/sampling.py000066400000000000000000000064261467301562300211340ustar00rootroot00000000000000"""Provide a wrapper function for performing flux sampling of cobra models.""" from typing import TYPE_CHECKING, Optional import pandas as pd from .achr import ACHRSampler from .optgp import OptGPSampler if TYPE_CHECKING: from cobra import Model def sample( model: "Model", n: int, method: str = "optgp", thinning: int = 100, processes: int = 1, seed: Optional[int] = None, ) -> pd.DataFrame: """Sample valid flux distributions from a cobra model. Currently, two methods are supported: 1. 'optgp' (default) which uses the OptGPSampler that supports parallel sampling. Requires large numbers of samples to be performant (`n` > 1000). For smaller samples, 'achr' might be better suited. For details, refer [1]_ . 2. 'achr' which uses artificial centering hit-and-run. This is a single process method with good convergence. For details, refer [2]_ . Parameters ---------- model : cobra.Model The model from which to sample flux distributions. n : int The number of samples to obtain. When using 'optgp', this must be a multiple of `processes`, otherwise a larger number of samples will be returned. method : {"optgp", "achr"}, optional The sampling algorithm to use (default "optgp"). thinning : int, optional The thinning factor of the generated sampling chain. A thinning of 10 means samples are returned every 10 steps. Defaults to 100 which in benchmarks gives approximately uncorrelated samples. If set to 1 will return all iterates (default 100). processes : int, optional Only used for 'optgp'. The number of processes used to generate samples (default 1). seed : int > 0, optional Sets the random number seed. Initialized to the current time stamp if None (default None). Returns ------- pandas.DataFrame The generated flux samples. Each row corresponds to a sample of the fluxes and the columns are the reactions. Notes ----- The samplers have a correction method to ensure equality feasibility for long-running chains, however this will only work for homogeneous models, meaning models with no non-zero fixed variables or constraints ( right-hand side of the equalities are zero). References ---------- .. [1] Megchelenbrink W, Huynen M, Marchiori E (2014) optGpSampler: An Improved Tool for Uniformly Sampling the Solution-Space of Genome-Scale Metabolic Networks. PLoS ONE 9(2): e86587. https://doi.org/10.1371/journal.pone.0086587 .. [2] Direction Choice for Accelerated Convergence in Hit-and-Run Sampling David E. Kaufman, Robert L. Smith Operations Research 199846:1 , 84-95 https://doi.org/10.1287/opre.46.1.84 """ if method == "optgp": sampler = OptGPSampler(model, processes=processes, thinning=thinning, seed=seed) elif method == "achr": sampler = ACHRSampler(model, thinning=thinning, seed=seed) else: raise ValueError( f'Invalid value: "{method}" for method used. ' 'The value must be "optgp" or "achr".' ) return pd.DataFrame( columns=[rxn.id for rxn in model.reactions], data=sampler.sample(n) ) cobrapy-0.29.1/src/cobra/summary/000077500000000000000000000000001467301562300166235ustar00rootroot00000000000000cobrapy-0.29.1/src/cobra/summary/__init__.py000066400000000000000000000002451467301562300207350ustar00rootroot00000000000000from .summary import Summary from .metabolite_summary import MetaboliteSummary from .reaction_summary import ReactionSummary from .model_summary import ModelSummary cobrapy-0.29.1/src/cobra/summary/metabolite_summary.py000066400000000000000000000333551467301562300231100ustar00rootroot00000000000000"""Provide the metabolite summary class.""" import logging from operator import attrgetter from textwrap import shorten from typing import TYPE_CHECKING, List, Optional, Union import pandas as pd from cobra.flux_analysis import flux_variability_analysis, pfba from cobra.summary import Summary if TYPE_CHECKING: from cobra.core import Metabolite, Model, Reaction, Solution logger = logging.getLogger(__name__) class MetaboliteSummary(Summary): """ Define the metabolite summary. Attributes ---------- producing_flux : pandas.DataFrame A pandas DataFrame of only the producing fluxes. consuming_flux : pandas.DataFrame A pandas DataFrame of only the consuming fluxes. See Also -------- Summary : Parent that defines further attributes. ReactionSummary ModelSummary """ def __init__( self, *, metabolite: "Metabolite", model: "Model", solution: Optional["Solution"] = None, fva: Optional[Union[float, pd.DataFrame]] = None, **kwargs, ) -> None: """ Initialize a metabolite summary. Parameters ---------- metabolite : cobra.Metabolite The metabolite object whose summary we intend to get. model : cobra.Model The metabolic model for which to generate a metabolite summary. solution : cobra.Solution, optional A previous model solution to use for generating the summary. If ``None``, the summary method will generate a parsimonious flux distribution (default None). fva : pandas.DataFrame or float, optional Whether or not to include flux variability analysis in the output. If given, `fva` should either be a previous FVA solution matching the model or a float between 0 and 1 representing the fraction of the optimum objective to be searched (default None). Other Parameters ---------------- kwargs : Further keyword arguments are passed on to the parent class. See Also -------- Summary : Parent that has further default parameters. ReactionSummary ModelSummary """ super().__init__(**kwargs) self._metabolite = metabolite.copy() self._reactions: List["Reaction"] = [ r.copy() for r in sorted(metabolite.reactions, key=attrgetter("id")) ] self.producing_flux: Optional[pd.DataFrame] = None self.consuming_flux: Optional[pd.DataFrame] = None self._generate(model, solution, fva) def _generate( self, model: "Model", solution: Optional["Solution"], fva: Optional[Union[float, pd.DataFrame]], ) -> None: """ Prepare the data for the summary instance. Parameters ---------- model : cobra.Model The metabolic model for which to generate a metabolite summary. solution : cobra.Solution, optional A previous model solution to use for generating the summary. If ``None``, the summary method will generate a parsimonious flux distribution. fva : pandas.DataFrame or float, optional Whether or not to include flux variability analysis in the output. If given, `fva` should either be a previous FVA solution matching the model or a float between 0 and 1 representing the fraction of the optimum objective to be searched. """ super()._generate(model=model, solution=solution, fva=fva) if solution is None: logger.info("Generating new parsimonious flux distribution.") solution = pfba(model) if isinstance(fva, float): logger.info("Performing flux variability analysis.") fva = flux_variability_analysis( model=model, reaction_list=[r.id for r in self._reactions], fraction_of_optimum=fva, ) # Create the basic flux table. flux = pd.DataFrame( data=[ ( r.id, solution[r.id], r.get_coefficient(self._metabolite.id), ) for r in self._reactions ], columns=["reaction", "flux", "factor"], index=[r.id for r in self._reactions], ) # Scale fluxes by stoichiometric coefficient. flux["flux"] *= flux["factor"] if fva is not None: flux = flux.join(fva) view = flux[["flux", "minimum", "maximum"]] # Set fluxes below model tolerance to zero. flux[["flux", "minimum", "maximum"]] = view.where( view.abs() >= model.tolerance, 0 ) # Create the scaled compound flux. flux[["minimum", "maximum"]] = flux[["minimum", "maximum"]].mul( flux["factor"], axis=0 ) # Negative factors invert the minimum/maximum relationship. negative = flux["factor"] < 0 tmp = flux.loc[negative, "maximum"] flux.loc[negative, "maximum"] = flux.loc[negative, "minimum"] flux.loc[negative, "minimum"] = tmp # Add zero to turn negative zero into positive zero for nicer display later. flux[["flux", "minimum", "maximum"]] += 0 else: # Set fluxes below model tolerance to zero. flux.loc[flux["flux"].abs() < model.tolerance, "flux"] = 0 # Add zero to turn negative zero into positive zero for nicer display later. flux["flux"] += 0 # Create production table from producing fluxes or zero fluxes where the # metabolite is a product in the reaction. is_produced = (flux["flux"] > 0) | ((flux["flux"] == 0) & (flux["factor"] > 0)) if fva is not None: self.producing_flux = flux.loc[ is_produced, ["flux", "minimum", "maximum", "reaction"] ].copy() else: self.producing_flux = flux.loc[is_produced, ["flux", "reaction"]].copy() production = self.producing_flux["flux"].abs() self.producing_flux["percent"] = production / production.sum() # Create consumption table from consuming fluxes or zero fluxes where the # metabolite is a substrate in the reaction. is_consumed = (flux["flux"] < 0) | ((flux["flux"] == 0) & (flux["factor"] < 0)) if fva is not None: self.consuming_flux = flux.loc[ is_consumed, ["flux", "minimum", "maximum", "reaction"] ].copy() else: self.consuming_flux = flux.loc[is_consumed, ["flux", "reaction"]].copy() consumption = self.consuming_flux["flux"].abs() self.consuming_flux["percent"] = consumption / consumption.sum() self._flux = flux def _display_flux( self, frame: pd.DataFrame, names: bool, threshold: float ) -> pd.DataFrame: """ Transform a flux data frame for display. Parameters ---------- frame : pandas.DataFrame Either the producing or the consuming fluxes. names : bool Whether or not elements should be displayed by their common names. threshold : float Hide fluxes below the threshold from being displayed. Returns ------- pandas.DataFrame The transformed pandas DataFrame with flux percentages and reaction definitions. """ if "minimum" in frame.columns and "maximum" in frame.columns: frame = frame.loc[ (frame["flux"].abs() >= threshold) | (frame["minimum"].abs() >= threshold) | (frame["maximum"].abs() >= threshold), :, ].copy() else: frame = frame.loc[frame["flux"].abs() >= threshold, :].copy() reactions = {r.id: r for r in self._reactions} frame["definition"] = [ reactions[rxn_id].build_reaction_string(names) for rxn_id in frame["reaction"] ] if "minimum" in frame.columns and "maximum" in frame.columns: frame["range"] = list( frame[["minimum", "maximum"]].itertuples(index=False, name=None) ) return frame[["percent", "flux", "range", "reaction", "definition"]] else: return frame[["percent", "flux", "reaction", "definition"]] @staticmethod def _string_table(frame: pd.DataFrame, float_format: str, column_width: int) -> str: """ Create a pretty string representation of the data frame. Parameters ---------- frame : pandas.DataFrame A pandas DataFrame of fluxes. float_format : str Format string for floats. column_width : int The maximum column width for each row. Returns ------- str The data frame formatted as a pretty string. """ frame.columns = [header.title() for header in frame.columns] return frame.to_string( header=True, index=False, na_rep="", formatters={ "Percent": "{:.2%}".format, "Flux": f"{{:{float_format}}}".format, "Range": lambda pair: f"[{pair[0]:{float_format}}; " f"{pair[1]:{float_format}}]", }, max_colwidth=column_width, ) @staticmethod def _html_table(frame: pd.DataFrame, float_format: str) -> str: """ Create an HTML representation of the data frame. Parameters ---------- frame : pandas.DataFrame A pandas DataFrame of fluxes. float_format : str Format string for floats. Returns ------- str The data frame formatted as HTML. """ frame.columns = [header.title() for header in frame.columns] return frame.to_html( header=True, index=False, na_rep="", formatters={ "Percent": "{:.2%}".format, "Flux": f"{{:{float_format}}}".format, "Range": lambda pair: f"[{pair[0]:{float_format}}; " f" {pair[1]:{float_format}}]", }, ) def to_string( self, names: bool = False, threshold: Optional[float] = None, float_format: str = ".4G", column_width: int = 79, ) -> str: """ Return a pretty string representation of the metabolite summary. Parameters ---------- names : bool, optional Whether or not elements should be displayed by their common names (default False). threshold : float, optional Hide fluxes below the threshold from being displayed. If no value is given, the model tolerance is used (default None). float_format : str, optional Format string for floats (default '.4G'). column_width : int, optional The maximum column width for each row (default 79). Returns ------- str The summary formatted as a pretty string. """ threshold = self._normalize_threshold(threshold) if names: metabolite = shorten( self._metabolite.name, width=column_width, placeholder="..." ) else: metabolite = shorten( self._metabolite.id, width=column_width, placeholder="..." ) production = self._string_table( self._display_flux(self.producing_flux, names, threshold), float_format, column_width, ) consumption = self._string_table( self._display_flux(self.consuming_flux, names, threshold), float_format, column_width, ) return ( f"{metabolite}\n" f"{'=' * len(metabolite)}\n" f"Formula: {self._metabolite.formula}\n\n" f"Producing Reactions\n" f"-------------------\n" f"{production}\n\n" f"Consuming Reactions\n" f"-------------------\n" f"{consumption}" ) def to_html( self, names: bool = False, threshold: Optional[float] = None, float_format: str = ".4G", ) -> str: """ Return a rich HTML representation of the metabolite summary. Parameters ---------- names : bool, optional Whether or not elements should be displayed by their common names (default False). threshold : float, optional Hide fluxes below the threshold from being displayed. If no value is given, the model tolerance is used (default None). float_format : str, optional Format string for floats (default '.4G'). Returns ------- str The summary formatted as HTML. """ threshold = self._normalize_threshold(threshold) if names: metabolite = self._metabolite.name else: metabolite = self._metabolite.id production = self._html_table( self._display_flux(self.producing_flux, names, threshold), float_format, ) consumption = self._html_table( self._display_flux(self.consuming_flux, names, threshold), float_format, ) return ( f"

{metabolite}

" f"

{self._metabolite.formula}

" f"

Producing Reactions

" f"{production}" f"

Consuming Reactions

" f"{consumption}" ) cobrapy-0.29.1/src/cobra/summary/model_summary.py000066400000000000000000000372231467301562300220610ustar00rootroot00000000000000"""Provide the model summary class.""" import logging from operator import attrgetter from typing import TYPE_CHECKING, Dict, List, Optional, Union import pandas as pd from cobra.core import Reaction from cobra.flux_analysis import flux_variability_analysis, pfba from cobra.summary import Summary from cobra.util.solver import linear_reaction_coefficients if TYPE_CHECKING: from cobra.core import Metabolite, Model, Solution logger = logging.getLogger(__name__) class ModelSummary(Summary): """ Define the model summary. Attributes ---------- uptake_flux : pandas.DataFrame A pandas DataFrame of only the uptake fluxes. secretion_flux : pandas.DataFrame A pandas DataFrame of only the consuming fluxes. See Also -------- Summary : Parent that defines further attributes. MetaboliteSummary ReactionSummary """ def __init__( self, *, model: "Model", solution: Optional["Solution"] = None, fva: Optional[Union[float, pd.DataFrame]] = None, **kwargs, ): """ Initialize a model summary. Parameters ---------- model : cobra.Model The metabolic model for which to generate a metabolite summary. solution : cobra.Solution, optional A previous model solution to use for generating the summary. If ``None``, the summary method will generate a parsimonious flux distribution (default None). fva : pandas.DataFrame or float, optional Whether or not to include flux variability analysis in the output. If given, `fva` should either be a previous FVA solution matching the model or a float between 0 and 1 representing the fraction of the optimum objective to be searched (default None). Other Parameters ---------------- kwargs : Further keyword arguments are passed on to the parent class. See Also -------- Summary : Parent that has further default parameters. MetaboliteSummary ReactionSummary """ super().__init__(**kwargs) self._objective = None self._objective_value = None self._boundary = None self._boundary_metabolites = None self.uptake_flux: Optional[pd.DataFrame] = None self.secretion_flux: Optional[pd.DataFrame] = None self._generate(model, solution, fva) def _generate( self, model: "Model", solution: Optional["Solution"], fva: Optional[Union[float, pd.DataFrame]], ) -> None: """ Prepare the data for the summary instance. Parameters ---------- model : cobra.Model The metabolic model for which to generate a metabolite summary. solution : cobra.Solution, optional A previous model solution to use for generating the summary. If ``None``, the summary method will generate a parsimonious flux distribution. fva : pandas.DataFrame or float, optional Whether or not to include flux variability analysis in the output. If given, `fva` should either be a previous FVA solution matching the model or a float between 0 and 1 representing the fraction of the optimum objective to be searched. """ super()._generate(model=model, solution=solution, fva=fva) coefficients = linear_reaction_coefficients(model) if solution is None: logger.info("Generating new parsimonious flux distribution.") solution = pfba(model) if isinstance(fva, float): logger.info("Performing flux variability analysis.") fva = flux_variability_analysis( model=model, reaction_list=model.boundary, fraction_of_optimum=fva, ) if coefficients: self._objective: Dict["Reaction", float] = { rxn.copy(): coef for rxn, coef in coefficients.items() } self._objective_value: float = sum( solution[rxn.id] * coef for rxn, coef in self._objective.items() ) else: logger.warning( "Non-linear or non-reaction model objective. Falling back to minimal " "display." ) self._objective = { Reaction(id="Expression", name="Expression"): float("nan") } self._objective_value: float = float("nan") self._boundary: List["Reaction"] = [ rxn.copy() for rxn in sorted(model.boundary, key=attrgetter("id")) ] self._boundary_metabolites: List["Metabolite"] = [ met.copy() for rxn in self._boundary for met in rxn.metabolites ] flux = pd.DataFrame( data=[ (rxn.id, met.id, rxn.get_coefficient(met.id), solution[rxn.id]) for rxn, met in zip(self._boundary, self._boundary_metabolites) ], columns=["reaction", "metabolite", "factor", "flux"], index=[r.id for r in self._boundary], ) # Scale fluxes by stoichiometric coefficient. flux["flux"] *= flux["factor"] if fva is not None: flux = flux.join(fva) view = flux[["flux", "minimum", "maximum"]] # Set fluxes below model tolerance to zero. flux[["flux", "minimum", "maximum"]] = view.where( view.abs() >= model.tolerance, 0 ) # Create the scaled compound flux. flux[["minimum", "maximum"]] = flux[["minimum", "maximum"]].mul( flux["factor"], axis=0 ) # Negative factors invert the minimum/maximum relationship. negative = flux["factor"] < 0 tmp = flux.loc[negative, "maximum"] flux.loc[negative, "maximum"] = flux.loc[negative, "minimum"] flux.loc[negative, "minimum"] = tmp # Add zero to turn negative zero into positive zero for nicer display later. flux[["flux", "minimum", "maximum"]] += 0 else: # Set fluxes below model tolerance to zero. flux.loc[flux["flux"].abs() < model.tolerance, "flux"] = 0 # Add zero to turn negative zero into positive zero for nicer display later. flux["flux"] += 0 # Create production table from producing fluxes or zero fluxes where the # metabolite is a product in the reaction. is_produced = (flux["flux"] > 0) | ((flux["flux"] == 0) & (flux["factor"] > 0)) if fva is not None: self.uptake_flux = flux.loc[ is_produced, ["flux", "minimum", "maximum", "reaction", "metabolite"] ].copy() else: self.uptake_flux = flux.loc[ is_produced, ["flux", "reaction", "metabolite"] ].copy() # Create consumption table from consuming fluxes or zero fluxes where the # metabolite is a substrate in the reaction. is_consumed = (flux["flux"] < 0) | ((flux["flux"] == 0) & (flux["factor"] < 0)) if fva is not None: self.secretion_flux = flux.loc[ is_consumed, ["flux", "minimum", "maximum", "reaction", "metabolite"] ].copy() else: self.secretion_flux = flux.loc[ is_consumed, ["flux", "reaction", "metabolite"] ].copy() self._flux = flux def _display_flux( self, frame: pd.DataFrame, names: bool, element: str, threshold: float ) -> pd.DataFrame: """ Transform a flux data frame for display. Parameters ---------- frame : pandas.DataFrame Either the producing or the consuming fluxes. names : bool Whether or not elements should be displayed by their common names. element : str The atomic element to summarize fluxes for. threshold : float Hide fluxes below the threshold from being displayed. Returns ------- pandas.DataFrame The transformed pandas DataFrame with flux percentages and reaction definitions. """ if "minimum" in frame.columns and "maximum" in frame.columns: frame = frame.loc[ (frame["flux"].abs() >= threshold) | (frame["minimum"].abs() >= threshold) | (frame["maximum"].abs() >= threshold), :, ].copy() else: frame = frame.loc[frame["flux"].abs() >= threshold, :].copy() metabolites = {m.id: m for m in self._boundary_metabolites} element_num = f"{element}-Number" frame[element_num] = [ metabolites[met_id].elements.get(element, 0) for met_id in frame["metabolite"] ] element_percent = f"{element}-Flux" frame[element_percent] = frame[element_num] * frame["flux"].abs() total = frame[element_percent].sum() if total > 0.0: frame[element_percent] /= total frame[element_percent] = [f"{x:.2%}" for x in frame[element_percent]] if names: frame["metabolite"] = [ metabolites[met_id].name for met_id in frame["metabolite"] ] if "minimum" in frame.columns and "maximum" in frame.columns: frame["range"] = list( frame[["minimum", "maximum"]].itertuples(index=False, name=None) ) return frame[ [ "metabolite", "reaction", "flux", "range", element_num, element_percent, ] ] else: return frame[ ["metabolite", "reaction", "flux", element_num, element_percent] ] @staticmethod def _string_table(frame: pd.DataFrame, float_format: str, column_width: int) -> str: """ Create a pretty string representation of the data frame. Parameters ---------- frame : pandas.DataFrame A pandas DataFrame of fluxes. float_format : str Format string for floats. column_width : int The maximum column width for each row. Returns ------- str The data frame formatted as a pretty string. """ frame.columns = [header.title() for header in frame.columns] return frame.to_string( header=True, index=False, na_rep="", formatters={ "Flux": f"{{:{float_format}}}".format, "Range": lambda pair: f"[{pair[0]:{float_format}}; " f"{pair[1]:{float_format}}]", }, max_colwidth=column_width, ) @staticmethod def _html_table(frame: pd.DataFrame, float_format: str) -> str: """ Create an HTML representation of the data frame. Parameters ---------- frame : pandas.DataFrame A pandas DataFrame of fluxes. float_format : str Format string for floats. Returns ------- str The data frame formatted as HTML. """ frame.columns = [header.title() for header in frame.columns] return frame.to_html( header=True, index=False, na_rep="", formatters={ "Flux": f"{{:{float_format}}}".format, "Range": lambda pair: f"[{pair[0]:{float_format}}; " f" {pair[1]:{float_format}}]", }, ) def _string_objective(self, names: bool) -> str: """ Return a string representation of the objective. Parameters ---------- names : bool, optional Whether or not elements should be displayed by their common names. Returns ------- str The objective expression and value as a string. """ if names: objective = " + ".join( [f"{coef} {rxn.name}" for rxn, coef in self._objective.items()] ) else: objective = " + ".join( [f"{coef} {rxn.id}" for rxn, coef in self._objective.items()] ) return f"{objective} = {self._objective_value}" def to_string( self, names: bool = False, element: str = "C", threshold: Optional[float] = None, float_format: str = ".4G", column_width: int = 79, ) -> str: """ Return a pretty string representation of the model summary. Parameters ---------- names : bool, optional Whether or not elements should be displayed by their common names (default False). element : str, optional The atomic element to summarize uptake and secretion for (default 'C'). threshold : float, optional Hide fluxes below the threshold from being displayed. If no value is given, the model tolerance is used (default None). float_format : str, optional Format string for floats (default '.4G'). column_width : int, optional The maximum column width for each row (default 79). Returns ------- str The summary formatted as a pretty string. """ threshold = self._normalize_threshold(threshold) objective = self._string_objective(names) uptake = self._string_table( self._display_flux(self.uptake_flux, names, element, threshold), float_format, column_width, ) secretion = self._string_table( self._display_flux(self.secretion_flux, names, element, threshold), float_format, column_width, ) return ( f"Objective\n" f"=========\n" f"{objective}\n\n" f"Uptake\n" f"------\n" f"{uptake}\n\n" f"Secretion\n" f"---------\n" f"{secretion}\n" ) def to_html( self, names: bool = False, element: str = "C", threshold: Optional[float] = None, float_format: str = ".4G", ) -> str: """ Return a rich HTML representation of the model summary. Parameters ---------- names : bool, optional Whether or not elements should be displayed by their common names (default False). element : str, optional The atomic element to summarize uptake and secretion for (default 'C'). threshold : float, optional Hide fluxes below the threshold from being displayed. If no value is given, the model tolerance is used (default None). float_format : str, optional Format string for floats (default '.4G'). Returns ------- str The summary formatted as HTML. """ threshold = self._normalize_threshold(threshold) objective = self._string_objective(names) uptake = self._html_table( self._display_flux(self.uptake_flux, names, element, threshold), float_format, ) secretion = self._html_table( self._display_flux(self.secretion_flux, names, element, threshold), float_format, ) return ( f"

Objective

" f"

{objective}

" f"

Uptake

" f"{uptake}" f"

Secretion

" f"{secretion}" ) cobrapy-0.29.1/src/cobra/summary/reaction_summary.py000066400000000000000000000173271467301562300225700ustar00rootroot00000000000000"""Provide the reaction summary class.""" import logging from textwrap import dedent, shorten from typing import TYPE_CHECKING, Optional, Union import pandas as pd from cobra.flux_analysis import flux_variability_analysis, pfba from cobra.summary import Summary if TYPE_CHECKING: from cobra import Model, Reaction, Solution logger = logging.getLogger(__name__) class ReactionSummary(Summary): """ Define the reaction summary. See Also -------- Summary : Parent that defines further attributes. MetaboliteSummary ModelSummary """ def __init__( self, *, reaction: "Reaction", model: "Model", solution: Optional["Solution"] = None, fva: Optional[Union[float, pd.DataFrame]] = None, **kwargs, ) -> None: """ Initialize a reaction summary. Parameters ---------- reaction: cobra.Reaction The reaction object whose summary we intend to get. model : cobra.Model The metabolic model in which to generate a reaction summary. solution : cobra.Solution, optional A previous model solution to use for generating the summary. If ``None``, the summary method will generate a parsimonious flux distribution (default None). fva : pandas.DataFrame or float, optional Whether or not to include flux variability analysis in the output. If given, `fva` should either be a previous FVA solution matching the model or a float between 0 and 1 representing the fraction of the optimum objective to be searched (default None). Other Parameters ---------------- kwargs : Further keyword arguments are passed on to the parent class. See Also -------- Summary : Parent that has further default parameters. MetaboliteSummary ModelSummary """ super(ReactionSummary, self).__init__(**kwargs) self._reaction = reaction.copy() self._generate(model, solution, fva) def _generate( self, model: "Model", solution: Optional["Solution"], fva: Optional[Union[float, pd.DataFrame]], ) -> None: """ Prepare the data for the summary instance. Parameters ---------- model : cobra.Model The metabolic model for which to generate a metabolite summary. solution : cobra.Solution, optional A previous model solution to use for generating the summary. If ``None``, the summary method will generate a parsimonious flux distribution. fva : pandas.DataFrame or float, optional Whether or not to include flux variability analysis in the output. If given, `fva` should either be a previous FVA solution matching the model or a float between 0 and 1 representing the fraction of the optimum objective to be searched. """ super()._generate(model=model, solution=solution, fva=fva) if solution is None: logger.info("Generating new parsimonious flux distribution.") solution = pfba(model) if isinstance(fva, float): logger.info("Performing flux variability analysis.") fva = flux_variability_analysis( model, reaction_list=[self._reaction], fraction_of_optimum=fva, ) # Create the basic flux table. self._flux = pd.DataFrame( data={"flux": [solution[self._reaction.id]]}, index=[self._reaction.id], ) if fva is not None: self._flux = self._flux.join(fva) def _string_flux( self, threshold: float, float_format: str, ) -> str: """ Transform a flux data frame to a string. Parameters ---------- threshold : float Hide fluxes below the threshold from being displayed. float_format : str Format string for floats. Returns ------- str A string representation of the flux (with ranges). """ if "minimum" in self._flux.columns and "maximum" in self._flux.columns: frame = self._flux.loc[ (self._flux["flux"].abs() >= threshold) | (self._flux["minimum"].abs() >= threshold) | (self._flux["maximum"].abs() >= threshold), :, ].copy() return ( f"{frame.at[self._reaction.id, 'flux']:{float_format}} " f"[{frame.at[self._reaction.id, 'minimum']:{float_format}}; " f"{frame.at[self._reaction.id, 'maximum']:{float_format}}]" ) else: frame = self._flux.loc[self._flux["flux"].abs() >= threshold, :].copy() return f"{frame.at[self._reaction.id, 'flux']:{float_format}}" def to_string( self, names: bool = False, threshold: Optional[float] = None, float_format: str = ".4G", column_width: int = 79, ) -> str: """ Return a pretty string representation of the reaction summary. Parameters ---------- names : bool, optional Whether or not elements should be displayed by their common names (default False). threshold : float, optional Hide fluxes below the threshold from being displayed. If no value is given, the model tolerance is used (default None). float_format : str, optional Format string for floats (default '.4G'). column_width : int, optional The maximum column width for each row (default 79). Returns ------- str The summary formatted as a pretty string. """ threshold = self._normalize_threshold(threshold) if names: header = shorten(self._reaction.name, width=column_width, placeholder="...") else: header = shorten(self._reaction.id, width=column_width, placeholder="...") flux = self._string_flux(threshold, float_format) return dedent( f""" {header} {'=' * len(header)} {self._reaction.build_reaction_string(use_metabolite_names=names)} Bounds: {self._reaction.lower_bound}, {self._reaction.upper_bound} Flux: {flux} """ ) def to_html( self, names: bool = False, threshold: Optional[float] = None, float_format: str = ".4G", ) -> str: """ Return a rich HTML representation of the reaction summary. Parameters ---------- names : bool, optional Whether or not elements should be displayed by their common names (default False). threshold : float, optional Hide fluxes below the threshold from being displayed. If no value is given, the model tolerance is used (default None). float_format : str, optional Format string for floats (default '.4G'). Returns ------- str The summary formatted as HTML. """ threshold = self._normalize_threshold(threshold) if names: header = self._reaction.name else: header = self._reaction.id flux = self._string_flux(threshold, float_format) return ( f"

{header}

" f"

{self._reaction.build_reaction_string(use_metabolite_names=names)}

" f"

Bounds: {self._reaction.lower_bound}, {self._reaction.upper_bound}

" f"

Flux: {flux}

" ) cobrapy-0.29.1/src/cobra/summary/summary.py000066400000000000000000000112401467301562300206700ustar00rootroot00000000000000"""Provide the abstract base summary class.""" import logging from abc import ABC, abstractmethod from typing import TYPE_CHECKING, Optional, Union import pandas as pd if TYPE_CHECKING: from cobra import Model, Solution logger = logging.getLogger(__name__) class Summary(ABC): """ Define the abstract base summary. See Also -------- MetaboliteSummary ReactionSummary ModelSummary """ def __init__( self, **kwargs, ) -> None: """ Initialize a summary. Other Parameters ---------------- kwargs : Further keyword arguments are passed on to the parent class. """ super().__init__(**kwargs) self._flux = None self._tolerance = None def _generate( self, model: "Model", solution: Optional["Solution"], fva: Optional[Union[float, pd.DataFrame]], ) -> None: """ Prepare the data for the summary instance. Parameters ---------- model : cobra.Model The metabolic model for which to generate a metabolite summary. solution : cobra.Solution, optional A previous model solution to use for generating the summary. If ``None``, the summary method will generate a parsimonious flux distribution. fva : pandas.DataFrame or float, optional Whether or not to include flux variability analysis in the output. If given, `fva` should either be a previous FVA solution matching the model or a float between 0 and 1 representing the fraction of the optimum objective to be searched. """ self._tolerance = model.tolerance def __str__(self) -> str: """Return a string representation of the summary.""" return self.to_string() def _repr_html_(self) -> str: """Return a rich HTML representation of the summary.""" return self.to_html() @property def tolerance(self) -> float: """Return the set threshold.""" return self._tolerance def _normalize_threshold(self, threshold: Optional[float]): """Return a sensible threshold value.""" if threshold is None: return self.tolerance else: if threshold < self.tolerance: logger.warning( "The chosen threshold cannot be less than the model's " "tolerance value. Using the tolerance instead." ) return self.tolerance else: return threshold @abstractmethod def to_string( self, names: bool = False, threshold: Optional[float] = None, float_format: str = ".4G", column_width: int = 79, ) -> str: """ Return a pretty string representation of the summary. Parameters ---------- names : bool, optional Whether or not elements should be displayed by their common names (default False). threshold : float, optional Hide fluxes below the threshold from being displayed. If no value is given, the model tolerance is used (default None). float_format : str, optional Format string for floats (default '.4G'). column_width : int, optional The maximum column width for each row (default 79). Returns ------- str The summary formatted as a pretty string. """ raise NotImplementedError( "This method needs to be implemented by the subclass." ) @abstractmethod def to_html( self, names: bool = False, threshold: Optional[float] = None, float_format: str = ".4G", ) -> str: """ Return a rich HTML representation of the metabolite summary. Parameters ---------- names : bool, optional Whether or not elements should be displayed by their common names (default False). threshold : float, optional Hide fluxes below the threshold from being displayed. If no value is given, the model tolerance is used (default None). float_format : str, optional Format string for floats (default '.4G'). Returns ------- str The summary formatted as HTML. """ raise NotImplementedError( "This method needs to be implemented by the subclass." ) def to_frame(self) -> pd.DataFrame: """Return the a data frame representation of the summary.""" return self._flux.copy() cobrapy-0.29.1/src/cobra/util/000077500000000000000000000000001467301562300161035ustar00rootroot00000000000000cobrapy-0.29.1/src/cobra/util/__init__.py000066400000000000000000000002441467301562300202140ustar00rootroot00000000000000from cobra.util.array import * from cobra.util.context import * from cobra.util.solver import * from cobra.util.util import * from cobra.util.process_pool import * cobrapy-0.29.1/src/cobra/util/array.py000066400000000000000000000204551467301562300176010ustar00rootroot00000000000000"""Helper functions for array operations and sampling.""" from typing import TYPE_CHECKING, NamedTuple, Optional, Union import numpy as np import pandas as pd # Used to avoid cyclic reference and enable third-party static type checkers to work if TYPE_CHECKING: from cobra import Model try: from scipy.sparse import dok_matrix, lil_matrix except ImportError: dok_matrix, lil_matrix = None, None def create_stoichiometric_matrix( model: "Model", array_type: str = "dense", dtype: Optional[np.dtype] = None ) -> Union[np.ndarray, dok_matrix, lil_matrix, pd.DataFrame]: """Return a stoichiometric array representation of the given model. The the columns represent the reactions and rows represent metabolites. S[i,j] therefore contains the quantity of metabolite `i` produced (negative for consumed) by reaction `j`. Parameters ---------- model : cobra.Model The cobra model to construct the matrix for. array_type : {"dense", "dok", "lil", "DataFrame"} The type of array to construct. "dense" will return a standard numpy.ndarray. "dok", or "lil" will construct a sparse array using scipy of the corresponding type. "DataFrame" will give a pandas.DataFrame with metabolite as indices and reaction as columns. dtype : numpy.dtype, optional The desired numpy data type for the array (default numpy.float64). Returns ------- matrix of class `dtype` The stoichiometric matrix for the given model. Raises ------ ValueError If sparse matrix is used and scipy is not installed. .. deprecated:: 0.18.1 "DataFrame" option for `array_type` will be replaced with "frame" in future versions. """ if array_type not in ("DataFrame", "dense") and not dok_matrix: raise ValueError("Sparse matrices require scipy.") if dtype is None: dtype = np.float64 array_constructor = { "dense": np.zeros, "dok": dok_matrix, "lil": lil_matrix, "DataFrame": np.zeros, } n_metabolites = len(model.metabolites) n_reactions = len(model.reactions) array = array_constructor[array_type]((n_metabolites, n_reactions), dtype=dtype) m_ind = model.metabolites.index r_ind = model.reactions.index for reaction in model.reactions: for metabolite, stoich in reaction.metabolites.items(): array[m_ind(metabolite), r_ind(reaction)] = stoich if array_type == "DataFrame": metabolite_ids = [met.id for met in model.metabolites] reaction_ids = [rxn.id for rxn in model.reactions] return pd.DataFrame(array, index=metabolite_ids, columns=reaction_ids) else: return array def nullspace(A: np.ndarray, atol: float = 1e-13, rtol: float = 0.0) -> np.ndarray: r"""Compute an approximate basis for the nullspace of A. The algorithm used by this function is based on the Singular Value Decomposition (SVD) of `A`. Parameters ---------- A : numpy.ndarray `A` should be at most 2-D. 1-D array with length k will be treated as a 2-D with shape (1, k). atol : float, optional The absolute tolerance for a zero singular value. Singular values smaller than `atol` are considered to be zero (default 1e-13). rtol : float, optional The relative tolerance. Singular values less than `rtol * smax` are considered to be zero, where `smax` is the largest singular value (default 0.0). Returns ------- numpy.ndarray If `A` is an array with shape (m, k), then `ns` will be an array with shape (k, n), where `n` is the estimated dimension of the nullspace of `A`. The columns of `ns` are a basis for the nullspace; each element in numpy.dot(A, ns) will be approximately zero. Notes ----- This is taken from the numpy cookbook. If both `atol` and `rtol` are positive, the combined tolerance is the maximum of the two; that is: .. math:: \mathtt{tol} = \max(\mathtt{atol}, \mathtt{rtol} * \mathtt{smax}) Singular values smaller than `tol` are considered to be zero. """ A = np.atleast_2d(A) _, s, vh = np.linalg.svd(A) tol = max(atol, rtol * s[0]) nnz = (s >= tol).sum() ns = vh[nnz:].conj().T return ns def constraint_matrices( model: "Model", array_type: str = "dense", zero_tol: float = 1e-6, ) -> NamedTuple: """Create a matrix representation of the problem. This is used for alternative solution approaches that do not use "optlang". The function will construct the equality matrix, inequality matrix and bounds for the complete problem. Parameters ---------- model : cobra.Model The model from which to obtain the LP problem. array_type : {"dense", "dok", "lil", "DataFrame"} The type of array to construct. "dense" will return a standard numpy.ndarray. "dok", or "lil" will construct a sparse array using scipy of the corresponding type. "DataFrame" will give a pandas.DataFrame with metabolite as indices and reaction as columns. zero_tol : float, optional The zero tolerance used to judge whether two bounds are the same (default 1e-6). Returns ------- NamedTuple A named tuple consisting of 6 matrices and 2 vectors: - "equalities" is a matrix `S` such that `S * vars = b`. It includes a row for each constraint and one column for each variable. - "b" is the right side of the equality equation such that `S * vars = b`. - "inequalities" is a matrix M such that `lb <= M * vars <= ub`. It contains a row for each inequality and as many columns as variables. - "bounds" is a compound matrix [lb ub] containing the lower and upper bounds for the inequality constraints in M. - "variable_fixed" is a boolean vector indicating whether the variable at that index is fixed (`lower bound == upper_bound`) and is thus bounded by an equality constraint. - "variable_bounds" is a compound matrix `[lb ub]` containing the lower and upper bounds for all variables. Notes ----- To accomodate non-zero equalities, the problem will add the variable "const_one" which is a variable that equals one. .. deprecated:: 0.18.1 "DataFrame" option for `array_type` will be replaced with "frame" in future versions. """ if array_type not in ("DataFrame", "dense") and not dok_matrix: raise ValueError("Sparse matrices require scipy.") array_builder = { "dense": np.array, "dok": dok_matrix, "lil": lil_matrix, "DataFrame": pd.DataFrame, }[array_type] Problem = NamedTuple( "Problem", [ ("equalities", Union[np.ndarray, dok_matrix, lil_matrix, pd.DataFrame]), ("b", np.ndarray), ("inequalities", Union[np.ndarray, dok_matrix, lil_matrix, pd.DataFrame]), ("bounds", Union[np.ndarray, dok_matrix, lil_matrix, pd.DataFrame]), ("variable_fixed", np.ndarray), ( "variable_bounds", Union[np.ndarray, dok_matrix, lil_matrix, pd.DataFrame], ), ], ) equality_rows = [] inequality_rows = [] inequality_bounds = [] b = [] for const in model.constraints: lb = -np.inf if const.lb is None else const.lb ub = np.inf if const.ub is None else const.ub equality = (ub - lb) < zero_tol coefs = const.get_linear_coefficients(model.variables) coefs = [coefs[v] for v in model.variables] if equality: b.append(lb if abs(lb) > zero_tol else 0.0) equality_rows.append(coefs) else: inequality_rows.append(coefs) inequality_bounds.append([lb, ub]) var_bounds = np.array([[v.lb, v.ub] for v in model.variables]) fixed = var_bounds[:, 1] - var_bounds[:, 0] < zero_tol results = Problem( equalities=array_builder(equality_rows), b=np.array(b), inequalities=array_builder(inequality_rows), bounds=array_builder(inequality_bounds), variable_fixed=np.array(fixed), variable_bounds=array_builder(var_bounds), ) return results cobrapy-0.29.1/src/cobra/util/context.py000066400000000000000000000054241467301562300201460ustar00rootroot00000000000000"""Context manager for the package.""" from functools import partial from typing import TYPE_CHECKING, Any, Callable, Optional if TYPE_CHECKING: from cobra import Object class HistoryManager: """ Define a base context manager. It records a list of actions to be taken at a later time. This is used to implement context managers that allow temporary changes to a `cobra.core.Model`. """ def __init__(self, **kwargs) -> None: """Initialize the class.""" super().__init__(**kwargs) # this acts like a stack self._history = [] def __call__(self, operation: Callable[[Any], Any]) -> None: """Add the corresponding operation to the history stack. Parameters ---------- operation : callable A function to be called at a later time. """ self._history.append(operation) def reset(self) -> None: """Trigger executions for all items in the stack in reverse order.""" while self._history: entry = self._history.pop() entry() def size(self) -> int: """Calculate number of operations on the stack.""" return len(self._history) def get_context(obj: "Object") -> Optional[HistoryManager]: """Search for a context manager. Parameters ---------- obj: cobra.Object The cobra.Object for which to search context manager. Returns ------- HistoryManager or None HistoryManager instance, or None if no context manager is found. Raises ------ AttributeError If no context manager is found. IndexError If no context manager is found. """ # works for cobra.core.Model objects try: return obj._contexts[-1] except (AttributeError, IndexError): pass # works for objects other than cobra.core.Model try: return obj._model._contexts[-1] except (AttributeError, IndexError): pass def resettable(func: Callable[[Any], Any]) -> Callable[[Any], Any]: """ Simplify the context management of simple object attributes. It gets the value of the attribute prior to setting it, and stores a function to set the value to the old value in the `cobra.util.HistoryManager`. Parameters ---------- func: callable The function to decorate. Returns ------- callable The decorated function. """ def wrapper(self, new_value): context = get_context(self) if context: old_value = getattr(self, func.__name__) # Don't clutter the context with unchanged variables if old_value == new_value: return context(partial(func, self, old_value)) func(self, new_value) return wrapper cobrapy-0.29.1/src/cobra/util/process_pool.py000066400000000000000000000067731467301562300212010ustar00rootroot00000000000000"""Provide a process pool with enhanced performance on Windows.""" import multiprocessing import os import pickle from pathlib import Path from platform import system from tempfile import mkstemp from types import TracebackType from typing import Any, Callable, Optional, Tuple, Type __all__ = ("ProcessPool",) def _init_win_worker(filename: str) -> None: """Retrieve worker initialization code from a pickle file and call it.""" with open(filename, mode="rb") as handle: func, *args = pickle.load(handle) func(*args) class ProcessPool: """Define a process pool that handles the Windows platform specially.""" def __init__( self, processes: Optional[int] = None, initializer: Optional[Callable] = None, initargs: Tuple = (), maxtasksperchild: Optional[int] = None, **kwargs, ) -> None: """ Initialize a process pool. Add a thin layer on top of the `multiprocessing.Pool` that, on Windows, passes initialization code to workers via a pickle file rather than directly. This is done to avoid a performance issue that exists on Windows. Please, also see the discussion [1_]. References ---------- .. [1] https://github.com/opencobra/cobrapy/issues/997 """ super().__init__(**kwargs) self._filename = None if initializer is not None and system() == "Windows": descriptor, self._filename = mkstemp(suffix=".pkl") # We use the file descriptor to the open file returned by `mkstemp` to # ensure that the resource is closed and can later be removed. Otherwise # Windows will cause a `PermissionError`. with os.fdopen(descriptor, mode="wb") as handle: pickle.dump((initializer,) + initargs, handle) initializer = _init_win_worker initargs = (self._filename,) self._pool = multiprocessing.Pool( processes=processes, initializer=initializer, initargs=initargs, maxtasksperchild=maxtasksperchild, ) def __getattr__(self, name: str, **kwargs) -> Any: """Defer attribute access to the pool instance.""" return getattr(self._pool, name, **kwargs) def __enter__(self) -> "ProcessPool": """Enable context management.""" self._pool.__enter__() return self def __exit__( self, exc_type: Optional[Type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType], ) -> Optional[bool]: """Clean up resources when leaving a context.""" # The `multiprocessing.Pool.__exit__` only terminates pool processes. For a # clean exit, we close the pool and join the pool processes first. try: self._pool.close() self._pool.join() finally: self._clean_up() result = self._pool.__exit__(exc_type, exc_val, exc_tb) return result def close(self) -> None: """ Close the process pool. Prevent any more tasks from being submitted to the pool. Once all the tasks have been completed, the worker processes will exit. """ try: self._pool.close() finally: self._clean_up() def _clean_up(self) -> None: """Remove the dump file if it exists.""" if self._filename is not None and Path(self._filename).exists(): Path(self._filename).unlink() cobrapy-0.29.1/src/cobra/util/solver.py000066400000000000000000000526061467301562300200000ustar00rootroot00000000000000"""Additional helper functions for the optlang solvers. All functions integrate well with the context manager, meaning that all operations defined here are automatically reverted when used in a `with model:` block. The functions defined here together with the existing model functions should allow you to implement custom flux analysis methods with ease. """ import logging import re from functools import partial from types import ModuleType from typing import TYPE_CHECKING, Dict, List, NamedTuple, Optional, Tuple, Union from warnings import warn import optlang import pandas as pd from optlang.interface import ( FEASIBLE, INFEASIBLE, ITERATION_LIMIT, NUMERIC, OPTIMAL, SUBOPTIMAL, TIME_LIMIT, ) from optlang.symbolics import Basic, Zero from cobra.exceptions import ( OPTLANG_TO_EXCEPTIONS_DICT, OptimizationError, SolverNotFound, ) from cobra.util.context import get_context # Used to avoid cyclic reference and enable third-party static type checkers to work if TYPE_CHECKING: from cobra import Model, Reaction CONS_VARS = Union[optlang.interface.Constraint, optlang.interface.Variable] logger = logging.getLogger(__name__) # Define all the solvers that are found in optlang. solvers = { match.split("_interface")[0]: getattr(optlang, match) for match in dir(optlang) if "_interface" in match and match != "matrix_interface" } # Defines all the QP solvers implemented in optlang. qp_solvers = ["cplex", "gurobi", "hybrid"] # optlang solution statuses which still allow retrieving primal values has_primals = [NUMERIC, FEASIBLE, INFEASIBLE, SUBOPTIMAL, ITERATION_LIMIT, TIME_LIMIT] class Components(NamedTuple): """Define an object for adding absolute expressions.""" variable: optlang.interface.Variable upper_constraint: optlang.interface.Constraint lower_constraint: optlang.interface.Constraint def linear_reaction_coefficients( model: "Model", reactions: Optional[List["Reaction"]] = None ) -> Dict["Reaction", float]: """Retrieve coefficient for the reactions in a linear objective. Parameters ---------- model : cobra.Model The cobra model defining the linear objective. reactions : list of cobra.Reaction, optional An optional list of the reactions to get the coefficients for. By default, all reactions are considered (default None). Returns ------- dict A dictionary where the keys are the reaction objects and the values are the corresponding coefficient. Empty dictionary if there are no linear terms in the objective. """ linear_coefficients = {} reactions = model.reactions if not reactions else reactions try: objective_expression = model.solver.objective.expression coefficients = objective_expression.as_coefficients_dict() except AttributeError: return linear_coefficients for rxn in reactions: forward_coefficient = coefficients.get(rxn.forward_variable, 0) reverse_coefficient = coefficients.get(rxn.reverse_variable, 0) if forward_coefficient != 0: if forward_coefficient == -reverse_coefficient: linear_coefficients[rxn] = float(forward_coefficient) return linear_coefficients def _valid_atoms(model: "Model", expression: optlang.symbolics.Basic) -> bool: """Check whether a sympy expression references the correct variables. Parameters ---------- model : cobra.Model The model in which to check for variables. expression : sympy.Basic A sympy expression. Returns ------- bool True if all referenced variables are contained in model, False otherwise. """ atoms = expression.atoms(optlang.interface.Variable) return all(a.problem is model.solver for a in atoms) def set_objective( model: "Model", value: Union[ optlang.interface.Objective, optlang.symbolics.Basic, Dict["Reaction", float], ], additive: bool = False, ) -> None: """Set the model objective. Parameters ---------- model : cobra.Model The model to set the objective for. value : optlang.interface.Objective, optlang.symbolics.Basic, dict If the model objective is linear, then the value can be a new optlang.interface.Objective or a dictionary with linear coefficients where each key is a reaction and the corresponding value is the new coefficient (float). If the objective is non-linear and `additive` is True, then only values of class optlang.interface.Objective, are accepted. additive : bool If True, add the terms to the current objective, otherwise start with an empty objective. Raises ------ ValueError If model objective is non-linear and the `value` is a dict. TypeError If the type of `value` is not one of the accepted ones. """ interface = model.problem reverse_value = model.solver.objective.expression reverse_value = interface.Objective( reverse_value, direction=model.solver.objective.direction, sloppy=True ) if isinstance(value, dict): if not model.objective.is_Linear: raise ValueError( "You can only update non-linear objectives additively using object of " f"class optlang.interface.Objective, not of {type(value)}" ) if not additive: model.solver.objective = interface.Objective( Zero, direction=model.solver.objective.direction ) for reaction, coef in value.items(): model.solver.objective.set_linear_coefficients( {reaction.forward_variable: coef, reaction.reverse_variable: -coef} ) elif isinstance(value, (Basic, optlang.interface.Objective)): if isinstance(value, Basic): value = interface.Objective( value, direction=model.solver.objective.direction, sloppy=False ) # Check whether expression only uses variables from current model; # clone the objective if not, faster than cloning without checking if not _valid_atoms(model, value.expression): value = interface.Objective.clone(value, model=model.solver) if not additive: model.solver.objective = value else: model.solver.objective += value.expression else: raise TypeError(f"{value} is not a valid objective for {model.solver}.") context = get_context(model) if context: def reset(): model.solver.objective = reverse_value model.solver.objective.direction = reverse_value.direction context(reset) def interface_to_str(interface: Union[str, ModuleType]) -> str: """Give a string representation for an optlang interface. Parameters ---------- interface : str, ModuleType Full name of the interface in optlang or cobra representation. For instance, 'optlang.glpk_interface' or 'optlang-glpk'. Returns ------- str The name of the interface as a string. """ if isinstance(interface, ModuleType): interface = interface.__name__ return re.sub(r"optlang.|.interface", "", interface) def get_solver_name(mip: bool = False, qp: bool = False) -> str: """Select a solver for a given optimization problem. Parameters ---------- mip : bool True if the solver requires mixed integer linear programming capabilities. qp : bool True if the solver requires quadratic programming capabilities. Returns ------- str The name of the feasible solver. Raises ------ SolverNotFound If no suitable solver could be found. """ if len(solvers) == 0: raise SolverNotFound("No solvers found.") # Those lists need to be updated as optlang implements more solvers mip_order = ["gurobi", "cplex", "hybrid", "glpk"] lp_order = ["glpk", "hybrid", "cplex", "gurobi"] qp_order = ["cplex", "gurobi", "hybrid"] if mip is False and qp is False: for solver_name in lp_order: if solver_name in solvers: return solver_name # none of them are in the list order - so return the first one return list(solvers)[0] elif qp: # mip does not yet matter for this determination for solver_name in qp_order: if solver_name in solvers: return solver_name raise SolverNotFound("No QP-capable solver found.") else: for solver_name in mip_order: if solver_name in solvers: return solver_name raise SolverNotFound("No MIP-capable solver found.") def choose_solver( model: "Model", solver: Optional[str] = None, qp: bool = False ) -> ModuleType: """Choose a solver given a solver name and model. This will choose a solver compatible with the model and required capabilities. Also respects model.solver where it can. Parameters ---------- model : cobra.Model The model for which to choose the solver. solver : str, optional The name of the solver to be used (default None). qp : boolean, optional True if the solver needs quadratic programming capabilities (default False). Returns ------- optlang.interface Valid solver for the problem. Raises ------ SolverNotFound If no suitable solver could be found. """ if solver is None: solver = model.problem else: model.solver = solver # Check for QP, raise error if no QP solver found if qp and interface_to_str(solver) not in qp_solvers: solver = solvers[get_solver_name(qp=True)] return solver def check_solver(obj): """Check whether the chosen solver is valid. Check whether chosen solver is valid and also warn when using a specialized solver. Will return the optlang interface for the requested solver. Parameters ---------- obj : str or optlang.interface or optlang.interface.Model The chosen solver. Raises ------ SolverNotFound If the solver is not valid. """ not_valid_interface = SolverNotFound( f"{obj} is not a valid solver interface. Pick one from {', '.join(solvers)}." ) if isinstance(obj, str): try: interface = solvers[interface_to_str(obj)] except KeyError: raise not_valid_interface elif isinstance(obj, ModuleType) and hasattr(obj, "Model"): interface = obj elif isinstance(obj, optlang.interface.Model): interface = obj.interface else: raise not_valid_interface if interface_to_str(interface) in ["osqp", "coinor_cbc"]: logger.warning( "OSQP and CBC are specialized solvers for quadratic programming (QP) and " "mixed-integer programming (MIP) problems and may not perform well on " "general LP problems. So unless you intend to solve a QP or MIP problem, " "we recommend to change the solver back to a general purpose solver " "like `model.solver = 'glpk'` for instance." ) return interface def add_cons_vars_to_problem( model: "Model", what: Union[List[CONS_VARS], Tuple[CONS_VARS], Components], **kwargs, ) -> None: """Add variables and constraints to a model's solver object. Useful for variables and constraints that can not be expressed with reactions and lower/upper bounds. It will integrate with the model's context manager in order to revert changes upon leaving the context. Parameters ---------- model : cobra.Model The model to which to add the variables and constraints. what : list or tuple of optlang.interface.Variable or optlang.interface.Constraint The variables and constraints to add to the model. **kwargs : keyword arguments Keyword arguments passed to solver's add() method. """ model.solver.add(what, **kwargs) context = get_context(model) if context: context(partial(model.solver.remove, what)) def remove_cons_vars_from_problem( model: "Model", what: Union[List[CONS_VARS], Tuple[CONS_VARS], Components], ) -> None: """Remove variables and constraints from a model's solver object. Useful to temporarily remove variables and constraints from a model's solver object. Parameters ---------- model : cobra.Model The model from which to remove the variables and constraints. what : list or tuple of optlang.interface.Variable or optlang.interface.Constraint The variables and constraints to remove from the model. """ model.solver.remove(what) context = get_context(model) if context: context(partial(model.solver.add, what)) def add_absolute_expression( model: "Model", expression: str, name: str = "abs_var", ub: Optional[float] = None, difference: float = 0.0, add: bool = True, ) -> Components: """Add the absolute value of an expression to the model. Also defines a variable for the absolute value that can be used in other objectives or constraints. Parameters ---------- model : cobra.Model The model to which to add the absolute expression. expression : str Must be a valid symbolic expression within the model's solver object. The absolute value is applied automatically on the expression. name : str, optional The name of the newly created variable (default "abs_var"). ub : positive float, optional The upper bound for the variable (default None). difference : positive float, optional The difference between the expression and the variable (default 0.0). add : bool, optional Whether to add the variable to the model at once (default True). Returns ------- Components A named tuple with variable and two constraints (upper_constraint, lower_constraint) describing the new variable and the constraints that assign the absolute value of the expression to it. """ variable = model.problem.Variable(name, lb=0, ub=ub) # The following constraints enforce variable > expression and # variable > -expression upper_constraint = model.problem.Constraint( expression - variable, ub=difference, name="abs_pos_" + name ) lower_constraint = model.problem.Constraint( expression + variable, lb=difference, name="abs_neg_" + name ) to_add = Components(variable, upper_constraint, lower_constraint) if add: add_cons_vars_to_problem(model, to_add) return to_add def fix_objective_as_constraint( model: "Model", fraction: float = 1.0, bound: Optional[float] = None, name: str = "fixed_objective_{}", ) -> float: """Fix current objective as an additional constraint. When adding constraints to a model, such as done in pFBA which minimizes total flux, these constraints can become too powerful, resulting in solutions that satisfy optimality but sacrifices too much for the original objective function. To avoid that, we can fix the current objective value as a constraint to ignore solutions that give a lower (or higher depending on the optimization direction) objective value than the original model. When done with the model as a context, the modification to the objective will be reverted when exiting that context. Parameters ---------- model : cobra.Model The model to operate on. fraction : float, optional The fraction of the optimum the objective is allowed to reach (default 1.0). bound : float, optional The bound to use instead of fraction of maximum optimal value. If not None, `fraction` is ignored (default None). name : str, optional Name of the objective. May contain one "{}" placeholder which is filled with the name of the old objective (default "fixed_objective_{}"). Returns ------- float The value of the optimized objective * fraction """ fix_objective_name = name.format(model.objective.name) if fix_objective_name in model.constraints: model.solver.remove(fix_objective_name) if bound is None: bound = model.slim_optimize(error_value=None) * fraction if model.objective.direction == "max": ub, lb = None, bound else: ub, lb = bound, None constraint = model.problem.Constraint( model.objective.expression, name=fix_objective_name, ub=ub, lb=lb ) add_cons_vars_to_problem(model, constraint, sloppy=True) return bound def check_solver_status(status: str = None, raise_error: bool = False) -> None: """Perform standard checks on a solver's status. Parameters ---------- status: str, optional The status string obtained from the solver (default None). raise_error: bool, optional If True, raise error or display warning if False (default False). Returns ------- None Warns ----- UserWarning If `status` is not optimal and `raise_error` is set to True. Raises ------ OptimizationError If `status` is None or is not optimal and `raise_error` is set to True. """ if status == OPTIMAL: return None elif (status in has_primals) and not raise_error: warn(f"Solver status is '{status}'.", UserWarning) elif status is None: raise OptimizationError( "Model is not optimized yet or solver context has been switched." ) else: raise OptimizationError(f"Solver status is '{status}'.") def assert_optimal(model: "Model", message: str = "Optimization failed") -> None: """Assert model solver status is optimal. Do nothing if model solver status is optimal, otherwise throw appropriate exception depending on the status. Parameters ---------- model : cobra.Model The model to check the solver status for. message : str, optional Message for the exception if solver status is not optimal (default "Optimization failed"). Returns ------- None Raises ------ OptimizationError If solver status is not optimal. """ status = model.solver.status if status != OPTIMAL: exception_cls = OPTLANG_TO_EXCEPTIONS_DICT.get(status, OptimizationError) raise exception_cls(f"{message} ({status}).") def add_lp_feasibility(model: "Model") -> None: """Add a new objective and variables to ensure a feasible solution. The optimized objective will be zero for a feasible solution and otherwise represent the distance from feasibility (please see [1]_ for more information). Parameters ---------- model : cobra.Model The model whose feasibility is to be tested. Returns ------- None References ---------- .. [1] Gomez, Jose A., Kai Höffner, and Paul I. Barton. “DFBAlab: A Fast and Reliable MATLAB Code for Dynamic Flux Balance Analysis.” BMC Bioinformatics 15, no. 1 (December 18, 2014): 409. https://doi.org/10.1186/s12859-014-0409-8. """ obj_vars = [] prob = model.problem for met in model.metabolites: s_plus = prob.Variable("s_plus_" + met.id, lb=0) s_minus = prob.Variable("s_minus_" + met.id, lb=0) model.add_cons_vars([s_plus, s_minus]) model.constraints[met.id].set_linear_coefficients({s_plus: 1.0, s_minus: -1.0}) obj_vars.append(s_plus) obj_vars.append(s_minus) model.objective = prob.Objective(Zero, sloppy=True, direction="min") model.objective.set_linear_coefficients({v: 1.0 for v in obj_vars}) def add_lexicographic_constraints( model: "Model", objectives: List["Reaction"], objective_direction: Union[str, List[str]] = "max", ) -> pd.Series: """Successively optimize separate targets in a specific order. For each objective, optimize the model and set the optimal value as a constraint. Proceed in the order of the objectives given. Due to the specific order this is called lexicographic FBA [1]_. This procedure is useful for returning unique solutions for a set of important fluxes. Typically this is applied to exchange fluxes. Parameters ---------- model : cobra.Model The model to be optimized. objectives : list of cobra.Reaction A list of reactions (or objectives) in the model for which unique fluxes are to be determined. objective_direction : str or list of str, optional The desired objective direction for each reaction (if a list) or the objective direction to use for all reactions (default "max"). Returns ------- pandas.Series A pandas Series containing the optimized fluxes for each of the given reactions in `objectives`. References ---------- .. [1] Gomez, Jose A., Kai Höffner, and Paul I. Barton. “DFBAlab: A Fast and Reliable MATLAB Code for Dynamic Flux Balance Analysis.” BMC Bioinformatics 15, no. 1 (December 18, 2014): 409. https://doi.org/10.1186/s12859-014-0409-8. """ if type(objective_direction) is not list: objective_direction = [objective_direction] * len(objectives) constraints = [] for rxn_id, obj_dir in zip(objectives, objective_direction): model.objective = model.reactions.get_by_id(rxn_id) model.objective_direction = obj_dir constraints.append(fix_objective_as_constraint(model)) return pd.Series(constraints, index=objectives) cobrapy-0.29.1/src/cobra/util/util.py000066400000000000000000000024771467301562300174440ustar00rootroot00000000000000"""General utilities used across the package.""" import textwrap from typing import Any from depinfo.application import DisplayApplication def format_long_string(string: str, max_length: int = 50) -> str: """Shorten long string into a small string with ellipsis. Parameters ---------- string: str The long string to shorten. max_length: int, optional The maximum length after which to append ellipsis (default 50). Returns ------- str The shortened string. """ return textwrap.shorten(string, width=max_length, placeholder="...") class AutoVivification(dict): """ Implementation of Perl's autovivification feature. Notes ----- For more information, check https://stackoverflow.com/a/652284/280182 . """ def __getitem__(self, item: Any) -> Any: """Retrieve if item is found, else add it. Parameters ---------- item: Any The object to look for. Returns ------- Any The retrieved object. """ try: value = super().__getitem__(item) except KeyError: value = self[item] = type(self)() return value def show_versions() -> None: """Print dependency information.""" DisplayApplication.run("cobra") cobrapy-0.29.1/tests/000077500000000000000000000000001467301562300144135ustar00rootroot00000000000000cobrapy-0.29.1/tests/conftest.py000066400000000000000000000117161467301562300166200ustar00rootroot00000000000000"""Define global fixtures.""" from pathlib import Path from pickle import load as _load from typing import List, Tuple import importlib_resources import pytest import cobra from cobra import Metabolite, Model, Reaction, Solution from cobra.io import read_sbml_model from cobra.util import solver as sutil data_dir = Path(__file__).parent / "data" cobra_data_dir = importlib_resources.files(cobra.data) def create_test_model(model_name: str = "salmonella") -> Model: """Return a cobra model for testing. Parameters ---------- model_name: str One of 'ecoli', 'textbook', or 'salmonella', or the path to a pickled cobra.Model . Returns ------- cobra.Model The cobra model. Raises ------ OSError If no file is found at the path specified by `model_name`. """ if model_name == "ecoli": ecoli_sbml = str((cobra_data_dir / "iJO1366.xml.gz").resolve()) return read_sbml_model(ecoli_sbml) elif model_name == "textbook": textbook_sbml = str((cobra_data_dir / "textbook.xml.gz").resolve()) return read_sbml_model(textbook_sbml) elif model_name == "mini": mini_sbml = str((data_dir / "mini_fbc2.xml").resolve()) return read_sbml_model(mini_sbml) elif model_name == "salmonella": salmonella_pickle = str((data_dir / "salmonella.pickle").resolve()) model_name = salmonella_pickle with open(model_name, mode="rb") as infile: return _load(infile) @pytest.fixture(scope="session") def data_directory() -> Path: """Provide session-level fixture for test data directory.""" return data_dir @pytest.fixture(scope="session") def cobra_data_directory() -> Path: """Provide session-level fixture for cobra data directory.""" return Path(cobra_data_dir) @pytest.fixture(scope="session") def empty_once() -> Model: """Provide session-level fixture for empty model.""" return Model() @pytest.fixture(scope="function") def empty_model(empty_once: Model) -> Model: """Provide function-level fixture for empty model.""" return empty_once.copy() @pytest.fixture(scope="session") def small_model() -> Model: """Provide session-level fixture for textbook model.""" return create_test_model("textbook") @pytest.fixture(scope="function") def model(small_model: Model) -> Model: """Provide function-level fixture for textbook model.""" return small_model.copy() @pytest.fixture(scope="session") def large_once() -> Model: """Provide session-level fixture for ecoli model.""" return create_test_model("ecoli") @pytest.fixture(scope="function") def large_model(large_once: Model) -> Model: """Provide function-level fixture for ecoli model.""" return large_once.copy() @pytest.fixture(scope="session") def medium_model() -> Model: """Provide session-level fixture for salmonella model.""" return create_test_model("salmonella") @pytest.fixture(scope="function") def salmonella(medium_model: Model) -> Model: """Provide function-level fixture for salmonella model.""" return medium_model.copy() @pytest.fixture(scope="function") def solved_model(data_directory: Path) -> Tuple[Solution, Model]: """Provide function-level fixture for solved textbook model.""" model = create_test_model("textbook") with (data_directory / "textbook_solution.pickle").open(mode="rb") as infile: solution = _load(infile) return solution, model @pytest.fixture(scope="session") def tiny_toy_model() -> Model: """Provide session-level fixture for tiny toy model.""" tiny = Model("Toy Model") m1 = Metabolite("M1") d1 = Reaction("ex1") d1.add_metabolites({m1: -1}) d1.upper_bound = 0 d1.lower_bound = -1000 tiny.add_reactions([d1]) tiny.objective = "ex1" return tiny stable_optlang = ["glpk", "cplex", "gurobi"] all_solvers = ["optlang-" + s for s in stable_optlang if s in sutil.solvers] @pytest.fixture(params=all_solvers, scope="session") def opt_solver(request: pytest.FixtureRequest) -> str: """Provide session-level fixture for parametrized optlang solver names.""" return request.param @pytest.fixture(scope="function") def metabolites(model: Model, request: pytest.FixtureRequest) -> List[Metabolite]: """Provide function-level fixture for metabolite set based on `request`.""" if request.param == "exchange": return [ met for met in model.metabolites if met.compartment == "e" and "EX_" + met.id not in model.reactions ] elif request.param == "demand": return [ met for met in model.metabolites if met.compartment == "c" and "DM_" + met.id not in model.reactions ] elif request.param == "sink": return [ met for met in model.metabolites if met.compartment == "c" and "SK_" + met.id not in model.reactions ] else: raise ValueError("Unknown metabolites {}".format(request.param)) cobrapy-0.29.1/tests/data/000077500000000000000000000000001467301562300153245ustar00rootroot00000000000000cobrapy-0.29.1/tests/data/AntCore.mat000066400000000000000000000055051467301562300173670ustar00rootroot00000000000000MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Wed May 17 21:28:21 2017 IM xX͎0-e H.9qu$XY5ދ# i-GW:c|gsED~]K̨ kU e; 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C 0 JF$ y=u]^3!7" hl U51 @@t,!C6`Ca.w{%ӝ P100M,!axލny Pyv1 0A!K)/[=1 @@!K/w ֭l]3  Om7wء nP5$ܙQ#;Ez2vwժ1 0 0 R %d#U~rLd,1 0A!Kk^9Ρ00>t^ X5ಗkf>!0AJ0C;#.{10 0+R bNB]']M\_֡@A:tZhf^E& FzΡ0ߌѻ#5;ӵ|$!0JDS_]1 @@!K/w ֭u|pA :t,UB5` ̮&?.՚.{fpN100M:p(`F.Y  Om7wc`>!i6cobrapy-0.29.1/tests/data/JSON_with_inf_bounds.json000066400000000000000000000055121467301562300222340ustar00rootroot00000000000000{ "metabolites": [ { "id": "Glcxt", "name": "glucose", "compartment": "bioreactor", "charge": 0, "formula": "" }, { "id": "Ac", "name": "acetate", "compartment": "bioreactor", "charge": 0, "formula": "" }, { "id": "O2", "name": "oxygen", "compartment": "bioreactor", "charge": 0, "formula": "" }, { "id": "X", "name": "biomass", "compartment": "bioreactor", "charge": 0, "formula": "" } ], "reactions": [ { "id": "v1", "name": "v1 (39.43 Ac + 35 O2 -> X)", "metabolites": { "Ac": -39.43, "O2": -35, "X": 1 }, "lower_bound": 0, "upper_bound": "inf", "gene_reaction_rule": "", "objective_coefficient": 1 }, { "id": "v2", "name": "v2 (9.46 Glcxt + 12.92 O2 -> X)", "metabolites": { "Glcxt": -9.46, "O2": -12.92, "X": 1 }, "lower_bound": 0, "upper_bound": "inf", "gene_reaction_rule": "", "objective_coefficient": 1 }, { "id": "v3", "name": "v3 (9.84 Glcxt + 12.73 O2 -> 1.24 Ac + X)", "metabolites": { "Ac": 1.24, "Glcxt": -9.84, "O2": -12.73, "X": 1 }, "lower_bound": 0, "upper_bound": "inf", "gene_reaction_rule": "", "objective_coefficient": 1 }, { "id": "v4", "name": "v4 (19.23 Glcxt -> 12.12 Ac + X)", "metabolites": { "Ac": 12.12, "Glcxt": -19.23, "X": 1 }, "lower_bound": 0, "upper_bound": "inf", "gene_reaction_rule": "", "objective_coefficient": 1 }, { "id": "EX_Ac", "name": "EX_Ac", "metabolites": { "Ac": -1 }, "lower_bound": "-inf", "upper_bound": "inf", "gene_reaction_rule": "", "annotation": { "sbo": "SBO:0000627" } }, { "id": "EX_O2", "name": "EX_O2", "metabolites": { "O2": -1 }, "lower_bound": -10, "upper_bound": 0, "gene_reaction_rule": "", "annotation": { "sbo": "SBO:0000627" } }, { "id": "EX_Glcxt", "name": "EX_Glcxt", "metabolites": { "Glcxt": -1 }, "lower_bound": -15, "upper_bound": 0, "gene_reaction_rule": "", "annotation": { "sbo": "SBO:0000627" } }, { "id": "EX_X", "name": "EX_X", "metabolites": { "X": -1 }, "lower_bound": "-inf", "upper_bound": "inf", "gene_reaction_rule": "", "annotation": { "sbo": "SBO:0000627" } } ], "genes": [], "id": "fbc_ex1_1", "name": "fbc_ex1_1", "compartments": { "bioreactor": "bioreactor" }, "version": "1" } cobrapy-0.29.1/tests/data/annotation.xml000066400000000000000000000353701467301562300202300ustar00rootroot00000000000000 cobrapy-0.29.1/tests/data/cardiac_mit_glcuptake_atpmax.mat000066400000000000000000000131451467301562300237030ustar00rootroot00000000000000MATLAB 5.0 MAT-file, Platform: MACI, Created on: Wed Jun 5 09:37:49 2013 IMxYn0P71.&6q q$3WxKV)|||{I}Ӑգh ̃ Y= WP硆0y6wf7s^p}xMxzy|CK^D6.ivZۅ$G{9H"n soHrPVc*n!lxv/+]}C3_HzrNA<#qC-]Uukdo?zt_r>M['%5X׶_W`͏~Cx?{I^?#w}]o1_g1 c{qo!Dzg䟩o=<_JpճLmӟmo/%|FE SڐPkskC@&nEpyg%eՠϛfn0SV,yXTjSt7vk;vD<6waǞɐ*OG}s˱C(zz':$G8&8y_Q`~ƥ6|n1t~rTHsxafxowO*4BӉ=]lstCךӋqsY3ѻSP?t' Slg9}!y:w^ӝFgйg]h!#: Gsu#oO0;3ж^ 5.cMĝ+45kA~v9puR4ܓ;|ɇV\Kyy}s}]\3ׁvʿ--\9YJV~Egso% >+aɓ64QEh3dEJgN<[{ DdIR:/oFuyzk^>Wtvg'lVV'̚y|ʲRȷ?@^h?>e OY2~~Ӣ|@V{g(^PCyW0"/i \7 e}^=Dwç8@-UB`?U;1B+_h;_+p]6Sw OŸ!{X OPI i:"J'1 u@# H+^Me'}]߫Bj:4t;R)`C:d#:fڌQ!ay9%oʁI׷υk}>/w D}7 /I yTg-PzΓhzpE5\3yB? RT=Qw7FUBë߾Tqx8$ 9Pn\of#Q!6~;8aLJQً6%ǶӉBN9o',c;`j\TsM43x3|1fUhU=E\83P83pW]#O%VdWL]Tʯ ^1fM<N"=SW^oP~ E~i"Qu oUk9jqs)C (^y5<[(ŸeQVg΄MSf*^C'Y to06_"-~GY?߼v+io?tNpz%N"幄3s q/5q掋ޯߥd|p{W~z{߰_5>zVO}: }\ڽxx[;{ xx.e?gsԛ{x-MH?엾ZcpqL%~ ܗ UǏglaeZZdJHK!Iz<Ҧ䵒JBRKdyV(5djR=wwsqg>s;{:,K( ^5 LƋI,(P ԷEzpρ`e, pW Sv vJ,k@Ko2iK&xҏaS^,r; h{q gb Xg)":m͸g y Vro[yU\Yg H0V韜 `]tK9ܺ2}!fb_c߁Kyd꼟m~/:eJ j2JSߥy}ӌ (Ko}S_ܧ|fuNѕZWSWT޺_`ֹd7pAޡ[ɹk}_9֝9'l4>u[sR~L#y{1(y[M%J-ټ7]~onS )o>pϽ&_ޏ-hRҾ%u5m$Ird')KK ʛCr{EG n<$ &5Nj YRK|SޑR#]&5SބR[Hޖ"y9Ij_oqyK\Ko\?~')p5HuA}4 A Hi'`Ɓ d ,@.XV<flErP*Qp~'I jHA hAsAΠ H= @:2p0c80LS fl0 ",`%Xւ `#(AJ.8 p ~?A5N-P AcZRAW tgI6c0ٺc*VOS)e!w#w;ϵyN9՟w_*y^W~}?}B[_+}Hyz&ow{V ȼPe~ ͺUKv;b]0?ԸBST3yddIo_}N׏Geg\m^h]%Ӿ$oZ#w{Zǹ8ԋ3w?3i)o\]bό{Zݹs^/|۷_}zs 7#hM v^H/Tm;ܗ9)X acPz?]]zbC 8@T 飶:j'6oŻ2UzQ:'_H|OtL-{I /zap M:pa@XY\ZPXRXR@cobrapy-0.29.1/tests/data/compartments.mat000066400000000000000000000251401467301562300205450ustar00rootroot00000000000000MATLAB 5.0 MAT-file Platform: posix, Created on: Mon Apr 25 09:36:42 2022IM)testCompartmentsmetsmetNamesmetFormulasmetChargesmetKEGGIDmetBiGGIDmetPubChemSubstancemetCasNumbergenesgrRulesrxnsrxnNamessubSystemsSlbubbcrevdescription`@  glc__D_csf@  glc__D_bcK8glc__D_a@  glc__D_luSI@  glc__D_luLI@  glc__D_luP@  glc__D_aL@  glc__D_feh@  D-Glucose@  D-Glucose@  D-Glucose@  D-Glucose@  D-Glucose@  D-Glucose@  D-Glucose@  D-Glucose(8C6H12O68C6H12O68C6H12O68C6H12O68C6H12O68C6H12O68C6H12O68C6H12O6p @(8C000318C000318C000318C000318C000318C000318C000318C00031(8glc__D8glc__D8glc__D8glc__D8glc__D8glc__D8glc__D8glc__D0333303333033330333303333033330333303333(850-99-7850-99-7850-99-7850-99-7850-99-7850-99-7850-99-7850-99-7(p000000000000000P@  csf_t_bcKr8bcK_t_ar@  a_t_luSIr@  luSI_t_luLIr@  luLI_t_luPr@  luP_t_aLr8aL_t_fer@  DM_glc__D_csf@  DM_glc__D_bcK@  DM_glc__D_a@DM_glc__D_luSI@DM_glc__D_luLI@  DM_glc__D_luP@  DM_glc__D_aL@  DM_glc__D_feX$$Transfer from csf to bcK, reversibleX""Transfer from bcK to a, reversibleX##Transfer from a to luSI, reversibleX&&Transfer from luSI to luLI, reversibleX%%Transfer from luLI to luP, reversibleX##Transfer from luP to aL, reversibleX""Transfer from aL to fe, reversible@D-Glucose demand@D-Glucose demand@D-Glucose demand@D-Glucose demand@D-Glucose demand@D-Glucose demand@D-Glucose demand@D-Glucose demandp000000000000000 ??????? x@@@@@@@ x@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@p @ x? x@testCompartmentscobrapy-0.29.1/tests/data/e_coli_core.xml000066400000000000000000017057651467301562300203350ustar00rootroot00000000000000

e_coli_core - Escherichia coli str. K-12 substr. MG1655

Description

This is a metabolism model of Escherichia coli str. K-12 substr. MG1655 in SBML format.

The content of this model has been carefully created in a manual research effort. This file has been exported from the software COBRApy and further processed with the JSBML-based ModelPolisher application.
This file has been produced by the Systems Biology Research Group using BiGG Models knowledge-base version of Feb 24, 2018, where it is currently hosted and identified by: e_coli_core.

Terms of use

Copyright © 2017 The Regents of the University of California.

Redistribution and use of any part of this model from BiGG Models knowledge-base, with or without modification, are permitted provided that the following conditions are met:

  1. Redistributions of this SBML file must retain the above copyright notice, this list of conditions and the following disclaimer.
  2. Redistributions in a different form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
This model is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

For specific licensing terms about this particular model and regulations of commercial use, see this model in BiGG Models knowledge-base.

References

When using content from BiGG Models knowledge-base in your research works, please cite
King ZA, Lu JS, Dräger A, Miller PC, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, and Lewis NE. (2015).
BiGG Models: A platform for integrating, standardizing, and sharing genome-scale models. Nucl Acids Res. doi:10.1093/nar/gkv1049
cobrapy-0.29.1/tests/data/example_notes.xml000066400000000000000000000243541467301562300207210ustar00rootroot00000000000000 CHARGE: -1 FORMULA: C4H7O3 SMILES: CCC(O)C(O)=O CHARGE: -1 FORMULA: C21H26N7O14P2 SMILES: NC(=O)c1ccc[n+](c1)[C@@H]1O[C@H](COP([O-])(=O)OP([O-])(=O)OC[C@H]2O[C@H]([C@H](O)[C@@H]2O)n2cnc3c(N)ncnc23)[C@@H](O)[C@H]1O CHARGE: 1 FORMULA: H SMILES: [1H+] CHARGE: -1 FORMULA: C4H5O3 SMILES: CCC(=O)C([O-])=O CHARGE: -2 FORMULA: C21H27N7O14P2 SMILES: NC(=O)C1=CN(C=CC1)[C@@H]1O[C@H](COP([O-])(=O)OP([O-])(=O)OC[C@H]2O[C@H]([C@H](O)[C@@H]2O)n2cnc3c(N)ncnc23)[C@@H](O)[C@H]1O CONFIDENCE_LEVEL: 4 GENE_ASSOCIATION: (HGNC:8546 and HGNC:8548) or (HGNC:8547 and HGNC:8548) NOTES: NCD SUBSYSTEM: Propanoate metabolism FLUX_VALUE cobrapy-0.29.1/tests/data/fbc_ex1.xml000066400000000000000000000247111467301562300173620ustar00rootroot00000000000000 Koenig Matthias koenigmx@hu-berlin.de Humboldt-University Berlin, Institute for Theoretical Biology 2019-03-06T14:40:55Z 2019-03-06T14:40:55Z cobrapy-0.29.1/tests/data/fbc_ex2.xml000066400000000000000000000235641467301562300173700ustar00rootroot00000000000000 Koenig Matthias koenigmx@hu-berlin.de Humboldt-University Berlin, Institute for Theoretical Biology 2019-03-06T14:40:55Z 2019-03-06T14:40:55Z cobrapy-0.29.1/tests/data/iJO1366.pickle000066400000000000000000153726361467301562300175450ustar00rootroot00000000000000ccobra.core.model Model q)q}q(X_idqXiJO1366qXnameqX)Escherichia coli str. K-12 substr. MG1655qXnotesq}q(XaThis is a metabolism model of Escherichia coli str. K-12 substr. MG1655 in SBML format.q XRedistribution and use of any part of this model from BiGG Models knowledge-base, with or without modification, are permitted provided that the following conditions are metq X=
  1. Redistributions of this SBML file must retain the above copyright notice, this list of conditions and the following disclaimer.
  2. Redistributions in a different form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
This model is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.q XvFor specific licensing terms about this particular model and regulations of commercial use, see this model in BiGG Models knowledge-base.quX _annotationq}q(XtaxonomyqX511145qX bigg.modelqXiJO1366qXpubmedqX21988831quXgenesqccobra.core.dictlist DictList q)Rq(ccobra.core.gene Gene q)q}q(hXb1377qhXqh}qh}q (Xuniprotq!XP77747q"Xasapq#X ABE-0004608q$Xecogeneq%XEG13375q&Xncbigeneq'X946313q(Xncbigiq)X gi:16129338q*uX_modelq+NX _reactionq,c__builtin__ set q-]q.q/Rq0X _functionalq1ubh)q2}q3(hXb2215q4hhh}q5h}q6(Xuniprotq7XP06996q8Xasapq9X ABE-0007321q:Xecogeneq;XEG10670qXncbigiq?X gi:16130152q@uh+Nh,h-]qAqBRqCh1ubh)qD}qE(hXb0929qFhhh}qGh}qH(XuniprotqIXP02931qJXasapqKX ABE-0003153qLXecogeneqMXEG10671qNXncbigeneqOX945554qPXncbigiqQX gi:16128896qRuh+Nh,h-]qSqTRqUh1ubh)qV}qW(hXb0241qXhhh}qYh}qZ(Xuniprotq[XP02932q\Xasapq]X 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Rr!h1ubh)r"}r#(hXb4311r$hhh}r%h}r&(Xuniprotr'XP69856r(Xasapr)X ABE-0014136r*Xecogener+XEG12389r,Xncbigener-X946843r.Xncbigir/X gi:90111728r0uh+Nh,h-]r1r2Rr3h1ubh)r4}r5(hXb3225r6hhh}r7h}r8(Xuniprotr9XP0A6L4r:Xasapr;X ABE-0010579r<Xecogener=XEG10637r>Xncbigener?X947742r@XncbigirAX gi:16131115rBuh+Nh,h-]rCrDRrEh1ubh)rF}rG(hXb0221rHhhh}rIh}rJ(XuniprotrKXQ47146rLXasaprMX ABE-0000743rNXecogenerOXEG13145rPXncbigenerQX949007rRXncbigirSX gi:90111100rTuh+Nh,h-]rUrVRrWh1ubh)rX}rY(hXb1091rZhhh}r[h}r\(Xuniprotr]XP0A6R0r^Xasapr_X ABE-0003696r`XecogeneraXEG10277rbXncbigenercX946003rdXncbigireX gi:16129054rfuh+Nh,h-]rgrhRrih1ubh)rj}rk(hXb3957rlhhh}rmh}rn(XuniprotroXP23908rpXasaprqX ABE-0012955rrXecogenersXEG11286rtXncbigeneruX948456rvXncbigirwX gi:16131795rxuh+Nh,h-]ryrzRr{h1ubh)r|}r}(hXb3200r~hhh}rh}r(XuniprotrXP0ADV1rXasaprX ABE-0010510rXecogenerXEG12618rXncbigenerX947920rXncbigirX gi:16131090ruh+Nh,h-]rrRrh1ubh)r}r(hXb4261rhhh}rh}r(XuniprotrXP0AF98rXasaprX ABE-0013958rXecogenerXEG12535rXncbigenerX948795rXncbigirX gi:16132083ruh+Nh,h-]rrRrh1ubh)r}r(hXb3201rhhh}rh}r(XuniprotrXP0A9V1rXasaprX ABE-0010512rXecogenerXEG11680rXncbigenerX947725rXncbigirX gi:16131091ruh+Nh,h-]rrRrh1ubh)r}r(hXb4262rhhh}rh}r(XuniprotrXP0ADC6rXasaprX ABE-0013960rXecogenerXEG12536rXncbigenerX945324rXncbigirX gi:90111715ruh+Nh,h-]rrRrh1ubh)r}r(hXb3199rhhh}rh}r(XuniprotrXP0ADV9rXasaprX ABE-0010508rXecogenerXEG12806rXncbigenerX947722rXncbigirX gi:16131089ruh+Nh,h-]rrRrh1ubh)r}r(hXb1519rhhh}rh}r(XuniprotrXP76145rXasaprX ABE-0005070rXecogenerXEG13812rXncbigenerX946074rXncbigirX gi:16129478ruh+Nh,h-]rrRrh1ubh)r}r(hXb1276rhhh}rh}r(XuniprotrXP25516rXasaprX ABE-0004283rXecogenerXEG11325rXncbigenerX946724rXncbigirX gi:16129237ruh+Nh,h-]rrRrh1ubh)r}r(hXb0118rhhh}rh}r(XuniprotrXP36683rXasaprX ABE-0000411rXecogenerXEG12316rXncbigenerX944864rXncbigirX gi:16128111ruh+Nh,h-]r r Rr h1ubh)r }r (hXb1748rhhh}rh}r(XuniprotrXP77581rXasaprX ABE-0005824rXecogenerXEG13999rXncbigenerX946255rXncbigirX gi:16129702ruh+Nh,h-]rrRrh1ubh)r}r(hXb3359r hhh}r!h}r"(Xuniprotr#XP18335r$Xasapr%X ABE-0010976r&Xecogener'XEG10066r(Xncbigener)X947864r*Xncbigir+X gi:16131238r,uh+Nh,h-]r-r.Rr/h1ubh)r0}r1(hXb1090r2hhh}r3h}r4(Xuniprotr5XP27247r6Xasapr7X ABE-0003692r8Xecogener9XEG11437r:Xncbigener;X946165r<Xncbigir=X gi:90111212r>uh+Nh,h-]r?r@RrAh1ubh)rB}rC(hXb3475rDhhh}rEh}rF(XuniprotrGXP37623rHXasaprIX ABE-0011351rJXecogenerKXEG12221rLXncbigenerMX947979rNXncbigirOX gi:16131347rPuh+Nh,h-]rQrRRrSh1ubh)rT}rU(hXb2563rVhhh}rWh}rX(XuniprotrYXP24224rZXasapr[X ABE-0008433r\Xecogener]XEG10247r^Xncbigener_X947037r`XncbigiraX gi:16130488rbuh+Nh,h-]rcrdRreh1ubh)rf}rg(hXb2578rhhhh}rih}rj(XuniprotrkXP38101rlXasaprmX ABE-0008487rnXecogeneroXEG12445rpXncbigenerqX947065rrXncbigirsX gi:16130503rtuh+Nh,h-]rurvRrwh1ubh)rx}ry(hXb1533rzhhh}r{h}r|(Xuniprotr}XP31125r~XasaprX ABE-0005116rXecogenerXEG11639rXncbigenerX946081rXncbigirX gi:90111291ruh+Nh,h-]rrRrh1ubh)r}r(hXb4067rhhh}rh}r(XuniprotrXP32705rXasaprX ABE-0013328rXecogenerXEG11942rXncbigenerX948575rXncbigirX gi:16131893ruh+Nh,h-]rrRrh1ubh)r}r(hXb1623rhhh}rh}r(XuniprotrXP22333rXasaprX ABE-0005435rXecogenerXEG10030rXncbigenerX945851rXncbigirX gi:16129581ruh+Nh,h-]rrRrh1ubh)r}r(hXb1096rhhh}rh}r(XuniprotrXP28305rXasaprX ABE-0003709rXecogenerXEG11493rXncbigenerX946647rXncbigirX gi:16129059ruh+Nh,h-]rrRrh1ubh)r}r(hXb1812rhhh}rh}r(XuniprotrXP05041rXasaprX ABE-0006031rXecogenerXEG10683rXncbigenerX946337rXncbigirX gi:16129766ruh+Nh,h-]rrRrh1ubh)r}r(hXb3360rhhh}rh}r(XuniprotrXP00903rXasaprX ABE-0010983rXecogenerXEG10682rXncbigenerX947873rXncbigirX gi:16131239ruh+Nh,h-]rrRrh1ubh)r}r(hXb3665rhhh}rh}r(XuniprotrXP31441rXasaprX ABE-0011979rXecogenerXEG11692rXncbigenerX948210rXncbigirX gi:16131535ruh+Nh,h-]rrRrh1ubh)r}r(hXb3654rhhh}rh}r(XuniprotrXP0AGM9rXasaprX ABE-0011946rXecogenerXEG11194rXncbigenerX948172rXncbigirX gi:16131525ruh+Nh,h-]rrRrh1ubh)r}r (hXb3714r hhh}r h}r (Xuniprotr XP31466rXasaprX ABE-0012148rXecogenerXEG11724rXncbigenerX948224rXncbigirX gi:16131582ruh+Nh,h-]rrRrh1ubh)r}r(hXb0474rhhh}rh}r(XuniprotrXP69441r Xasapr!X ABE-0001645r"Xecogener#XEG10032r$Xncbigener%X945097r&Xncbigir'X gi:16128458r(uh+Nh,h-]r)r*Rr+h1ubh)r,}r-(hXb0120r.hhh}r/h}r0(Xuniprotr1XP0A7F6r2Xasapr3X ABE-0000416r4Xecogener5XEG10962r6Xncbigener7X947719r8Xncbigir9X gi:16128113r:uh+Nh,h-]r;r<Rr=h1ubh)r>}r?(hXb3806r@hhh}rAh}rB(XuniprotrCXP00936rDXasaprEX ABE-0012432rFXecogenerGXEG10170rHXncbigenerIX947755rJXncbigirKX gi:16131658rLuh+Nh,h-]rMrNRrOh1ubh)rP}rQ(hXb0030rRhhh}rSh}rT(XuniprotrUXP22564rVXasaprWX ABE-0000109rXXecogenerYXEG11082rZXncbigener[X944796r\Xncbigir]X gi:16128024r^uh+Nh,h-]r_r`Rrah1ubh)rb}rc(hXb2393rdhhh}reh}rf(XuniprotrgXP0AFF2rhXasapriX ABE-0007893rjXecogenerkXEG11971rlXncbigenermX946895rnXncbigiroX gi:16130325rpuh+Nh,h-]rqrrRrsh1ubh)rt}ru(hXb2964rvhhh}rwh}rx(XuniprotryXP0AFF4rzXasapr{X ABE-0009728r|Xecogener}XEG10664r~XncbigenerX946282rXncbigirX gi:90111521ruh+Nh,h-]rrRrh1ubh)r}r(hXb2406rhhh}rh}r(XuniprotrXP45562rXasaprX ABE-0007925rXecogenerXEG13159rXncbigenerX946868rXncbigirX gi:16130332ruh+Nh,h-]rrRrh1ubh)r}r(hXb0411rhhh}rh}r(XuniprotrXP0A927rXasaprX ABE-0001425rXecogenerXEG11035rXncbigenerX946242rXncbigirX gi:16128396ruh+Nh,h-]rrRrh1ubh)r}r(hXb1992rhhh}rh}r(XuniprotrXP36561rXasaprX ABE-0006613rXecogenerXEG12150rXncbigenerX946520rXncbigirX gi:16129933ruh+Nh,h-]rrRrh1ubh)r}r(hXb1709rhhh}rh}r(XuniprotrXP06611rXasaprX ABE-0005707rXecogenerXEG10128rXncbigenerX945751rXncbigirX gi:16129665ruh+Nh,h-]rrRrh1ubh)r}r(hXb0158rhhh}rh}r(XuniprotrXP37028rXasaprX ABE-0000542rXecogenerXEG12334rXncbigenerX947574rXncbigirX gi:16128151ruh+Nh,h-]rrRrh1ubh)r}r(hXb1711rhhh}rh}r(XuniprotrXP06609rXasaprX ABE-0005712rXecogenerXEG10127rXncbigenerX945877rXncbigirX gi:16129667ruh+Nh,h-]rrRrh1ubh)r}r(hXb3966rhhh}rh}r(XuniprotrXP06129rXasaprX ABE-0012987rXecogenerXEG10126rXncbigenerX948468rXncbigirX gi:16131804r uh+Nh,h-]r r Rr h1ubh)r }r (hXb3005r hhh}r h}r (Xuniprotr XP0ABV2r Xasapr X ABE-0009866r Xecogener XEG10272r Xncbigener X946345r Xncbigir X gi:16130903r uh+Nh,h-]r r Rr h1ubh)r }r (hXb3006r hhh}r h}r (Xuniprotr XP0ABU7r Xasapr X ABE-0009869r Xecogener XEG10271r Xncbigener! X945420r" Xncbigir# X gi:16130904r$ uh+Nh,h-]r% r& Rr' h1ubh)r( }r) (hXb1252r* hhh}r+ h}r, (Xuniprotr- XP02929r. Xasapr/ X ABE-0004196r0 Xecogener1 XEG11012r2 Xncbigener3 X945843r4 Xncbigir5 X gi:16129213r6 uh+Nh,h-]r7 r8 Rr9 h1ubh)r: }r; (hXb3397r< hhh}r= h}r> (Xuniprotr? XP45799r@ XasaprA X ABE-0011086rB XecogenerC XEG12926rD XncbigenerE X947906rF XncbigirG X gi:16131274rH uh+Nh,h-]rI rJ RrK h1ubh)rL }rM (hXb3034rN hhh}rO h}rP (XuniprotrQ XQ93K97rR XasaprS X ABE-0009961rT XecogenerU XEG12184rV XncbigenerW X947519rX XncbigirY X gi:16130930rZ uh+Nh,h-]r[ r\ Rr] h1ubh)r^ }r_ (hXb0469r` hhh}ra h}rb (Xuniprotrc XP69503rd Xasapre X ABE-0001628rf Xecogenerg XEG10051rh Xncbigeneri X945113rj Xncbigirk X gi:16128453rl uh+Nh,h-]rm rn Rro h1ubh)rp }rq (hXb2750rr hhh}rs h}rt (Xuniprotru XP0A6J1rv Xasaprw X ABE-0009023rx Xecogenery XEG10185rz Xncbigener{ X947221r| Xncbigir} X gi:16130657r~ uh+Nh,h-]r r Rr h1ubh)r }r (hXb1131r hhh}r h}r (Xuniprotr XP0AB89r Xasapr X ABE-0003810r Xecogener XEG11314r Xncbigener X945695r Xncbigir X gi:16129094r uh+Nh,h-]r r Rr h1ubh)r }r (hXb4177r hhh}r h}r (Xuniprotr XP0A7D4r Xasapr X ABE-0013672r Xecogener XEG10790r Xncbigener X948695r Xncbigir X gi:16131999r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0677r hhh}r h}r (Xuniprotr XP0AF18r Xasapr X ABE-0002301r Xecogener XEG10632r Xncbigener X945289r Xncbigir X gi:16128653r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0110r hhh}r h}r (Xuniprotr XP13016r Xasapr X ABE-0000380r Xecogener XEG10041r Xncbigener X948877r Xncbigir X gi:16128103r uh+Nh,h-]r r Rr h1ubh)r }r (hXb2817r hhh}r h}r (Xuniprotr XP63883r Xasapr X ABE-0009238r Xecogener XEG13086r Xncbigener X947293r Xncbigir X gi:90111492r uh+Nh,h-]r r Rr h1ubh)r }r (hXb2435r hhh}r h}r (Xuniprotr XP36548r Xasapr X ABE-0008032r Xecogener XEG11823r Xncbigener X946916r Xncbigir X gi:16130360r uh+Nh,h-]r r Rr h1ubh)r }r (hXb4169r hhh}r h}r (Xuniprotr XP26365r Xasapr X ABE-0013653r Xecogener XEG11363r Xncbigener X948683r Xncbigir X gi:16131991r uh+Nh,h-]r r Rr h1ubh)r }r (hXb1107r hhh}r h}r (Xuniprotr XP75949r Xasapr X ABE-0003737r Xecogener XEG13433r Xncbigener X945671r Xncbigir X gi:16129070r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0433r hhh}r h}r (Xuniprotr XP0AE16r Xasapr X ABE-0001501r Xecogener XEG12183r Xncbigener X946438r Xncbigir X gi:16128418r uh+Nh,h-]r! r" Rr# h1ubh)r$ }r% (hXb3619r& hhh}r' h}r( (Xuniprotr) XP67910r* Xasapr+ X ABE-0011843r, Xecogener- XEG10838r. Xncbigener/ X948134r0 Xncbigir1 X gi:16131490r2 uh+Nh,h-]r3 r4 Rr5 h1ubh)r6 }r7 (hXb2937r8 hhh}r9 h}r: (Xuniprotr; XP60651r< Xasapr= X ABE-0009632r> Xecogener? XEG10960r@ XncbigenerA X947715rB XncbigirC X gi:16130838rD uh+Nh,h-]rE rF RrG h1ubh)rH }rI (hXb3018rJ hhh}rK h}rL (XuniprotrM XP26647rN XasaprO X ABE-0009910rP XecogenerQ XEG11377rR XncbigenerS X947496rT XncbigirU X gi:16130914rV uh+Nh,h-]rW rX RrY h1ubh)rZ }r[ (hXb3958r\ hhh}r] h}r^ (Xuniprotr_ XP11446r` Xasapra X ABE-0012964rb Xecogenerc XEG10065rd Xncbigenere X948455rf Xncbigirg X gi:16131796rh uh+Nh,h-]ri rj Rrk h1ubh)rl }rm (hXb0575rn hhh}ro h}rp (Xuniprotrq XP38054rr Xasaprs X ABE-0001971rt Xecogeneru XEG12367rv Xncbigenerw X945191rx Xncbigiry X gi:16128558rz uh+Nh,h-]r{ r| Rr} h1ubh)r~ }r (hXb0572r hhh}r h}r (Xuniprotr XP77211r Xasapr X ABE-0001958r Xecogener XEG14233r Xncbigener X946288r Xncbigir X gi:16128555r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0573r hhh}r h}r (Xuniprotr XP77214r Xasapr X ABE-0001967r Xecogener XEG14234r Xncbigener X945188r Xncbigir X gi:16128556r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0574r hhh}r h}r (Xuniprotr XP77239r Xasapr X ABE-0001969r Xecogener XEG14235r Xncbigener X945189r Xncbigir X gi:16128557r uh+Nh,h-]r r Rr h1ubh)r }r (hXb4006r hhh}r h}r (Xuniprotr XP15639r Xasapr X ABE-0013097r Xecogener XEG10795r Xncbigener X948503r Xncbigir X gi:16131836r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0522r hhh}r h}r (Xuniprotr XP09029r Xasapr X ABE-0001794r Xecogener XEG10796r Xncbigener X945153r Xncbigir X gi:16128506r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0523r hhh}r h}r (Xuniprotr XP0AG18r Xasapr X ABE-0001796r Xecogener XEG10793r Xncbigener X949031r Xncbigir X gi:16128507r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0727r hhh}r h}r (Xuniprotr XP0AFG6r Xasapr X ABE-0002480r Xecogener XEG10980r Xncbigener X945307r Xncbigir X gi:16128702r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0116r hhh}r h}r (Xuniprotr XP0A9P0r Xasapr X ABE-0000404r Xecogener XEG10543r Xncbigener X944854r Xncbigir X gi:16128109r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0726r hhh}r h}r (Xuniprotr XP0AFG3r Xasapr X ABE-0002478r Xecogener XEG10979r Xncbigener X945303r Xncbigir X gi:16128701r uh+Nh,h-]r r Rr h1ubh)r }r! (hXb2587r" hhh}r# h}r$ (Xuniprotr% XP0AEX3r& Xasapr' X ABE-0008515r( Xecogener) XEG10522r* Xncbigener+ X947069r, Xncbigir- X gi:16130512r. uh+Nh,h-]r/ r0 Rr1 h1ubh)r2 }r3 (hXb1488r4 hhh}r5 h}r6 (Xuniprotr7 XP77790r8 Xasapr9 X ABE-0004960r: Xecogener; XEG13791r< Xncbigener= X945532r> Xncbigir? X gi:16129447r@ uh+Nh,h-]rA rB RrC h1ubh)rD }rE (hXb3542rF hhh}rG h}rH (XuniprotrI XP0AEG1rJ XasaprK X ABE-0011570rL XecogenerM XEG12626rN XncbigenerO X948064rP XncbigirQ X gi:16131414rR uh+Nh,h-]rS rT RrU h1ubh)rV }rW (hXb1486rX hhh}rY h}rZ (Xuniprotr[ XP77308r\ Xasapr] X ABE-0004955r^ Xecogener_ XEG13789r` Xncbigenera X946044rb Xncbigirc X gi:16129445rd uh+Nh,h-]re rf Rrg h1ubh)rh }ri (hXb1485rj hhh}rk h}rl (Xuniprotrm XP77463rn Xasapro X ABE-0004953rp Xecogenerq XEG13788rr Xncbigeners X946028rt Xncbigiru X gi:16129444rv uh+Nh,h-]rw rx Rry h1ubh)rz }r{ (hXb3543r| hhh}r} h}r~ (Xuniprotr XP0AEF8r Xasapr X ABE-0011572r Xecogener XEG12625r Xncbigener X948063r Xncbigir X gi:16131415r uh+Nh,h-]r r Rr h1ubh)r }r (hXb3541r hhh}r h}r (Xuniprotr XP0AAG0r Xasapr X ABE-0011566r Xecogener XEG12627r Xncbigener X948065r Xncbigir X gi:16131413r uh+Nh,h-]r r Rr h1ubh)r }r (hXb1484r hhh}r h}r (Xuniprotr XP77268r Xasapr X ABE-0004949r Xecogener XEG13787r Xncbigener X946002r Xncbigir X gi:16129443r uh+Nh,h-]r r Rr h1ubh)r }r (hXb3540r hhh}r h}r (Xuniprotr XP37313r Xasapr X ABE-0011564r Xecogener XEG12628r Xncbigener X948056r Xncbigir X gi:16131412r uh+Nh,h-]r r Rr h1ubh)r }r (hXb3544r hhh}r h}r (Xuniprotr XP23847r Xasapr X ABE-0011576r Xecogener XEG10248r Xncbigener X948062r Xncbigir X gi:16131416r uh+Nh,h-]r r Rr h1ubh)r }r (hXb1483r hhh}r h}r (Xuniprotr XP77622r Xasapr X ABE-0004947r Xecogener XEG13786r Xncbigener X946020r Xncbigir X gi:16129442r uh+Nh,h-]r r Rr h1ubh)r }r (hXb1487r hhh}r h}r (Xuniprotr XP76128r Xasapr X ABE-0004958r Xecogener XEG13790r Xncbigener X946052r Xncbigir X gi:16129446r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0092r hhh}r h}r (Xuniprotr XP07862r Xasapr X ABE-0000326r Xecogener XEG10214r Xncbigener X946324r Xncbigir X gi:16128085r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0381r hhh}r h}r (Xuniprotr XP0A6J8r Xasapr X ABE-0001309r Xecogener XEG10213r Xncbigener X945313r Xncbigir X gi:16128366r uh+Nh,h-]r r Rr h1ubh)r }r (hXb1325r hhh}r h}r (Xuniprotr! XP51981r" Xasapr# X ABE-0004448r$ Xecogener% XEG13228r& Xncbigener' X946013r( Xncbigir) X gi:90111249r* uh+Nh,h-]r+ r, Rr- h1ubh)r. }r/ (hXb1190r0 hhh}r1 h}r2 (Xuniprotr3 XP29012r4 Xasapr5 X ABE-0003997r6 Xecogener7 XEG11408r8 Xncbigener9 X945754r: Xncbigir; X gi:16129153r< uh+Nh,h-]r= r> Rr? h1ubh)r@ }rA (hXb4053rB hhh}rC h}rD (XuniprotrE XP0A6B4rF XasaprG X ABE-0013272rH XecogenerI XEG10001rJ XncbigenerK X948564rL XncbigirM X gi:16131879rN uh+Nh,h-]rO rP RrQ h1ubh)rR }rS (hXb2551rT hhh}rU h}rV (XuniprotrW XP0A825rX XasaprY X ABE-0008389rZ Xecogener[ XEG10408r\ Xncbigener] X947022r^ Xncbigir_ X gi:16130476r` uh+Nh,h-]ra rb Rrc h1ubh)rd }re (hXb0870rf hhh}rg h}rh (Xuniprotri XP75823rj Xasaprk X ABE-0002954rl Xecogenerm XEG13690rn Xncbigenero X944955rp Xncbigirq X gi:16128838rr uh+Nh,h-]rs rt Rru h1ubh)rv }rw (hXb2379rx hhh}ry h}rz (Xuniprotr{ XP77434r| Xasapr} X ABE-0007850r~ Xecogener XEG14198r Xncbigener X946850r Xncbigir X gi:16130311r uh+Nh,h-]r r Rr h1ubh)r }r (hXb2290r hhh}r h}r (Xuniprotr XP0A959r Xasapr X ABE-0007560r Xecogener XEG14101r Xncbigener X946772r Xncbigir X gi:16130225r uh+Nh,h-]r r Rr h1ubh)r }r (hXb2697r hhh}r h}r (Xuniprotr XP00957r Xasapr X ABE-0008869r Xecogener XEG10034r Xncbigener X947175r Xncbigir X gi:16130604r uh+Nh,h-]r r Rr h1ubh)r }r (hXb3460r hhh}r h}r (Xuniprotr XP0AD96r Xasapr X ABE-0011299r Xecogener XEG10539r Xncbigener X947971r Xncbigir X gi:49176358r uh+Nh,h-]r r Rr h1ubh)r }r (hXb3456r hhh}r h}r (Xuniprotr XP22729r Xasapr X ABE-0011287r Xecogener XEG10541r Xncbigener X947966r Xncbigir X gi:16131328r uh+Nh,h-]r r Rr h1ubh)r }r (hXb3457r hhh}r h}r (Xuniprotr XP0AEX7r Xasapr X ABE-0011290r Xecogener XEG10538r Xncbigener X947965r Xncbigir X gi:16131329r uh+Nh,h-]r r Rr h1ubh)r }r (hXb3455r hhh}r h}r (Xuniprotr XP0A9S7r Xasapr X ABE-0011284r Xecogener XEG10537r Xncbigener X947967r Xncbigir X gi:16131327r uh+Nh,h-]r r Rr h1ubh)r }r (hXb3454r hhh}r h}r (Xuniprotr XP22731r Xasapr X ABE-0011282r Xecogener XEG10536r Xncbigener X947961r Xncbigir X gi:90111594r uh+Nh,h-]r r Rr h1ubh)r }r (hXb4208r hhh}r h}r (Xuniprotr XP0AAE0r Xasapr X ABE-0013764r Xecogener XEG12504r Xncbigener X948725r Xncbigir X gi:16132030r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0007r hhh}r h}r (Xuniprotr XP30143r Xasapr X ABE-0000020r Xecogener! XEG11555r" Xncbigener# X944745r$ Xncbigir% X gi:16128001r& uh+Nh,h-]r' r( Rr) h1ubh)r* }r+ (hXb0356r, hhh}r- h}r. (Xuniprotr/ XP25437r0 Xasapr1 X ABE-0001221r2 Xecogener3 XEG50010r4 Xncbigener5 X944988r6 Xncbigir7 X gi:16128341r8 uh+Nh,h-]r9 r: Rr; h1ubh)r< }r= (hXb1478r> hhh}r? h}r@ (XuniprotrA XP39451rB XasaprC X ABE-0004928rD XecogenerE XEG12622rF XncbigenerG X946036rH XncbigirI X gi:90111280rJ uh+Nh,h-]rK rL RrM h1ubh)rN }rO (hXb1385rP hhh}rQ h}rR (XuniprotrS XP80668rT XasaprU X ABE-0004637rV XecogenerW XEG13179rX XncbigenerY X945933rZ Xncbigir[ X gi:90111264r\ uh+Nh,h-]r] r^ Rr_ h1ubh)r` }ra (hXb1300rb hhh}rc h}rd (Xuniprotre XP23883rf Xasaprg X ABE-0004376rh Xecogeneri XEG10036rj Xncbigenerk X947003rl Xncbigirm X gi:16129261rn uh+Nh,h-]ro rp Rrq h1ubh)rr }rs (hXb3588rt hhh}ru h}rv (Xuniprotrw XP37685rx Xasapry X ABE-0011718rz Xecogener{ XEG12292r| Xncbigener} X948104r~ Xncbigir X gi:90111619r uh+Nh,h-]r r Rr h1ubh)r }r (hXb4084r hhh}r h}r (Xuniprotr XP32718r Xasapr X ABE-0013380r Xecogener XEG11956r Xncbigener X948596r Xncbigir X gi:16131910r uh+Nh,h-]r r Rr h1ubh)r }r (hXb4090r hhh}r h}r (Xuniprotr XP37351r Xasapr X ABE-0013405r Xecogener XEG11827r Xncbigener X948602r Xncbigir X gi:16131916r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0516r hhh}r h}r (Xuniprotr XP77425r Xasapr X ABE-0001777r Xecogener XEG13623r Xncbigener X945150r Xncbigir X gi:16128500r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0512r hhh}r h}r (Xuniprotr XP77671r Xasapr X ABE-0001768r Xecogener XEG13619r Xncbigener X945134r Xncbigir X gi:16128496r uh+Nh,h-]r r Rr h1ubh)r }r (hXb0511r hhh}r h}r (Xuniprotr XP75712r Xasapr X ABE-0001764r Xecogener XEG13618r Xncbigener X945138r Xncbigir X gi:90111144r uh+Nh,h-]r r Rr h1ubh)r }r (hXb4085r hhh}r h}r (Xuniprotr XP32719r Xasapr X ABE-0013382r Xecogener XEG11957r Xncbigener X948595r Xncbigir X gi:16131911r uh+Nh,h-]r r Rr h1ubh)r }r (hXb4087r hhh}r h}r (Xuniprotr XP32721r Xasapr X ABE-0013394r Xecogener XEG11959r Xncbigener X948593r Xncbigir X gi:16131913r uh+Nh,h-]r rRrh1ubh)r}r(hXb4086rhhh}rh}r(XuniprotrXP32720rXasapr X ABE-0013392r Xecogener XEG11958r Xncbigener X948594rXncbigirX gi:16131912ruh+Nh,h-]rrRrh1ubh)r}r(hXb4088rhhh}rh}r(XuniprotrXP39265rXasaprX ABE-0013398rXecogenerXEG12458rXncbigenerX948604r Xncbigir!X gi:16131914r"uh+Nh,h-]r#r$Rr%h1ubh)r&}r'(hXb0657r(hhh}r)h}r*(Xuniprotr+XP23930r,Xasapr-X ABE-0002250r.Xecogener/XEG10168r0Xncbigener1X946201r2Xncbigir3X gi:16128640r4uh+Nh,h-]r5r6Rr7h1ubh)r8}r9(hXb1677r:hhh}r;h}r<(Xuniprotr=XP69776r>Xasapr?X ABE-0005603r@XecogenerAXEG10544rBXncbigenerCX946175rDXncbigirEX gi:16129633rFuh+Nh,h-]rGrHRrIh1ubh)rJ}rK(hXb1781rLhhh}rMh}rN(XuniprotrOXP76234rPXasaprQX ABE-0005925rRXecogenerSXEG13491rTXncbigenerUX946302rVXncbigirWX gi:16129735rXuh+Nh,h-]rYrZRr[h1ubh)r\}r](hXb3001r^hhh}r_h}r`(XuniprotraXQ46851rbXasaprcX ABE-0009848rdXecogenereXEG13010rfXncbigenergX947480rhXncbigiriX gi:16130899rjuh+Nh,h-]rkrlRrmh1ubh)rn}ro(hXb0207rphhh}rqh}rr(XuniprotrsXP30863rtXasapruX ABE-0000692rvXecogenerwXEG11648rxXncbigeneryX944901rzXncbigir{X gi:16128194r|uh+Nh,h-]r}r~Rrh1ubh)r}r(hXb3012rhhh}rh}r(XuniprotrXQ46857rXasaprX ABE-0009892rXecogenerXEG13015rXncbigenerX947495rXncbigirX gi:90111527ruh+Nh,h-]rrRrh1ubh)r}r(hXb3945rhhh}rh}r(XuniprotrXP0A9S5rXasaprX ABE-0012912rXecogenerXEG11904rXncbigenerX948440rXncbigirX gi:90111668ruh+Nh,h-]rrRrh1ubh)r}r(hXb3091rhhh}rh}r(XuniprotrXP42604rXasaprX ABE-0010164rXecogenerXEG12734rXncbigenerX947603rXncbigirX gi:16130986ruh+Nh,h-]rrRrh1ubh)r}r(hXb3416rhhh}rh}r(XuniprotrXP15977rXasaprX ABE-0011151rXecogenerXEG10561rXncbigenerX947923rXncbigirX gi:16131292ruh+Nh,h-]rrRrh1ubh)r}r(hXb3223rhhh}rh}r(XuniprotrXP0A761rXasaprX ABE-0010575rXecogenerXEG12816rXncbigenerX947745rXncbigirX gi:16131113ruh+Nh,h-]rrRrh1ubh)r}r(hXb3222rhhh}rh}r(XuniprotrXP45425rXasaprX ABE-0010571rXecogenerXEG12815rXncbigenerX947757rXncbigirX gi:90111558ruh+Nh,h-]rrRrh1ubh)r}r(hXb0774rhhh}rh}r(XuniprotrXP12995rXasaprX ABE-0002635rXecogenerXEG10117rXncbigenerX945376rXncbigirX gi:16128742ruh+Nh,h-]rrRrh1ubh)r}r(hXb3833rhhh}rh}r(XuniprotrXP0A887rXasaprX ABE-0012534rXecogenerXEG11473rXncbigener X948926r Xncbigir X gi:49176426r uh+Nh,h-]r rRrh1ubh)r}r(hXb3994rhhh}rh}r(XuniprotrXP30136rXasaprX ABE-0013057rXecogenerXEG11585rXncbigenerX948492rXncbigirX gi:16131824ruh+Nh,h-]rr Rr!h1ubh)r"}r#(hXb1982r$hhh}r%h}r&(Xuniprotr'XP0AE12r(Xasapr)X ABE-0006579r*Xecogener+XEG10039r,Xncbigener-X946508r.Xncbigir/X gi:16129926r0uh+Nh,h-]r1r2Rr3h1ubh)r4}r5(hXb0932r6hhh}r7h}r8(Xuniprotr9XP04825r:Xasapr;X ABE-0003171r<Xecogener=XEG10696r>Xncbigener?X947253r@XncbigirAX gi:16128899rBuh+Nh,h-]rCrDRrEh1ubh)rF}rG(hXb0237rHhhh}rIh}rJ(XuniprotrKXP15288rLXasaprMX ABE-0000809rNXecogenerOXEG10695rPXncbigenerQX945013rRXncbigirSX gi:16128223rTuh+Nh,h-]rUrVRrWh1ubh)rX}rY(hXb4260rZhhh}r[h}r\(Xuniprotr]XP68767r^Xasapr_X ABE-0013953r`XecogeneraXEG10694rbXncbigenercX948791rdXncbigireX gi:16132082rfuh+Nh,h-]rgrhRrih1ubh)rj}rk(hXb2523rlhhh}rmh}rn(XuniprotroXP37095rpXasaprqX ABE-0008305rrXecogenersXEG12310rtXncbigeneruX948766rvXncbigirwX gi:90111453rxuh+Nh,h-]ryrzRr{h1ubh)r|}r}(hXb1640r~hhh}rh}r(XuniprotrXP77570rXasaprX ABE-0005488rXecogenerXEG13942rXncbigenerX946810rXncbigirX gi:16129598ruh+Nh,h-]rrRrh1ubh)r}r(hXb1263rhhh}rh}r(XuniprotrXP00904rXasaprX ABE-0004241rXecogenerXEG11027rXncbigenerX945109rXncbigirX gi:16129224ruh+Nh,h-]rrRrh1ubh)r}r(hXb1264rhhh}rh}r(XuniprotrXP00895rXasaprX ABE-0004244rXecogenerXEG11028rXncbigenerX945846rXncbigirX gi:16129225ruh+Nh,h-]rrRrh1ubh)r}r(hXb0776rhhh}rh}r(XuniprotrXP12998rXasaprX ABE-0002646rXecogenerXEG10121rXncbigenerX945384rXncbigirX gi:16128744ruh+Nh,h-]rrRrh1ubh)r}r(hXb0049rhhh}rh}r(XuniprotrXP05637rXasaprX ABE-0000167rXecogenerXEG10048rXncbigenerX944770rXncbigirX gi:16128043ruh+Nh,h-]rrRrh1ubh)r}r(hXb4129rhhh}rh}r(XuniprotrXP0A8N5rXasaprX ABE-0013520rXecogenerXEG10553rXncbigenerX948645rXncbigirX gi:16131955ruh+Nh,h-]rrRrh1ubh)r}r(hXb0121rhhh}rh}r(XuniprotrXP09158rXasaprX ABE-0000418rXecogenerXEG10963rXncbigenerX947726rXncbigirX gi:16128114ruh+Nh,h-]rrRrh1ubh)r}r(hXb3059rhhh}rh}r(XuniprotrXP60782rXasaprX ABE-0010042rXecogenerXEG11674rXncbigenerX947561rXncbigirX gi:16130955ruh+Nh,h-]r r Rr h1ubh)r }r (hXb0968rhhh}rh}r(XuniprotrXP0AB65rXasaprX ABE-0003276rXecogenerXEG13726rXncbigenerX945304rXncbigirX gi:16128935ruh+Nh,h-]rrRrh1ubh)r}r(hXb0414r hhh}r!h}r"(Xuniprotr#XP25539r$Xasapr%X ABE-0001441r&Xecogener'XEG11321r(Xncbigener)X945620r*Xncbigir+X gi:16128399r,uh+Nh,h-]r-r.Rr/h1ubh)r0}r1(hXb0062r2hhh}r3h}r4(Xuniprotr5XP08202r6Xasapr7X ABE-0000221r8Xecogener9XEG10052r:Xncbigener;X947511r<Xncbigir=X gi:16128056r>uh+Nh,h-]r?r@RrAh1ubh)rB}rC(hXb0153rDhhh}rEh}rF(XuniprotrGXP06972rHXasaprIX ABE-0000526rJXecogenerKXEG10303rLXncbigenerMX946890rNXncbigirOX gi:16128146rPuh+Nh,h-]rQrRRrSh1ubh)rT}rU(hXb0151rVhhh}rWh}rX(XuniprotrYXP07821rZXasapr[X ABE-0000522r\Xecogener]XEG10304r^Xncbigener_X945250r`XncbigiraX gi:16128144rbuh+Nh,h-]rcrdRreh1ubh)rf}rg(hXb0152rhhhh}rih}rj(XuniprotrkXP07822rlXasaprmX ABE-0000524rnXecogeneroXEG10305rpXncbigenerqX947510rrXncbigirsX gi:16128145rtuh+Nh,h-]rurvRrwh1ubh)rx}ry(hXb2715rzhhh}r{h}r|(Xuniprotr}XP24241r~XasaprX ABE-0008926rXecogenerXEG10086rXncbigenerX947154rXncbigirX gi:49176263ruh+Nh,h-]rrRrh1ubh)r}r(hXb3722rhhh}rh}r(XuniprotrXP08722rXasaprX ABE-0012171rXecogenerXEG10115rXncbigenerX948236rXncbigirX gi:16131590ruh+Nh,h-]rrRrh1ubh)r}r(hXb1900rhhh}rh}r(XuniprotrXP0AAF3rXasaprX ABE-0006328rXecogenerXEG10058rXncbigenerX946408rXncbigirX gi:16129850ruh+Nh,h-]rrRrh1ubh)r}r(hXb4460rhhh}rh}r(XuniprotrXP0AE26rXasaprX ABE-0174093rXecogenerXEG10059rXncbigenerX948923rXncbigirX gi:145698280ruh+Nh,h-]rrRrh1ubh)r}r(hXb1901rhhh}rh}r(XuniprotrXP02924rXasaprX ABE-0006330rXecogenerXEG10057rXncbigenerX946409rXncbigirX gi:16129851ruh+Nh,h-]rrRrh1ubh)r}r(hXb2841rhhh}rh}r(XuniprotrXP0AE24rXasaprX ABE-0009319rXecogenerXEG10056rXncbigenerX947341rXncbigirX gi:16130745ruh+Nh,h-]rrRrh1ubh)r}r(hXb1528rhhh}rh}r(XuniprotrXP31122rXasaprX ABE-0005103rXecogenerXEG11636rXncbigenerX947218rXncbigirX gi:16129487ruh+Nh,h-]rrRrh1ubh)r}r(hXb4115rhhh}rh}r(XuniprotrXP60061rXasaprX ABE-0013473rXecogenerXEG12462rXncbigenerX948628rXncbigirX gi:16131941ruh+Nh,h-]rrRrh1ubh)r}r (hXb4117r hhh}r h}r (Xuniprotr XP28629rXasaprX ABE-0013483rXecogenerXEG11501rXncbigenerX948638rXncbigirX gi:221800782ruh+Nh,h-]rrRrh1ubh)r}r(hXb2938rhhh}rh}r(XuniprotrXP21170r Xasapr!X ABE-0009634r"Xecogener#XEG10959r$Xncbigener%X947432r&Xncbigir'X gi:16130839r(uh+Nh,h-]r)r*Rr+h1ubh)r,}r-(hXb1605r.hhh}r/h}r0(Xuniprotr1XP0AAE5r2Xasapr3X ABE-0005362r4Xecogener5XEG13930r6Xncbigener7X945159r8Xncbigir9X gi:16129563r:uh+Nh,h-]r;r<Rr=h1ubh)r>}r?(hXb3960r@hhh}rAh}rB(XuniprotrCXP11447rDXasaprEX ABE-0012969rFXecogenerGXEG11223rHXncbigenerIX948463rJXncbigirKX gi:16131798rLuh+Nh,h-]rMrNRrOh1ubh)rP}rQ(hXb3172rRhhh}rSh}rT(XuniprotrUXP0A6E4rVXasaprWX ABE-0010428rXXecogenerYXEG10068rZXncbigener[X947590r\Xncbigir]X gi:16131063r^uh+Nh,h-]r_r`Rrah1ubh)rb}rc(hXb1876rdhhh}reh}rf(XuniprotrgXP11875rhXasapriX 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gi:162135909r)uh+Nh,h-]r)r)Rr)h1ubh)r)}r)(hXb4474r*hhh}r*h}r*(Xuniprotr*XP45541r*Xasapr*X ABE-0174114r*Xecogener*XEG12910r*Xncbigener *X2847758r *Xncbigir *X gi:49176341r *uh+Nh,h-]r *r*Rr*h1ubh)r*}r*(hXb3374r*hhh}r*h}r*(Xuniprotr*XP45543r*Xasapr*X ABE-0011029r*Xecogener*XEG12912r*Xncbigener*X947886r*Xncbigir*X gi:16131252r*uh+Nh,h-]r*r *Rr!*h1ubh)r"*}r#*(hXb3370r$*hhh}r%*h}r&*(Xuniprotr'*XP45539r(*Xasapr)*X ABE-0011019r**Xecogener+*XEG12908r,*Xncbigener-*X947878r.*Xncbigir/*X gi:90111576r0*uh+Nh,h-]r1*r2*Rr3*h1ubh)r4*}r5*(hXb4321r6*hhh}r7*h}r8*(Xuniprotr9*XP0AC94r:*Xasapr;*X ABE-0014161r<*Xecogener=*XEG12563r>*Xncbigener?*X948848r@*XncbigirA*X gi:16132142rB*uh+Nh,h-]rC*rD*RrE*h1ubh)rF*}rG*(hXb2169rH*hhh}rI*h}rJ*(XuniprotrK*XP69811rL*XasaprM*X ABE-0007180rN*XecogenerO*XEG10005rP*XncbigenerQ*X946677rR*XncbigirS*X gi:16130107rT*uh+Nh,h-]rU*rV*RrW*h1ubh)rX*}rY*(hXb2167rZ*hhh}r[*h}r\*(Xuniprotr]*XP20966r^*Xasapr_*X ABE-0007172r`*Xecogenera*XEG10336rb*Xncbigenerc*X946672rd*Xncbigire*X gi:16130105rf*uh+Nh,h-]rg*rh*Rri*h1ubh)rj*}rk*(hXb1232rl*hhh}rm*h}rn*(Xuniprotro*XP37051rp*Xasaprq*X ABE-0004138rr*Xecogeners*XEG11819rt*Xncbigeneru*X945827rv*Xncbigirw*X gi:16129193rx*uh+Nh,h-]ry*rz*Rr{*h1ubh)r|*}r}*(hXb2801r~*hhh}r*h}r*(Xuniprotr*XP11551r*Xasapr*X ABE-0009187r*Xecogener*XEG10352r*Xncbigener*X947487r*Xncbigir*X gi:16130708r*uh+Nh,h-]r*r*Rr*h1ubh)r*}r*(hXb1611r*hhh}r*h}r*(Xuniprotr*XP05042r*Xasapr*X ABE-0005380r*Xecogener*XEG10358r*Xncbigener*X946147r*Xncbigir*X gi:16129569r*uh+Nh,h-]r*r*Rr*h1ubh)r*}r*(hXb1612r*hhh}r*h}r*(Xuniprotr*XP0AC33r*Xasapr*X ABE-0005392r*Xecogener*XEG10356r*Xncbigener*X946826r*Xncbigir*X gi:16129570r*uh+Nh,h-]r*r*Rr*h1ubh)r*}r*(hXb0621r*hhh}r*h}r*(Xuniprotr*XP0ABP3r*Xasapr*X ABE-0002133r*Xecogener*XEG13545r*Xncbigener*X945000r*Xncbigir*X gi:16128604r*uh+Nh,h-]r*r*Rr*h1ubh)r*}r*(hXb3730r*hhh}r*h}r*(Xuniprotr*XP0ACC7r*Xasapr*X ABE-0012201r*Xecogener*XEG11198r*Xncbigener*X948246r*Xncbigir*X gi:16131598r*uh+Nh,h-]r*r*Rr*h1ubh)r*}r*(hXb1002r*hhh}r*h}r*(Xuniprotr*XP19926r*Xasapr*X ABE-0003388r*Xecogener*XEG10033r*Xncbigener*X945773r*Xncbigir*X gi:16128968r*uh+Nh,h-]r*r*Rr*h1ubh)r*}r*(hXb3789r*hhh}r*h}r*(Xuniprotr*XP61887r*Xasapr*X ABE-0012379r*Xecogener*XEG11454r*Xncbigener*X948299r*Xncbigir*X gi:16131645r*uh+Nh,h-]r*r*Rr*h1ubh)r*}r*(hXb2039r*hhh}r*h}r*(Xuniprotr*XP37744r+Xasapr+X ABE-0006762r+Xecogener+XEG11978r+Xncbigener+X945154r+Xncbigir+X gi:16129979r+uh+Nh,h-]r +r +Rr +h1ubh)r +}r +(hXb0154r+hhh}r+h}r+(Xuniprotr+XP23893r+Xasapr+X ABE-0000529r+Xecogener+XEG10432r+Xncbigener+X946892r+Xncbigir+X gi:16128147r+uh+Nh,h-]r+r+Rr+h1ubh)r+}r+(hXb2533r +hhh}r!+h}r"+(Xuniprotr#+XP0ADG4r$+Xasapr%+X ABE-0008337r&+Xecogener'+XEG10983r(+Xncbigener)+X947285r*+Xncbigir++X gi:16130458r,+uh+Nh,h-]r-+r.+Rr/+h1ubh)r0+}r1+(hXb4055r2+hhh}r3+h}r4+(Xuniprotr5+XP0AE22r6+Xasapr7+X ABE-0013283r8+Xecogener9+XEG11934r:+Xncbigener;+X948562r<+Xncbigir=+X gi:16131881r>+uh+Nh,h-]r?+r@+RrA+h1ubh)rB+}rC+(hXb4041rD+hhh}rE+h}rF+(XuniprotrG+XP0A7A7rH+XasaprI+X ABE-0013232rJ+XecogenerK+XEG10740rL+XncbigenerM+X948541rN+XncbigirO+X gi:90111678rP+uh+Nh,h-]rQ+rR+RrS+h1ubh)rT+}rU+(hXb3451rV+hhh}rW+h}rX+(XuniprotrY+XP10906rZ+Xasapr[+X ABE-0011270r\+Xecogener]+XEG11049r^+Xncbigener_+X947959r`+Xncbigira+X gi:16131323rb+uh+Nh,h-]rc+rd+Rre+h1ubh)rf+}rg+(hXb3452rh+hhh}ri+h}rj+(Xuniprotrk+XP10905rl+Xasaprm+X ABE-0011272rn+Xecogenero+XEG11046rp+Xncbigenerq+X947957rr+Xncbigirs+X gi:16131324rt+uh+Nh,h-]ru+rv+Rrw+h1ubh)rx+}ry+(hXb3453rz+hhh}r{+h}r|+(Xuniprotr}+XP0AG80r~+Xasapr+X ABE-0011274r+Xecogener+XEG11047r+Xncbigener+X947962r+Xncbigir+X gi:16131325r+uh+Nh,h-]r+r+Rr+h1ubh)r+}r+(hXb3450r+hhh}r+h}r+(Xuniprotr+XP10907r+Xasapr+X ABE-0011266r+Xecogener+XEG11048r+Xncbigener+X947953r+Xncbigir+X gi:90111593r+uh+Nh,h-]r+r+Rr+h1ubh)r+}r+(hXb3608r+hhh}r+h}r+(Xuniprotr+XP0A6S7r+Xasapr+X ABE-0011795r+Xecogener+XEG20091r+Xncbigener+X948125r+Xncbigir+X gi:16131479r+uh+Nh,h-]r+r+Rr+h1ubh)r+}r+(hXb2242r+hhh}r+h}r+(Xuniprotr+XP13033r+Xasapr+X ABE-0007424r+Xecogener+XEG10392r+Xncbigener+X946733r+Xncbigir+X gi:16130177r+uh+Nh,h-]r+r+Rr+h1ubh)r+}r+(hXb2241r+hhh}r+h}r+(Xuniprotr+XP0A9C0r+Xasapr+X ABE-0007422r+Xecogener+XEG10391r+Xncbigener+X946713r+Xncbigir+X gi:16130176r+uh+Nh,h-]r+r+Rr+h1ubh)r+}r+(hXb2243r+hhh}r+h}r+(Xuniprotr+XP0A996r+Xasapr+X ABE-0007426r+Xecogener+XEG10393r+Xncbigener+X946735r+Xncbigir+X gi:16130178r+uh+Nh,h-]r+r+Rr+h1ubh)r+}r+(hXb3426r+hhh}r+h}r+(Xuniprotr+XP13035r+Xasapr+X ABE-0011191r+Xecogener+XEG10394r+Xncbigener+X947934r+Xncbigir+X gi:16131300r+uh+Nh,h-]r+r+Rr+h1ubh)r+}r+(hXb0243r+hhh}r+h}r+(Xuniprotr+XP07004r+Xasapr+X ABE-0000832r+Xecogener+XEG10767r,Xncbigener,X946680r,Xncbigir,X gi:16128229r,uh+Nh,h-]r,r,Rr,h1ubh)r,}r ,(hXb0678r ,hhh}r ,h}r ,(Xuniprotr ,XP0A759r,Xasapr,X ABE-0002304r,Xecogener,XEG10633r,Xncbigener,X945290r,Xncbigir,X gi:16128654r,uh+Nh,h-]r,r,Rr,h1ubh)r,}r,(hXb1852r,hhh}r,h}r,(Xuniprotr,XP0AC53r ,Xasapr!,X ABE-0006171r",Xecogener#,XEG11221r$,Xncbigener%,X946370r&,Xncbigir',X gi:16129805r(,uh+Nh,h-]r),r*,Rr+,h1ubh)r,,}r-,(hXb3128r.,hhh}r/,h}r0,(Xuniprotr1,XP39829r2,Xasapr3,X ABE-0010284r4,Xecogener5,XEG12522r6,Xncbigener7,X947641r8,Xncbigir9,X gi:16131020r:,uh+Nh,h-]r;,r<,Rr=,h1ubh)r>,}r?,(hXb4358r@,hhh}rA,h}rB,(XuniprotrC,XP39400rD,XasaprE,X ABE-0014294rF,XecogenerG,XEG12590rH,XncbigenerI,X948883rJ,XncbigirK,X gi:226524765rL,uh+Nh,h-]rM,rN,RrO,h1ubh)rP,}rQ,(hXb4478rR,hhh}rS,h}rT,(XuniprotrU,XQ6BF17rV,XasaprW,X ABE-0174106rX,XecogenerY,XEG20050rZ,Xncbigener[,X2847765r\,Xncbigir],X gi:49176390r^,uh+Nh,h-]r_,r`,Rra,h1ubh)rb,}rc,(hXb4356rd,hhh}re,h}rf,(Xuniprotrg,XP39398rh,Xasapri,X ABE-0014284rj,Xecogenerk,XEG12588rl,Xncbigenerm,X948879rn,Xncbigiro,X gi:16132177rp,uh+Nh,h-]rq,rr,Rrs,h1ubh)rt,}ru,(hXb3691rv,hhh}rw,h}rx,(Xuniprotry,XP0AA76rz,Xasapr{,X ABE-0012072r|,Xecogener},XEG11715r~,Xncbigener,X948196r,Xncbigir,X gi:90111641r,uh+Nh,h-]r,r,Rr,h1ubh)r,}r,(hXb3127r,hhh}r,h}r,(Xuniprotr,XP0AA80r,Xasapr,X ABE-0010279r,Xecogener,XEG12760r,Xncbigener,X947642r,Xncbigir,X gi:16131019r,uh+Nh,h-]r,r,Rr,h1ubh)r,}r,(hXb2789r,hhh}r,h}r,(Xuniprotr,XQ46916r,Xasapr,X ABE-0009139r,Xecogener,XEG13169r,Xncbigener,X947265r,Xncbigir,X gi:16130696r,uh+Nh,h-]r,r,Rr,h1ubh)r,}r,(hXb0757r,hhh}r,h}r,(Xuniprotr,XP0A6T3r,Xasapr,X ABE-0002568r,Xecogener,XEG10363r,Xncbigener,X945358r,Xncbigir,X gi:16128725r,uh+Nh,h-]r,r,Rr,h1ubh)r,}r,(hXb2045r,hhh}r,h}r,(Xuniprotr,XP71242r,Xasapr,X ABE-0006780r,Xecogener,XEG13577r,Xncbigener,X946569r,Xncbigir,X gi:16129985r,uh+Nh,h-]r,r,Rr,h1ubh)r,}r,(hXb0756r,hhh}r,h}r,(Xuniprotr,XP0A9C3r,Xasapr,X ABE-0002566r,Xecogener,XEG11698r,Xncbigener,X944943r,Xncbigir,X gi:16128724r,uh+Nh,h-]r,r,Rr,h1ubh)r,}r,(hXb4119r,hhh}r,h}r,(Xuniprotr,XP06720r,Xasapr,X ABE-0013493r,Xecogener,XEG10577r,Xncbigener,X948636r,Xncbigir,X gi:16131945r,uh+Nh,h-]r,r,Rr,h1ubh)r,}r,(hXb3628r,hhh}r,h}r,(Xuniprotr,XP27127r,Xasapr,X ABE-0011862r,Xecogener,XEG11351r,Xncbigener,X948144r,Xncbigir,X gi:226524757r-uh+Nh,h-]r-r-Rr-h1ubh)r-}r-(hXb2093r-hhh}r-h}r-(Xuniprotr -XP37188r -Xasapr -X ABE-0006930r -Xecogener -XEG12415r-Xncbigener-X946610r-Xncbigir-X gi:16130031r-uh+Nh,h-]r-r-Rr-h1ubh)r-}r-(hXb2092r-hhh}r-h}r-(Xuniprotr-XP69831r-Xasapr-X ABE-0006928r-Xecogener-XEG12416r -Xncbigener!-X946603r"-uh+Nh,h-]r#-r$-Rr%-h1ubh)r&-}r'-(hXb2094r(-hhh}r)-h}r*-(Xuniprotr+-XP69828r,-Xasapr--X ABE-0006932r.-Xecogener/-XEG12414r0-Xncbigener1-X946633r2-Xncbigir3-X gi:16130032r4-uh+Nh,h-]r5-r6-Rr7-h1ubh)r8-}r9-(hXb3093r:-hhh}r;-h}r<-(Xuniprotr=-XP0AA78r>-Xasapr?-X ABE-0010171r@-XecogenerA-XEG12738rB-XncbigenerC-X947601rD-XncbigirE-X gi:674842734rF-uh+Nh,h-]rG-rH-RrI-h1ubh)rJ-}rK-(hXb1236rL-hhh}rM-h}rN-(XuniprotrO-XP0AEP3rP-XasaprQ-X ABE-0004150rR-XecogenerS-XEG11319rT-XncbigenerU-X945730rV-XncbigirW-X gi:16129197rX-uh+Nh,h-]rY-rZ-Rr[-h1ubh)r\-}r]-(hXb4230r^-hhh}r_-h}r`-(Xuniprotra-XP39328rb-Xasaprc-X ABE-0013838rd-Xecogenere-XEG12520rf-Xncbigenerg-X948743rh-Xncbigiri-X gi:145698343rj-uh+Nh,h-]rk-rl-Rrm-h1ubh)rn-}ro-(hXb2148rp-hhh}rq-h}rr-(Xuniprotrs-XP23200rt-Xasapru-X ABE-0007101rv-Xecogenerw-XEG10594rx-Xncbigenery-X949039rz-Xncbigir{-X gi:16130086r|-uh+Nh,h-]r}-r~-Rr-h1ubh)r-}r-(hXb2149r-hhh}r-h}r-(Xuniprotr-XP0AAG8r-Xasapr-X ABE-0007103r-Xecogener-XEG10592r-Xncbigener-X949036r-Xncbigir-X gi:16130087r-uh+Nh,h-]r-r-Rr-h1ubh)r-}r-(hXb2150r-hhh}r-h}r-(Xuniprotr-XP0AEE5r-Xasapr-X ABE-0007108r-Xecogener-XEG10593r-Xncbigener-X949041r-Xncbigir-X gi:16130088r-uh+Nh,h-]r-r-Rr-h1ubh)r-}r-(hXb4231r-hhh}r-h}r-(Xuniprotr-XP37772r-Xasapr-X ABE-0013840r-Xecogener-XEG12439r-Xncbigener-X948754r-Xncbigir-X gi:90111710r-uh+Nh,h-]r-r-Rr-h1ubh)r-}r-(hXb4485r-hhh}r-h}r-(Xuniprotr-XQ6BEX0r-Xasapr-X ABE-0174118r-Xecogener-XEG12518r-Xncbigener-X2847725r-Xncbigir-X 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gi:16130691r0.uh+Nh,h-]r1.r2.Rr3.h1ubh)r4.}r5.(hXb3650r6.hhh}r7.h}r8.(Xuniprotr9.XP0AG24r:.Xasapr;.X ABE-0011935r<.Xecogener=.XEG10966r>.Xncbigener?.X948159r@.XncbigirA.X gi:16131521rB.uh+Nh,h-]rC.rD.RrE.h1ubh)rF.}rG.(hXb2051rH.hhh}rI.h}rJ.(XuniprotrK.XP32056rL.XasaprM.X ABE-0006796rN.XecogenerO.XEG11789rP.XncbigenerQ.X946559rR.XncbigirS.X gi:90111376rT.uh+Nh,h-]rU.rV.RrW.h1ubh)rX.}rY.(hXb2467rZ.hhh}r[.h}r\.(Xuniprotr].XP37128r^.Xasapr_.X ABE-0008123r`.Xecogenera.XEG12410rb.Xncbigenerc.X947072rd.Xncbigire.X gi:16130392rf.uh+Nh,h-]rg.rh.Rri.h1ubh)rj.}rk.(hXb3729rl.hhh}rm.h}rn.(Xuniprotro.XP17169rp.Xasaprq.X ABE-0012198rr.Xecogeners.XEG10382rt.Xncbigeneru.X948241rv.Xncbigirw.X gi:16131597rx.uh+Nh,h-]ry.rz.Rr{.h1ubh)r|.}r}.(hXb1298r~.hhh}r.h}r.(Xuniprotr.XP76038r.Xasapr.X ABE-0004369r.Xecogener.XEG13909r.Xncbigener.X945882r.Xncbigir.X gi:90111245r.uh+Nh,h-]r.r.Rr.h1ubh)r.}r.(hXb1301r.hhh}r.h}r.(Xuniprotr.XP37906r.Xasapr.X ABE-0004378r.Xecogener.XEG11822r.Xncbigener.X945072r.Xncbigir.X 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23dhbzs_c n i 29 23dhdp_c n i 30 23dhmb_c n i 31 23dhmp_c n i 32 23doguln_c n i 33 25aics_c n i 34 25dkglcn_c n i 35 25drapp_c n i 36 26dap_LL_c n i 37 26dap__M_c n i 38 2agpe120_c n i 39 2agpe140_c n i 40 2agpe141_c n i 41 2agpe160_c n i 42 2agpe161_c n i 43 2agpe180_c n i 44 2agpe181_c n i 45 2agpg120_c n i 46 2agpg140_c n i 47 2agpg141_c n i 48 2agpg160_c n i 49 2agpg161_c n i 50 2agpg180_c n i 51 2agpg181_c n i 52 2ahbut_c n i 53 2amsa_c n i 54 2aobut_c n i 55 2cpr5p_c n i 56 2dda7p_c n i 57 2ddecg3p_c n i 58 2ddg6p_c n i 59 2ddglcn_c n i 60 2dh3dgal_c n i 61 2dh3dgal6p_c n i 62 2dhguln_c n i 63 2dhp_c n i 64 2dmmq8_c n i 65 2dmmql8_c n i 66 2dr1p_c n i 67 2dr5p_c n i 68 2fe1s_c n i 69 2fe2s_c n i 70 2h3oppan_c n i 71 2hdec9eg3p_c n i 72 2hdecg3p_c n i 73 2ippm_c n i 74 2mahmp_c n i 75 2mcacn_c n i 76 2mcit_c n i 77 2me4p_c n i 78 2mecdp_c n i 79 2obut_c n i 80 2odec11eg3p_c n i 81 2odecg3p_c n i 82 2ohph_c n i 83 2ombzl_c n i 84 2omhmbl_c n i 85 2ommbl_c n i 86 2omph_c n i 87 2oph_c n i 88 2oxpaccoa_c n i 89 2p4c2me_c n i 90 2pg_c n i 91 2pglyc_c n i 92 2sephchc_c n i 93 2shchc_c n i 94 2tdec7eg3p_c n i 95 2tdecg3p_c n i 96 2tpr3dpcoa_c n i 97 34hpp_c n i 98 35cgmp_c n i 99 3amac_c n i 100 3c2hmp_c n i 101 3c3hmp_c n i 102 3c4mop_c n i 103 3dhguln_c n i 104 3dhgulnp_c n i 105 3dhq_c n i 106 3dhsk_c n i 107 3fe4s_c n i 108 3haACP_c n i 109 3hadpcoa_c n i 110 3hbcoa_c n i 111 3hcddec5eACP_c n i 112 3hcinnm_c n i 113 3hcmrs7eACP_c n i 114 3hcpalm9eACP_c n i 115 3hcvac11eACP_c n i 116 3hdcoa_c n i 117 3hddcoa_c n i 118 3hddecACP_c n i 119 3hdecACP_c n i 120 3hhcoa_c n i 121 3hhdcoa_c n i 122 3hhexACP_c n i 123 3hmrsACP_c n i 124 3hocoa_c n i 125 3hoctACP_c n i 126 3hoctaACP_c n i 127 3hodcoa_c n i 128 3hpalmACP_c n i 129 3hpp_c n i 130 3hpppn_c n i 131 3htdcoa_c n i 132 3ig3p_c n i 133 3mob_c n i 134 3mop_c n i 135 3ocddec5eACP_c n i 136 3ocmrs7eACP_c n i 137 3ocpalm9eACP_c n i 138 3ocvac11eACP_c n i 139 3odcoa_c n i 140 3oddcoa_c n i 141 3oddecACP_c n i 142 3odecACP_c n i 143 3ohcoa_c n i 144 3ohdcoa_c n i 145 3ohexACP_c n i 146 3omrsACP_c n i 147 3oocoa_c n i 148 3ooctACP_c n i 149 3ooctdACP_c n i 150 3ohodcoa_c n i 151 3opalmACP_c n i 152 3ophb_c n i 153 3otdcoa_c n i 154 3oxdhscoa_c n i 155 3pg_c n i 156 3php_c n i 157 3psme_c n i 158 3sala_c n i 159 4abut_c n i 160 4abutn_c n i 161 4abz_c n i 162 4adcho_c n i 163 4ahmmp_c n i 164 4ampm_c n i 165 4c2me_c n i 166 4crsol_c n i 167 4fe4s_c n i 168 4h2opntn_c n i 169 4hbz_c n i 170 4hthr_c n i 171 4mhetz_c n i 172 4mop_c n i 173 4mpetz_c n i 174 4pasp_c n i 175 4per_c n i 176 4ppan_c n i 177 4ppcys_c n i 178 4r5au_c n i 179 56dura_c n i 180 5aizc_c n i 181 5aop_c n i 182 5aprbu_c n i 183 5apru_c n i 184 5caiz_c n i 185 5dglcn_c n i 186 5dh4dglc_c n i 187 5drib_c n i 188 5fthf_c n i 189 5mta_c n i 190 5mthf_c n i 191 5mtr_c n i 192 5prdmbz_c n i 193 6hmhpt_c n i 194 6hmhptpp_c n i 195 6pgc_c n i 196 6pgl_c n i 197 8aonn_c n i 198 ACP_c n i 199 LalaDglu_c n i 200 LalaDgluMdap_c n i 201 LalaDgluMdapDala_c n i 202 LalaLglu_c n i 203 N1aspmd_c n i 204 Nmtrp_c n i 205 Sfglutth_c n i 206 aacald_c n i 207 aacoa_c n i 208 aact_c n i 209 ac_c n i 210 acACP_c n i 211 acac_c n i 212 acald_c n i 213 acanth_c n i 214 accoa_c n i 215 acetol_c n i 216 acg5p_c n i 217 acg5sa_c n i 218 acgam_c n i 219 acgam1p_c n i 220 acgam6p_c n i 221 acglc__D_c n i 222 acglu_c n i 223 acmalt_c n i 224 acmana_c n i 225 acmanap_c n i 226 acmum6p_c n i 227 acnam_c n i 228 acon_C_c n i 229 acon_T_c n i 230 aconm_c n i 231 acorn_c n i 232 acser_c n i 233 actACP_c n i 234 actp_c n i 235 ade_c n i 236 adn_c n i 237 adocbi_c n i 238 adocbip_c n i 239 adocbl_c n i 240 adp_c n i 241 adpglc_c n i 242 adphep_DD_c n i 243 adphep_LD_c n i 244 adprib_c n i 245 ag_c n i 246 agdpcbi_c n i 247 agm_c n i 248 ahcys_c n i 249 ahdt_c n i 250 aicar_c n i 251 air_c n i 252 akg_c n i 253 ala_B_c n i 254 ala__D_c n i 255 ala__L_c n i 256 alaala_c n i 257 alac__S_c n i 258 alatrna_c n i 259 all__D_c n i 260 all6p_c n i 261 alltn_c n i 262 alltt_c n i 263 allul6p_c n i 264 altrn_c n i 265 amet_c n i 266 ametam_c n i 267 amob_c n i 268 amp_c n i 269 anhgm_c n i 270 anhgm3p_c n i 271 anhgm4p_c n i 272 anhm_c n i 273 anhm3p_c n i 274 anhm4p_c n i 275 anth_c n i 276 ap4a_c n i 277 ap5a_c n i 278 apg120_c n i 279 apg140_c n i 280 apg141_c n i 281 apg160_c n i 282 apg161_c n i 283 apg180_c n i 284 apg181_c n i 285 apoACP_c n i 286 appl_c n i 287 aps_c n i 288 ara5p_c n i 289 arab__L_c n i 290 aragund_c n i 291 arbt6p_c n i 292 arbtn_c n i 293 arbtn_fe3_c n i 294 arg__L_c n i 295 argsuc_c n i 296 argtrna_c n i 297 ascb6p_c n i 298 asn__L_c n i 299 asntrna_c n i 300 aso3_c n i 301 aso4_c n i 302 asp__L_c n i 303 aspsa_c n i 304 asptrna_c n i 305 athr__L_c n i 306 athtp_c n i 307 atp_c n i 308 b2coa_c n i 309 bbtcoa_c n i 310 betald_c n i 311 bglycogen_c n i 312 bmoco_c n i 313 bmoco1gdp_c n i 314 bmocogdp_c n i 315 btal_c n i 316 btcoa_c n i 317 btn_c n i 318 btnso_c n i 319 but_c n i 320 but2eACP_c n i 321 butACP_c n i 322 butso3_c n i 323 bwco_c n i 324 bwco1gdp_c n i 325 bwcogdp_c n i 326 ca2_c n i 327 camp_c n i 328 cbasp_c n i 329 cbi_c n i 330 cbl1_c n i 331 cbm_c n i 332 cbp_c n i 333 cd2_c n i 334 cddec5eACP_c n i 335 cdec3eACP_c n i 336 cdg_c n i 337 cdp_c n i 338 cdpdddecg_c n i 339 cdpdhdec9eg_c n i 340 cdpdhdecg_c n i 341 cdpdodec11eg_c n i 342 cdpdodecg_c n i 343 cdpdtdec7eg_c n i 344 cdpdtdecg_c n i 345 cechddd_c n i 346 cenchddd_c n i 347 cgly_c n i 348 chol_c n i 349 chor_c n i 350 chtbs6p_c n i 351 cinnm_c n i 352 cit_c n i 353 citr__L_c n i 354 ckdo_c n i 355 cl_c n i 356 cmp_c n i 357 co2_c n i 358 coa_c n i 359 cobalt2_c n i 360 colipa_c n i 361 cpe160_c n i 362 cpe180_c n i 363 cpg160_c n i 364 cpg180_c n i 365 cpgn_c n i 366 cpgn_un_c n i 367 cph4_c n i 368 cpmp_c n i 369 cpppg3_c n i 370 crn_c n i 371 crn__D_c n i 372 crnDcoa_c n i 373 crncoa_c n i 374 csn_c n i 375 ctbt_c n i 376 ctbtcoa_c n i 377 ctp_c n i 378 cu_c n i 379 cu2_c n i 380 cyan_c n i 381 cynt_c n i 382 cys__D_c n i 383 cys__L_c n i 384 cyst__L_c n i 385 cystrna_c n i 386 cytd_c n i 387 dad_2_c n i 388 dad_5_c n i 389 dadp_c n i 390 damp_c n i 391 dann_c n i 392 datp_c n i 393 db4p_c n i 394 dc2coa_c n i 395 dca_c n i 396 dcaACP_c n i 397 dcacoa_c n i 398 dcamp_c n i 399 dcdp_c n i 400 dcmp_c n i 401 dctp_c n i 402 dcyt_c n i 403 dd2coa_c n i 404 ddca_c n i 405 ddcaACP_c n i 406 ddcacoa_c n i 407 ddcap_c n i 408 dgdp_c n i 409 dgmp_c n i 410 dgslnt_c n i 411 dgsn_c n i 412 dgtp_c n i 413 dha_c n i 414 dhap_c n i 415 dhcinnm_c n i 416 dhf_c n i 417 dhgly_c n i 418 dhmpt_c n i 419 dhmptp_c n i 420 dhna_c n i 421 dhnpt_c n i 422 dhor__S_c n i 423 dhpmp_c n i 424 dhpppn_c n i 425 dhpt_c n i 426 dhptd_c n i 427 dhptdn_c n i 428 didp_c n i 429 dimp_c n i 430 din_c n i 431 ditp_c n i 432 dmbzid_c n i 433 dmlz_c n i 434 dmpp_c n i 435 dms_c n i 436 dmso_c n i 437 dnad_c n i 438 dpcoa_c n i 439 dsbdox_c n i 440 dsbdrd_c n i 441 dscl_c n i 442 dtbt_c n i 443 dtdp_c n i 444 dtdp4aaddg_c n i 445 dtdp4addg_c n i 446 dtdp4d6dg_c n i 447 dtdp4d6dm_c n i 448 dtdpglu_c n i 449 dtdprmn_c n i 450 dtmp_c n i 451 dttp_c n i 452 dudp_c n i 453 dump_c n i 454 duri_c n i 455 dutp_c n i 456 dxyl_c n i 457 dxyl5p_c n i 458 e4p_c n i 459 egmeACP_c n i 460 eig3p_c n i 461 enter_c n i 462 epmeACP_c n i 463 etha_c n i 464 ethso3_c n i 465 etoh_c n i 466 f1p_c n i 467 f6p_c n i 468 fad_c n i 469 fadh2_c n i 470 fald_c n i 471 fc1p_c n i 472 fcl__L_c n i 473 fdp_c n i 474 fe2_c n i 475 fe3_c n i 476 fe3dhbzs_c n i 477 fe3hox_c n i 478 fe3hox_un_c n i 479 fecrm_c n i 480 fecrm_un_c n i 481 feenter_c n i 482 feoxam_c n i 483 feoxam_un_c n i 484 fgam_c n i 485 flxr_c n i 486 flxso_c n i 487 fmettrna_c n i 488 fmn_c n i 489 fmnh2_c n i 490 for_c n i 491 forcoa_c n i 492 fpram_c n i 493 fprica_c n i 494 frdp_c n i 495 fru_c n i 496 frulys_c n i 497 frulysp_c n i 498 fruur_c n i 499 fuc__L_c n i 500 fum_c n i 501 g1p_c n i 502 g3p_c n i 503 g3pc_c n i 504 g3pe_c n i 505 g3pg_c n i 506 g3pi_c n i 507 g3ps_c n i 508 g6p_c n i 509 gagicolipa_c n i 510 gal_c n i 511 gal1p_c n i 512 galct__D_c n i 513 galctn__D_c n i 514 galctn__L_c n i 515 galt1p_c n i 516 galur_c n i 517 gam1p_c n i 518 gam6p_c n i 519 gar_c n i 520 garagund_c n i 521 gbbtn_c n i 522 gcald_c n i 523 gdp_c n i 524 gdpddman_c n i 525 gdpfuc_c n i 526 gdpmann_c n i 527 gdpofuc_c n i 528 gdptp_c n i 529 gfgaragund_c n i 530 gg4abut_c n i 531 ggagicolipa_c n i 532 ggbutal_c n i 533 gggagicolipa_c n i 534 ggptrc_c n i 535 ghb_c n i 536 gicolipa_c n i 537 glc__D_c n i 538 glcn_c n i 539 glcr_c n i 540 glcur_c n i 541 gln__L_c n i 542 glntrna_c n i 543 glu__D_c n i 544 glu__L_c n i 545 glu1sa_c n i 546 glu5p_c n i 547 glu5sa_c n i 548 glucys_c n i 549 glutrna_c n i 550 glx_c n i 551 gly_c n i 552 glyald_c n i 553 glyb_c n i 554 glyc_c n i 555 glyc__R_c n i 556 glyc2p_c n i 557 glyc3p_c n i 558 glyclt_c n i 559 glycogen_c n i 560 glytrna_c n i 561 gmeACP_c n i 562 gmhep17bp_c n i 563 gmhep1p_c n i 564 gmhep7p_c n i 565 gmp_c n i 566 gp4g_c n i 567 grdp_c n i 568 grxox_c n i 569 grxrd_c n i 570 gslnt_c n i 571 gsn_c n i 572 gthox_c n i 573 gthrd_c n i 574 gtp_c n i 575 gtspmd_c n i 576 gua_c n i 577 h_c n i 578 h2_c n i 579 h2mb4p_c n i 580 h2o_c n i 581 h2o2_c n i 582 h2s_c n i 583 hco3_c n i 584 hcys__L_c n i 585 hdca_c n i 586 hdcap_c n i 587 hdcea_c n i 588 hdceap_c n i 589 hdcoa_c n i 590 hdd2coa_c n i 591 hdeACP_c n i 592 hemeO_c n i 593 hexACP_c n i 594 hg2_c n i 595 hgmeACP_c n i 596 hhlipa_c n i 597 his__L_c n i 598 hisp_c n i 599 histd_c n i 600 histrna_c n i 601 hkndd_c n i 602 hkntd_c n i 603 hlipa_c n i 604 hmbil_c n i 605 hmgth_c n i 606 hom__L_c n i 607 hphhlipa_c n i 608 hpmeACP_c n i 609 hpyr_c n i 610 hqn_c n i 611 hx2coa_c n i 612 hxa_c n i 613 hxan_c n i 614 hxcoa_c n i 615 iasp_c n i 616 ichor_c n i 617 icit_c n i 618 icolipa_c n i 619 idon__L_c n i 620 idp_c n i 621 ile__L_c n i 622 iletrna_c n i 623 imacp_c n i 624 imp_c n i 625 indole_c n i 626 inost_c n i 627 ins_c n i 628 ipdp_c n i 629 iscs_c n i 630 iscssh_c n i 631 iscu_c n i 632 iscu_2fe2s_c n i 633 iscu_2fe2s2_c n i 634 iscu_4fe4s_c n i 635 isetac_c n i 636 itp_c n i 637 k_c n i 638 kdo_c n i 639 kdo2lipid4_c n i 640 kdo2lipid4L_c n i 641 kdo2lipid4p_c n i 642 kdo8p_c n i 643 kdolipid4_c n i 644 kphphhlipa_c n i 645 lac__D_c n i 646 lac__L_c n i 647 lald__D_c n i 648 lald__L_c n i 649 lcts_c n i 650 leu__L_c n i 651 leutrna_c n i 652 lgt__S_c n i 653 lipa_c n i 654 lipa_cold_c n i 655 lipidA_c n i 656 lipidAds_c n i 657 lipidX_c n i 658 lipoamp_c n i 659 lipoate_c n i 660 lipopb_c n i 661 lys__L_c n i 662 lystrna_c n i 663 lyx__L_c n i 664 mal__D_c n i 665 mal__L_c n i 666 malACP_c n i 667 malcoa_c n i 668 malcoame_c n i 669 malt_c n i 670 malt6p_c n i 671 malthp_c n i 672 malthx_c n i 673 maltpt_c n i 674 malttr_c n i 675 maltttr_c n i 676 man_c n i 677 man1p_c n i 678 man6p_c n i 679 man6pglyc_c n i 680 mana_c n i 681 mdhdhf_c n i 682 melib_c n i 683 meoh_c n i 684 mercppyr_c n i 685 met__D_c n i 686 met__L_c n i 687 methf_c n i 688 metsox_R__L_c n i 689 metsox_S__L_c n i 690 mettrna_c n i 691 mg2_c n i 692 mi1p__D_c n i 693 micit_c n i 694 mlthf_c n i 695 mmcoa__S_c n i 696 mmet_c n i 697 mn2_c n i 698 mnl1p_c n i 699 moadamp_c n i 700 moadcoo_c n i 701 moadcosh_c n i 702 mobd_c n i 703 moco_c n i 704 mococdp_c n i 705 mocogdp_c n i 706 mpt_c n i 707 mptamp_c n i 708 mql8_c n i 709 mqn8_c n i 710 msa_c n i 711 mso3_c n i 712 mthgxl_c n i 713 mththf_c n i 714 myrsACP_c n i 715 n2o_c n i 716 n8aspmd_c n i 717 na1_c n i 718 na15dap_c n i 719 nac_c n i 720 nad_c n i 721 nadh_c n i 722 nadp_c n i 723 nadph_c n i 724 ncam_c n i 725 nh4_c n i 726 ni2_c n i 727 nicrnt_c n i 728 nmn_c n i 729 no_c n i 730 no2_c n i 731 no3_c n i 732 o16aund_c n i 733 o2_c n i 734 o2s_c n i 735 oaa_c n i 736 oc2coa_c n i 737 ocACP_c n i 738 occoa_c n i 739 ocdca_c n i 740 ocdcaACP_c n i 741 ocdcap_c n i 742 ocdcea_c n i 743 ocdceap_c n i 744 octa_c n i 745 octapb_c n i 746 octdp_c n i 747 octeACP_c n i 748 od2coa_c n i 749 odecoa_c n i 750 ogmeACP_c n i 751 ohpb_c n i 752 op4en_c n i 753 opmeACP_c n i 754 orn_c n i 755 orot_c n i 756 orot5p_c n i 757 oxa_c n i 758 oxadpcoa_c n i 759 oxalcoa_c n i 760 oxam_c n i 761 oxur_c n i 762 pa120_c n i 763 pa140_c n i 764 pa141_c n i 765 pa160_c n i 766 pa161_c n i 767 pa180_c n i 768 pa181_c n i 769 pac_c n i 770 pacald_c n i 771 palmACP_c n i 772 pan4p_c n i 773 pant__R_c n i 774 pap_c n i 775 paps_c n i 776 pdx5p_c n i 777 pe120_c n i 778 pe140_c n i 779 pe141_c n i 780 pe160_c n i 781 pe161_c n i 782 pe180_c n i 783 pe181_c n i 784 pep_c n i 785 pg120_c n i 786 pg140_c n i 787 pg141_c n i 788 pg160_c n i 789 pg161_c n i 790 pg180_c n i 791 pg181_c n i 792 pgp120_c n i 793 pgp140_c n i 794 pgp141_c n i 795 pgp160_c n i 796 pgp161_c n i 797 pgp180_c n i 798 pgp181_c n i 799 phaccoa_c n i 800 phe__L_c n i 801 pheme_c n i 802 phetrna_c n i 803 phhlipa_c n i 804 phom_c n i 805 phphhlipa_c n i 806 phpyr_c n i 807 phthr_c n i 808 pi_c n i 809 pimACP_c n i 810 pmeACP_c n i 811 pmtcoa_c n i 812 pnto__R_c n i 813 poaac_c n i 814 ppa_c n i 815 ppal_c n i 816 ppap_c n i 817 ppbng_c n i 818 ppcoa_c n i 819 ppgpp_c n i 820 pphn_c n i 821 ppi_c n i 822 ppp9_c n i 823 pppg9_c n i 824 pppi_c n i 825 pppn_c n i 826 pram_c n i 827 pran_c n i 828 prbamp_c n i 829 prbatp_c n i 830 preq0_c n i 831 preq1_c n i 832 prfp_c n i 833 prlp_c n i 834 pro__L_c n i 835 progly_c n i 836 protrna_c n i 837 prpp_c n i 838 ps120_c n i 839 ps140_c n i 840 ps141_c n i 841 ps160_c n i 842 ps161_c n i 843 ps180_c n i 844 ps181_c n i 845 psclys_c n i 846 pser__L_c n i 847 ptrc_c n i 848 pyam5p_c n i 849 pydam_c n i 850 pydx_c n i 851 pydx5p_c n i 852 pydxn_c n i 853 pyr_c n i 854 q8_c n i 855 q8h2_c n i 856 quin_c n i 857 quln_c n i 858 r15bp_c n i 859 r1p_c n i 860 r5p_c n i 861 ragund_c n i 862 rbflvrd_c n i 863 rbl__L_c n i 864 rdmbzi_c n i 865 rephaccoa_c n i 866 rhcys_c n i 867 rib__D_c n i 868 ribflv_c n i 869 rml_c n i 870 rml1p_c n i 871 rmn_c n i 872 ru5p__D_c n i 873 ru5p__L_c n i 874 s17bp_c n i 875 s7p_c n i 876 sarcs_c n i 877 sbt6p_c n i 878 sbzcoa_c n i 879 scl_c n i 880 sectrna_c n i 881 sel_c n i 882 seln_c n i 883 selnp_c n i 884 ser__D_c n i 885 ser__L_c n i 886 seramp_c n i 887 sertrna_c n i 888 sertrna_sec_c n i 889 sheme_c n i 890 skm_c n i 891 skm5p_c n i 892 sl26da_c n i 893 sl2a6o_c n i 894 slnt_c n i 895 so2_c n i 896 so3_c n i 897 so4_c n i 898 spmd_c n i 899 stcoa_c n i 900 suc6p_c n i 901 sucarg_c n i 902 sucbz_c n i 903 succ_c n i 904 succoa_c n i 905 sucglu_c n i 906 sucgsa_c n i 907 suchms_c n i 908 sucorn_c n i 909 sucsal_c n i 910 sufbcd_c n i 911 sufbcd_2fe2s_c n i 912 sufbcd_2fe2s2_c n i 913 sufbcd_4fe4s_c n i 914 sufse_c n i 915 sufsesh_c n i 916 sulfac_c n i 917 t3c11vaceACP_c n i 918 t3c5ddeceACP_c n i 919 t3c7mrseACP_c n i 920 t3c9palmeACP_c n i 921 tag6p__D_c n i 922 tagdp__D_c n i 923 tagur_c n i 924 tartr__D_c n i 925 tartr__L_c n i 926 taur_c n i 927 tcynt_c n i 928 td2coa_c n i 929 tdcoa_c n i 930 tddec2eACP_c n i 931 tdeACP_c n i 932 tdec2eACP_c n i 933 tdecoa_c n i 934 thdp_c n i 935 thex2eACP_c n i 936 thf_c n i 937 thm_c n i 938 thmmp_c n i 939 thmnp_c n i 940 thmpp_c n i 941 thptdn_c n i 942 thr__L_c n i 943 thrtrna_c n i 944 thym_c n i 945 thymd_c n i 946 tma_c n i 947 tmao_c n i 948 tmrs2eACP_c n i 949 toct2eACP_c n i 950 toctd2eACP_c n i 951 tpalm2eACP_c n i 952 trdox_c n i 953 trdrd_c n i 954 tre_c n i 955 tre6p_c n i 956 trnaala_c n i 957 trnaarg_c n i 958 trnaasn_c n i 959 trnaasp_c n i 960 trnacys_c n i 961 trnagln_c n i 962 trnaglu_c n i 963 trnagly_c n i 964 trnahis_c n i 965 trnaile_c n i 966 trnaleu_c n i 967 trnalys_c n i 968 trnamet_c n i 969 trnaphe_c n i 970 trnapro_c n i 971 trnasecys_c n i 972 trnaser_c n i 973 trnathr_c n i 974 trnatrp_c n i 975 trnatyr_c n i 976 trnaval_c n i 977 trp__L_c n i 978 trptrna_c n i 979 tsul_c n i 980 ttdca_c n i 981 ttdcap_c n i 982 ttdcea_c n i 983 ttdceap_c n i 984 tungs_c n i 985 tyr__L_c n i 986 tyrtrna_c n i 987 u23ga_c n i 988 u3aga_c n i 989 u3hga_c n i 990 uLa4fn_c n i 991 uLa4n_c n i 992 uaagmda_c n i 993 uaccg_c n i 994 uacgam_c n i 995 uacmam_c n i 996 uacmamu_c n i 997 uagmda_c n i 998 uama_c n i 999 uamag_c n i 1000 uamr_c n i 1001 udcpdp_c n i 1002 udcpp_c n i 1003 udp_c n i 1004 udpLa4fn_c n i 1005 udpLa4n_c n i 1006 udpLa4o_c n i 1007 udpg_c n i 1008 udpgal_c n i 1009 udpgalfur_c n i 1010 udpglcur_c n i 1011 ugmd_c n i 1012 ugmda_c n i 1013 um4p_c n i 1014 ump_c n i 1015 unaga_c n i 1016 unagamu_c n i 1017 unagamuf_c n i 1018 uppg3_c n i 1019 ura_c n i 1020 uracp_c n i 1021 urate_c n i 1022 urdglyc_c n i 1023 urea_c n i 1024 uri_c n i 1025 utp_c n i 1026 val__L_c n i 1027 valtrna_c n i 1028 wco_c n i 1029 xan_c n i 1030 xdp_c n i 1031 xmp_c n i 1032 xtp_c n i 1033 xtsn_c n i 1034 xu5p__D_c n i 1035 xu5p__L_c n i 1036 xyl__D_c n i 1037 xylu__D_c n i 1038 xylu__L_c n i 1039 zn2_c n i 1040 12ppd__R_e n i 1041 12ppd__S_e n i 1042 14glucan_e n i 1043 15dap_e n i 1044 23camp_e n i 1045 23ccmp_e n i 1046 23cgmp_e n i 1047 23cump_e n i 1048 23dappa_e n i 1049 26dap__M_e n i 1050 2ddglcn_e n i 1051 34dhpac_e n i 1052 3amp_e n i 1053 3cmp_e n i 1054 3gmp_e n i 1055 3hcinnm_e n i 1056 3hpp_e n i 1057 3hpppn_e n i 1058 3ump_e n i 1059 4abut_e n i 1060 4hoxpacd_e n i 1061 5dglcn_e n i 1062 5mtr_e n i 1063 LalaDglu_e n i 1064 LalaDgluMdap_e n i 1065 LalaDgluMdapDala_e n i 1066 LalaLglu_e n i 1067 ac_e n i 1068 acac_e n i 1069 acald_e n i 1070 acgal_e n i 1071 acgal1p_e n i 1072 acgam_e n i 1073 acgam1p_e n i 1074 acmana_e n i 1075 acmum_e n i 1076 acnam_e n i 1077 acolipa_e n i 1078 acser_e n i 1079 ade_e n i 1080 adn_e n i 1081 adocbl_e n i 1082 ag_e n i 1083 agm_e n i 1084 akg_e n i 1085 ala_B_e n i 1086 ala__D_e n i 1087 ala__L_e n i 1088 alaala_e n i 1089 all__D_e n i 1090 alltn_e n i 1091 amp_e n i 1092 anhgm_e n i 1093 arab__L_e n i 1094 arbt_e n i 1095 arbtn_e n i 1096 arbtn_fe3_e n i 1097 arg__L_e n i 1098 ascb__L_e n i 1099 asn__L_e n i 1100 aso3_e n i 1101 asp__L_e n i 1102 btn_e n i 1103 but_e n i 1104 butso3_e n i 1105 ca2_e n i 1106 cbi_e n i 1107 cbl1_e n i 1108 cd2_e n i 1109 cgly_e n i 1110 chol_e n i 1111 chtbs_e n i 1112 cit_e n i 1113 cl_e n i 1114 cm_e n i 1115 cmp_e n i 1116 co2_e n i 1117 cobalt2_e n i 1118 colipa_e n i 1119 colipap_e n i 1120 cpgn_e n i 1121 cpgn_un_e n i 1122 crn_e n i 1123 crn__D_e n i 1124 csn_e n i 1125 cu_e n i 1126 cu2_e n i 1127 cyan_e n i 1128 cynt_e n i 1129 cys__D_e n i 1130 cys__L_e n i 1131 cytd_e n i 1132 dad_2_e n i 1133 damp_e n i 1134 dca_e n i 1135 dcmp_e n i 1136 dcyt_e n i 1137 ddca_e n i 1138 dgmp_e n i 1139 dgsn_e n i 1140 dha_e n i 1141 dimp_e n i 1142 din_e n i 1143 dms_e n i 1144 dmso_e n i 1145 dopa_e n i 1146 doxrbcn_e n i 1147 dtmp_e n i 1148 dump_e n i 1149 duri_e n i 1150 eca4colipa_e n i 1151 enlipa_e n i 1152 enter_e n i 1153 etha_e n i 1154 ethso3_e n i 1155 etoh_e n i 1156 f6p_e n i 1157 fald_e n i 1158 fe2_e n i 1159 fe3_e n i 1160 fe3dcit_e n i 1161 fe3dhbzs_e n i 1162 fe3hox_e n i 1163 fe3hox_un_e n i 1164 fecrm_e n i 1165 fecrm_un_e n i 1166 feenter_e n i 1167 feoxam_e n i 1168 feoxam_un_e n i 1169 for_e n i 1170 fru_e n i 1171 frulys_e n i 1172 fruur_e n i 1173 fuc__L_e n i 1174 fum_e n i 1175 fusa_e n i 1176 g1p_e n i 1177 g3pc_e n i 1178 g3pe_e n i 1179 g3pg_e n i 1180 g3pi_e n i 1181 g3ps_e n i 1182 g6p_e n i 1183 gal_e n i 1184 gal_bD_e n i 1185 gal1p_e n i 1186 galct__D_e n i 1187 galctn__D_e n i 1188 galctn__L_e n i 1189 galt_e n i 1190 galur_e n i 1191 gam_e n i 1192 gam6p_e n i 1193 gbbtn_e n i 1194 gdp_e n i 1195 glc__D_e n i 1196 glcn_e n i 1197 glcr_e n i 1198 glcur_e n i 1199 glcur1p_e n i 1200 gln__L_e n i 1201 glu__L_e n i 1202 gly_e n i 1203 glyald_e n i 1204 glyb_e n i 1205 glyc_e n i 1206 glyc__R_e n i 1207 glyc2p_e n i 1208 glyc3p_e n i 1209 glyclt_e n i 1210 gmp_e n i 1211 gsn_e n i 1212 gthox_e n i 1213 gthrd_e n i 1214 gtp_e n i 1215 gua_e n i 1216 h_e n i 1217 h2_e n i 1218 h2o_e n i 1219 h2o2_e n i 1220 h2s_e n i 1221 hacolipa_e n i 1222 halipa_e n i 1223 hdca_e n i 1224 hdcea_e n i 1225 hg2_e n i 1226 his__L_e n i 1227 hom__L_e n i 1228 hxa_e n i 1229 hxan_e n i 1230 idon__L_e n i 1231 ile__L_e n i 1232 imp_e n i 1233 indole_e n i 1234 inost_e n i 1235 ins_e n i 1236 isetac_e n i 1237 k_e n i 1238 kdo2lipid4_e n i 1239 lac__D_e n i 1240 lac__L_e n i 1241 lcts_e n i 1242 leu__L_e n i 1243 lipa_e n i 1244 lipa_cold_e n i 1245 lipoate_e n i 1246 lys__L_e n i 1247 lyx__L_e n i 1248 mal__D_e n i 1249 mal__L_e n i 1250 malt_e n i 1251 malthx_e n i 1252 maltpt_e n i 1253 malttr_e n i 1254 maltttr_e n i 1255 man_e n i 1256 man6p_e n i 1257 manglyc_e n i 1258 melib_e n i 1259 meoh_e n i 1260 met__D_e n i 1261 met__L_e n i 1262 metsox_R__L_e n i 1263 metsox_S__L_e n i 1264 mg2_e n i 1265 mincyc_e n i 1266 minohp_e n i 1267 mmet_e n i 1268 mn2_e n i 1269 mnl_e n i 1270 mobd_e n i 1271 mso3_e n i 1272 n2o_e n i 1273 na1_e n i 1274 nac_e n i 1275 nh4_e n i 1276 ni2_e n i 1277 nmn_e n i 1278 no_e n i 1279 no2_e n i 1280 no3_e n i 1281 novbcn_e n i 1282 o16a4colipa_e n i 1283 o2_e n i 1284 o2s_e n i 1285 ocdca_e n i 1286 ocdcea_e n i 1287 octa_e n i 1288 orn_e n i 1289 orot_e n i 1290 pacald_e n i 1291 peamn_e n i 1292 phe__L_e n i 1293 pheme_e n i 1294 pi_e n i 1295 pnto__R_e n i 1296 ppa_e n i 1297 ppal_e n i 1298 pppn_e n i 1299 ppt_e n i 1300 pro__L_e n i 1301 progly_e n i 1302 psclys_e n i 1303 pser__L_e n i 1304 ptrc_e n i 1305 pydam_e n i 1306 pydx_e n i 1307 pydxn_e n i 1308 pyr_e n i 1309 quin_e n i 1310 r5p_e n i 1311 rfamp_e n i 1312 rib__D_e n i 1313 rmn_e n i 1314 sbt__D_e n i 1315 sel_e n i 1316 ser__D_e n i 1317 ser__L_e n i 1318 skm_e n i 1319 slnt_e n i 1320 so2_e n i 1321 so3_e n i 1322 so4_e n i 1323 spmd_e n i 1324 succ_e n i 1325 sucr_e n i 1326 sulfac_e n i 1327 tartr__D_e n i 1328 tartr__L_e n i 1329 taur_e n i 1330 tcynt_e n i 1331 thm_e n i 1332 thr__L_e n i 1333 thrp_e n i 1334 thym_e n i 1335 thymd_e n i 1336 tma_e n i 1337 tmao_e n i 1338 tre_e n i 1339 trp__L_e n i 1340 tsul_e n i 1341 ttdca_e n i 1342 ttdcea_e n i 1343 ttrcyc_e n i 1344 tungs_e n i 1345 tym_e n i 1346 tyr__L_e n i 1347 tyrp_e n i 1348 uacgam_e n i 1349 udpacgal_e n i 1350 udpg_e n i 1351 udpgal_e n i 1352 udpglcur_e n i 1353 ump_e n i 1354 ura_e n i 1355 urea_e n i 1356 uri_e n i 1357 val__L_e n i 1358 xan_e n i 1359 xmp_e n i 1360 xtsn_e n i 1361 xyl__D_e n i 1362 xylu__L_e n i 1363 zn2_e n i 1364 12dgr120_p n i 1365 12dgr140_p n i 1366 12dgr141_p n i 1367 12dgr160_p n i 1368 12dgr161_p n i 1369 12dgr180_p n i 1370 12dgr181_p n i 1371 12ppd__R_p n i 1372 12ppd__S_p n i 1373 14glucan_p n i 1374 15dap_p n i 1375 1agpe120_p n i 1376 1agpe140_p n i 1377 1agpe141_p n i 1378 1agpe160_p n i 1379 1agpe161_p n i 1380 1agpe180_p n i 1381 1agpe181_p n i 1382 1agpg120_p n i 1383 1agpg140_p n i 1384 1agpg141_p n i 1385 1agpg160_p n i 1386 1agpg161_p n i 1387 1agpg180_p n i 1388 1agpg181_p n i 1389 1ddecg3p_p n i 1390 1hdec9eg3p_p n i 1391 1hdecg3p_p n i 1392 1odec11eg3p_p n i 1393 1odecg3p_p n i 1394 1tdec7eg3p_p n i 1395 1tdecg3p_p n i 1396 23camp_p n i 1397 23ccmp_p n i 1398 23cgmp_p n i 1399 23cump_p n i 1400 23dappa_p n i 1401 26dap__M_p n i 1402 2agpe120_p n i 1403 2agpe140_p n i 1404 2agpe141_p n i 1405 2agpe160_p n i 1406 2agpe161_p n i 1407 2agpe180_p n i 1408 2agpe181_p n i 1409 2agpg120_p n i 1410 2agpg140_p n i 1411 2agpg141_p n i 1412 2agpg160_p n i 1413 2agpg161_p n i 1414 2agpg180_p n i 1415 2agpg181_p n i 1416 2ddecg3p_p n i 1417 2ddglcn_p n i 1418 2hdec9eg3p_p n i 1419 2hdecg3p_p n i 1420 2odec11eg3p_p n i 1421 2odecg3p_p n i 1422 2tdec7eg3p_p n i 1423 2tdecg3p_p n i 1424 34dhpac_p n i 1425 3amp_p n i 1426 3cmp_p n i 1427 3gmp_p n i 1428 3hcinnm_p n i 1429 3hpp_p n i 1430 3hpppn_p n i 1431 3ump_p n i 1432 4abut_p n i 1433 4hoxpacd_p n i 1434 5dglcn_p n i 1435 5mtr_p n i 1436 LalaDglu_p n i 1437 LalaDgluMdap_p n i 1438 LalaDgluMdapDala_p n i 1439 LalaLglu_p n i 1440 ac_p n i 1441 acac_p n i 1442 acald_p n i 1443 acgal_p n i 1444 acgal1p_p n i 1445 acgam_p n i 1446 acgam1p_p n i 1447 acmana_p n i 1448 acmum_p n i 1449 acnam_p n i 1450 acolipa_p n i 1451 acser_p n i 1452 ade_p n i 1453 adn_p n i 1454 adocbl_p n i 1455 agm_p n i 1456 akg_p n i 1457 ala_B_p n i 1458 ala__D_p n i 1459 ala__L_p n i 1460 alaala_p n i 1461 all__D_p n i 1462 alltn_p n i 1463 alpp_p n i 1464 amp_p n i 1465 anhgm_p n i 1466 anhgm3p_p n i 1467 anhgm4p_p n i 1468 arab__L_p n i 1469 arbt_p n i 1470 arbtn_p n i 1471 arbtn_fe3_p n i 1472 arg__L_p n i 1473 ascb__L_p n i 1474 asn__L_p n i 1475 aso3_p n i 1476 asp__L_p n i 1477 btn_p n i 1478 but_p n i 1479 butso3_p n i 1480 ca2_p n i 1481 cbi_p n i 1482 cbl1_p n i 1483 cd2_p n i 1484 cgly_p n i 1485 chol_p n i 1486 chtbs_p n i 1487 cit_p n i 1488 cl_p n i 1489 clpn120_p n i 1490 clpn140_p n i 1491 clpn141_p n i 1492 clpn160_p n i 1493 clpn161_p n i 1494 clpn180_p n i 1495 clpn181_p n i 1496 cm_p n i 1497 cmp_p n i 1498 co2_p n i 1499 cobalt2_p n i 1500 colipa_p n i 1501 colipap_p n i 1502 cpgn_p n i 1503 cpgn_un_p n i 1504 crn_p n i 1505 crn__D_p n i 1506 csn_p n i 1507 ctbt_p n i 1508 cu_p n i 1509 cu2_p n i 1510 cyan_p n i 1511 cynt_p n i 1512 cys__D_p n i 1513 cys__L_p n i 1514 cytd_p n i 1515 dad_2_p n i 1516 damp_p n i 1517 dca_p n i 1518 dcmp_p n i 1519 dcyt_p n i 1520 ddca_p n i 1521 dgmp_p n i 1522 dgsn_p n i 1523 dha_p n i 1524 dimp_p n i 1525 din_p n i 1526 dms_p n i 1527 dmso_p n i 1528 dopa_p n i 1529 doxrbcn_p n i 1530 dsbaox_p n i 1531 dsbard_p n i 1532 dsbcox_p n i 1533 dsbcrd_p n i 1534 dsbgox_p n i 1535 dsbgrd_p n i 1536 dtmp_p n i 1537 dump_p n i 1538 duri_p n i 1539 eca2und_p n i 1540 eca3und_p n i 1541 eca4colipa_p n i 1542 eca4und_p n i 1543 enlipa_p n i 1544 enter_p n i 1545 etha_p n i 1546 ethso3_p n i 1547 etoh_p n i 1548 f6p_p n i 1549 fald_p n i 1550 fe2_p n i 1551 fe3_p n i 1552 fe3dcit_p n i 1553 fe3dhbzs_p n i 1554 fe3hox_p n i 1555 fe3hox_un_p n i 1556 fecrm_p n i 1557 fecrm_un_p n i 1558 feenter_p n i 1559 feoxam_p n i 1560 feoxam_un_p n i 1561 for_p n i 1562 fru_p n i 1563 frulys_p n i 1564 fruur_p n i 1565 fuc__L_p n i 1566 fum_p n i 1567 fusa_p n i 1568 g1p_p n i 1569 g3pc_p n i 1570 g3pe_p n i 1571 g3pg_p n i 1572 g3pi_p n i 1573 g3ps_p n i 1574 g6p_p n i 1575 gal_p n i 1576 gal_bD_p n i 1577 gal1p_p n i 1578 galct__D_p n i 1579 galctn__D_p n i 1580 galctn__L_p n i 1581 galt_p n i 1582 galur_p n i 1583 gam_p n i 1584 gam6p_p n i 1585 gbbtn_p n i 1586 gdp_p n i 1587 glc__D_p n i 1588 glcn_p n i 1589 glcr_p n i 1590 glcur_p n i 1591 glcur1p_p n i 1592 gln__L_p n i 1593 glu__L_p n i 1594 gly_p n i 1595 glyald_p n i 1596 glyb_p n i 1597 glyc_p n i 1598 glyc__R_p n i 1599 glyc2p_p n i 1600 glyc3p_p n i 1601 glyclt_p n i 1602 gmp_p n i 1603 gsn_p n i 1604 gthox_p n i 1605 gthrd_p n i 1606 gtp_p n i 1607 gua_p n i 1608 h_p n i 1609 h2_p n i 1610 h2o_p n i 1611 h2o2_p n i 1612 h2s_p n i 1613 hdca_p n i 1614 hdcea_p n i 1615 hg2_p n i 1616 his__L_p n i 1617 hom__L_p n i 1618 hxa_p n i 1619 hxan_p n i 1620 idon__L_p n i 1621 ile__L_p n i 1622 imp_p n i 1623 indole_p n i 1624 inost_p n i 1625 ins_p n i 1626 isetac_p n i 1627 k_p n i 1628 kdo2lipid4_p n i 1629 lac__D_p n i 1630 lac__L_p n i 1631 lcts_p n i 1632 leu__L_p n i 1633 lipa_p n i 1634 lipa_cold_p n i 1635 lipoate_p n i 1636 lpp_p n i 1637 lys__L_p n i 1638 lyx__L_p n i 1639 mal__D_p n i 1640 mal__L_p n i 1641 malt_p n i 1642 malthx_p n i 1643 maltpt_p n i 1644 malttr_p n i 1645 maltttr_p n i 1646 man_p n i 1647 man6p_p n i 1648 manglyc_p n i 1649 melib_p n i 1650 meoh_p n i 1651 met__D_p n i 1652 met__L_p n i 1653 metsox_R__L_p n i 1654 metsox_S__L_p n i 1655 mg2_p n i 1656 mincyc_p n i 1657 minohp_p n i 1658 mmet_p n i 1659 mn2_p n i 1660 mnl_p n i 1661 mobd_p n i 1662 mso3_p n i 1663 murein3p3p_p n i 1664 murein3px3p_p n i 1665 murein3px4p_p n i 1666 murein4p3p_p n i 1667 murein4p4p_p n i 1668 murein4px4p_p n i 1669 murein4px4p4p_p n i 1670 murein4px4px4p_p n i 1671 murein5p3p_p n i 1672 murein5p4p_p n i 1673 murein5p5p_p n i 1674 murein5p5p5p_p n i 1675 murein5px3p_p n i 1676 murein5px4p_p n i 1677 murein5px4px4p_p n i 1678 n2o_p n i 1679 na1_p n i 1680 nac_p n i 1681 nh4_p n i 1682 ni2_p n i 1683 nmn_p n i 1684 no_p n i 1685 no2_p n i 1686 no3_p n i 1687 novbcn_p n i 1688 o16a2und_p n i 1689 o16a3und_p n i 1690 o16a4colipa_p n i 1691 o16a4und_p n i 1692 o16aund_p n i 1693 o2_p n i 1694 o2s_p n i 1695 ocdca_p n i 1696 ocdcea_p n i 1697 octa_p n i 1698 orn_p n i 1699 orot_p n i 1700 pa120_p n i 1701 pa140_p n i 1702 pa141_p n i 1703 pa160_p n i 1704 pa161_p n i 1705 pa180_p n i 1706 pa181_p n i 1707 pacald_p n i 1708 pe120_p n i 1709 pe140_p n i 1710 pe141_p n i 1711 pe160_p n i 1712 pe161_p n i 1713 pe180_p n i 1714 pe181_p n i 1715 peamn_p n i 1716 pg120_p n i 1717 pg140_p n i 1718 pg141_p n i 1719 pg160_p n i 1720 pg161_p n i 1721 pg180_p n i 1722 pg181_p n i 1723 pgp120_p n i 1724 pgp140_p n i 1725 pgp141_p n i 1726 pgp160_p n i 1727 pgp161_p n i 1728 pgp180_p n i 1729 pgp181_p n i 1730 phe__L_p n i 1731 pheme_p n i 1732 pi_p n i 1733 pnto__R_p n i 1734 ppa_p n i 1735 ppal_p n i 1736 pppn_p n i 1737 ppt_p n i 1738 pro__L_p n i 1739 progly_p n i 1740 psclys_p n i 1741 pser__L_p n i 1742 ptrc_p n i 1743 pydam_p n i 1744 pydx_p n i 1745 pydxn_p n i 1746 pyr_p n i 1747 quin_p n i 1748 r5p_p n i 1749 rfamp_p n i 1750 rib__D_p n i 1751 rmn_p n i 1752 sbt__D_p n i 1753 sel_p n i 1754 ser__D_p n i 1755 ser__L_p n i 1756 skm_p n i 1757 slnt_p n i 1758 so2_p n i 1759 so3_p n i 1760 so4_p n i 1761 spmd_p n i 1762 succ_p n i 1763 sucr_p n i 1764 sulfac_p n i 1765 tartr__D_p n i 1766 tartr__L_p n i 1767 taur_p n i 1768 tcynt_p n i 1769 thm_p n i 1770 thr__L_p n i 1771 thrp_p n i 1772 thym_p n i 1773 thymd_p n i 1774 tma_p n i 1775 tmao_p n i 1776 tre_p n i 1777 trp__L_p n i 1778 tsul_p n i 1779 ttdca_p n i 1780 ttdcea_p n i 1781 ttrcyc_p n i 1782 tungs_p n i 1783 tym_p n i 1784 tyr__L_p n i 1785 tyrp_p n i 1786 uLa4n_p n i 1787 uacgam_p n i 1788 udcpdp_p n i 1789 udcpp_p n i 1790 udpacgal_p n i 1791 udpg_p n i 1792 udpgal_p n i 1793 udpglcur_p n i 1794 ump_p n i 1795 unagamuf_p n i 1796 ura_p n i 1797 urea_p n i 1798 uri_p n i 1799 val__L_p n i 1800 xan_p n i 1801 xmp_p n i 1802 xtsn_p n i 1803 xyl__D_p n i 1804 xylu__L_p n i 1805 zn2_p j 1 d 0 1000 n j 1 DM_4crsol_c j 2 s 0 n j 2 DM_4crsol_c_reverse_c57fa j 3 d 0 1000 n j 3 DM_5drib_c j 4 s 0 n j 4 DM_5drib_c_reverse_37606 j 5 d 0 1000 n j 5 DM_aacald_c j 6 s 0 n j 6 DM_aacald_c_reverse_6df50 j 7 d 0 1000 n j 7 DM_amob_c j 8 s 0 n j 8 DM_amob_c_reverse_90c8f j 9 d 0 1000 n j 9 DM_mththf_c j 10 s 0 n j 10 DM_mththf_c_reverse_46bf0 j 11 d 0 1000 n j 11 DM_oxam_c j 12 s 0 n j 12 DM_oxam_c_reverse_55504 j 13 d 0 1000 n j 13 BIOMASS_Ec_iJO1366_WT_53p95M j 14 s 0 n j 14 BIOMASS_Ec_iJO1366_WT_53p95M_reverse_06c4a j 15 d 0 1000 n j 15 BIOMASS_Ec_iJO1366_core_53p95M j 16 s 0 n j 16 BIOMASS_Ec_iJO1366_core_53p95M_reverse_5c8b1 j 17 d 0 1000 n j 17 EX_12ppd__R_e j 18 s 0 n j 18 EX_12ppd__R_e_reverse_cb334 j 19 d 0 1000 n j 19 EX_12ppd__S_e j 20 s 0 n j 20 EX_12ppd__S_e_reverse_6f659 j 21 d 0 1000 n j 21 EX_14glucan_e j 22 s 0 n j 22 EX_14glucan_e_reverse_4b919 j 23 d 0 1000 n j 23 EX_15dap_e j 24 s 0 n j 24 EX_15dap_e_reverse_020d4 j 25 d 0 1000 n j 25 EX_23camp_e j 26 s 0 n j 26 EX_23camp_e_reverse_60590 j 27 d 0 1000 n j 27 EX_23ccmp_e j 28 s 0 n j 28 EX_23ccmp_e_reverse_3700b j 29 d 0 1000 n j 29 EX_23cgmp_e j 30 s 0 n j 30 EX_23cgmp_e_reverse_48b5f j 31 d 0 1000 n j 31 EX_23cump_e j 32 s 0 n j 32 EX_23cump_e_reverse_f767c j 33 d 0 1000 n j 33 EX_23dappa_e j 34 s 0 n j 34 EX_23dappa_e_reverse_dfe54 j 35 d 0 1000 n j 35 EX_26dap__M_e j 36 s 0 n j 36 EX_26dap__M_e_reverse_a7bf0 j 37 d 0 1000 n j 37 EX_2ddglcn_e j 38 s 0 n j 38 EX_2ddglcn_e_reverse_e7cfb j 39 d 0 1000 n j 39 EX_34dhpac_e j 40 s 0 n j 40 EX_34dhpac_e_reverse_01c3a j 41 d 0 1000 n j 41 EX_3amp_e j 42 s 0 n j 42 EX_3amp_e_reverse_5699b j 43 d 0 1000 n j 43 EX_3cmp_e j 44 s 0 n j 44 EX_3cmp_e_reverse_4c45a j 45 d 0 1000 n j 45 EX_3gmp_e j 46 s 0 n j 46 EX_3gmp_e_reverse_00dd4 j 47 d 0 1000 n j 47 EX_3hcinnm_e j 48 s 0 n j 48 EX_3hcinnm_e_reverse_2f2b9 j 49 d 0 1000 n j 49 EX_3hpp_e j 50 s 0 n j 50 EX_3hpp_e_reverse_0d47d j 51 d 0 1000 n j 51 EX_3hpppn_e j 52 s 0 n j 52 EX_3hpppn_e_reverse_d4e4c j 53 d 0 1000 n j 53 EX_3ump_e j 54 s 0 n j 54 EX_3ump_e_reverse_f1b20 j 55 d 0 1000 n j 55 EX_4abut_e j 56 s 0 n j 56 EX_4abut_e_reverse_82295 j 57 d 0 1000 n j 57 EX_4hoxpacd_e j 58 s 0 n j 58 EX_4hoxpacd_e_reverse_7a05b j 59 d 0 1000 n j 59 EX_5dglcn_e j 60 s 0 n j 60 EX_5dglcn_e_reverse_af9e8 j 61 d 0 1000 n j 61 EX_5mtr_e j 62 s 0 n j 62 EX_5mtr_e_reverse_0c671 j 63 d 0 1000 n j 63 EX_LalaDglu_e j 64 s 0 n j 64 EX_LalaDglu_e_reverse_e27e3 j 65 d 0 1000 n j 65 EX_LalaDgluMdap_e j 66 s 0 n j 66 EX_LalaDgluMdap_e_reverse_1661a j 67 d 0 1000 n j 67 EX_LalaDgluMdapDala_e j 68 s 0 n j 68 EX_LalaDgluMdapDala_e_reverse_8fceb j 69 d 0 1000 n j 69 EX_LalaLglu_e j 70 s 0 n j 70 EX_LalaLglu_e_reverse_d1493 j 71 d 0 1000 n j 71 EX_ac_e j 72 s 0 n j 72 EX_ac_e_reverse_0be96 j 73 d 0 1000 n j 73 EX_acac_e j 74 s 0 n j 74 EX_acac_e_reverse_c46d5 j 75 d 0 1000 n j 75 EX_acald_e j 76 s 0 n j 76 EX_acald_e_reverse_c096e j 77 d 0 1000 n j 77 EX_acgal_e j 78 s 0 n j 78 EX_acgal_e_reverse_abd5a j 79 d 0 1000 n j 79 EX_acgal1p_e j 80 s 0 n j 80 EX_acgal1p_e_reverse_bcbdf j 81 d 0 1000 n j 81 EX_acgam_e j 82 s 0 n j 82 EX_acgam_e_reverse_da887 j 83 d 0 1000 n j 83 EX_acgam1p_e j 84 s 0 n j 84 EX_acgam1p_e_reverse_c1eb9 j 85 d 0 1000 n j 85 EX_acmana_e j 86 s 0 n j 86 EX_acmana_e_reverse_ae727 j 87 d 0 1000 n j 87 EX_acmum_e j 88 s 0 n j 88 EX_acmum_e_reverse_d42f2 j 89 d 0 1000 n j 89 EX_acnam_e j 90 s 0 n j 90 EX_acnam_e_reverse_7701a j 91 d 0 1000 n j 91 EX_acolipa_e j 92 s 0 n j 92 EX_acolipa_e_reverse_df5a1 j 93 d 0 1000 n j 93 EX_acser_e j 94 s 0 n j 94 EX_acser_e_reverse_f0c22 j 95 d 0 1000 n j 95 EX_ade_e j 96 s 0 n j 96 EX_ade_e_reverse_ea308 j 97 d 0 1000 n j 97 EX_adn_e j 98 s 0 n j 98 EX_adn_e_reverse_dffe8 j 99 d 0 1000 n j 99 EX_adocbl_e j 100 s 0 n j 100 EX_adocbl_e_reverse_70f85 j 101 d 0 1000 n j 101 EX_ag_e j 102 s 0 n j 102 EX_ag_e_reverse_4d026 j 103 d 0 1000 n j 103 EX_agm_e j 104 s 0 n j 104 EX_agm_e_reverse_86b3b j 105 d 0 1000 n j 105 EX_akg_e j 106 s 0 n j 106 EX_akg_e_reverse_70d85 j 107 d 0 1000 n j 107 EX_ala_B_e j 108 s 0 n j 108 EX_ala_B_e_reverse_16530 j 109 d 0 1000 n j 109 EX_ala__D_e j 110 s 0 n j 110 EX_ala__D_e_reverse_15447 j 111 d 0 1000 n j 111 EX_ala__L_e j 112 s 0 n j 112 EX_ala__L_e_reverse_1eb4b j 113 d 0 1000 n j 113 EX_alaala_e j 114 s 0 n j 114 EX_alaala_e_reverse_fad8b j 115 d 0 1000 n j 115 EX_all__D_e j 116 s 0 n j 116 EX_all__D_e_reverse_3d5f1 j 117 d 0 1000 n j 117 EX_alltn_e j 118 s 0 n j 118 EX_alltn_e_reverse_6592a j 119 d 0 1000 n j 119 EX_amp_e j 120 s 0 n j 120 EX_amp_e_reverse_ed5eb j 121 d 0 1000 n j 121 EX_anhgm_e j 122 s 0 n j 122 EX_anhgm_e_reverse_87c70 j 123 d 0 1000 n j 123 EX_arab__L_e j 124 s 0 n j 124 EX_arab__L_e_reverse_d0f5e j 125 d 0 1000 n j 125 EX_arbt_e j 126 s 0 n j 126 EX_arbt_e_reverse_87033 j 127 d 0 1000 n j 127 EX_arbtn_e j 128 s 0 n j 128 EX_arbtn_e_reverse_f3f32 j 129 d 0 1000 n j 129 EX_arbtn_fe3_e j 130 s 0 n j 130 EX_arbtn_fe3_e_reverse_ca996 j 131 d 0 1000 n j 131 EX_arg__L_e j 132 s 0 n j 132 EX_arg__L_e_reverse_d8799 j 133 d 0 1000 n j 133 EX_ascb__L_e j 134 s 0 n j 134 EX_ascb__L_e_reverse_75b41 j 135 d 0 1000 n j 135 EX_asn__L_e j 136 s 0 n j 136 EX_asn__L_e_reverse_460df j 137 d 0 1000 n j 137 EX_aso3_e j 138 s 0 n j 138 EX_aso3_e_reverse_32104 j 139 d 0 1000 n j 139 EX_asp__L_e j 140 s 0 n j 140 EX_asp__L_e_reverse_742f6 j 141 d 0 1000 n j 141 EX_btn_e j 142 s 0 n j 142 EX_btn_e_reverse_f2678 j 143 d 0 1000 n j 143 EX_but_e j 144 s 0 n j 144 EX_but_e_reverse_35eb9 j 145 d 0 1000 n j 145 EX_butso3_e j 146 s 0 n j 146 EX_butso3_e_reverse_31183 j 147 d 0 1000 n j 147 EX_ca2_e j 148 d 0 1000 n j 148 EX_ca2_e_reverse_aac13 j 149 d 0 1000 n j 149 EX_cbi_e j 150 s 0 n j 150 EX_cbi_e_reverse_207cf j 151 d 0 1000 n j 151 EX_cbl1_e j 152 d 0 0.01 n j 152 EX_cbl1_e_reverse_1093e j 153 d 0 1000 n j 153 EX_cd2_e j 154 s 0 n j 154 EX_cd2_e_reverse_0e8ba j 155 d 0 1000 n j 155 EX_cgly_e j 156 s 0 n j 156 EX_cgly_e_reverse_12551 j 157 d 0 1000 n j 157 EX_chol_e j 158 s 0 n j 158 EX_chol_e_reverse_b6b50 j 159 d 0 1000 n j 159 EX_chtbs_e j 160 s 0 n j 160 EX_chtbs_e_reverse_bbe59 j 161 d 0 1000 n j 161 EX_cit_e j 162 s 0 n j 162 EX_cit_e_reverse_0835e j 163 d 0 1000 n j 163 EX_cl_e j 164 d 0 1000 n j 164 EX_cl_e_reverse_2429b j 165 d 0 1000 n j 165 EX_cm_e j 166 s 0 n j 166 EX_cm_e_reverse_d60e9 j 167 d 0 1000 n j 167 EX_cmp_e j 168 s 0 n j 168 EX_cmp_e_reverse_e7a73 j 169 d 0 1000 n j 169 EX_co2_e j 170 d 0 1000 n j 170 EX_co2_e_reverse_d0466 j 171 d 0 1000 n j 171 EX_cobalt2_e j 172 d 0 1000 n j 172 EX_cobalt2_e_reverse_2bf0e j 173 d 0 1000 n j 173 EX_colipa_e j 174 s 0 n j 174 EX_colipa_e_reverse_f9125 j 175 d 0 1000 n j 175 EX_colipap_e j 176 s 0 n j 176 EX_colipap_e_reverse_c02be j 177 d 0 1000 n j 177 EX_cpgn_e j 178 s 0 n j 178 EX_cpgn_e_reverse_a1559 j 179 d 0 1000 n j 179 EX_cpgn_un_e j 180 s 0 n j 180 EX_cpgn_un_e_reverse_7a245 j 181 d 0 1000 n j 181 EX_crn_e j 182 s 0 n j 182 EX_crn_e_reverse_a19eb j 183 d 0 1000 n j 183 EX_crn__D_e j 184 s 0 n j 184 EX_crn__D_e_reverse_fbfeb j 185 d 0 1000 n j 185 EX_csn_e j 186 s 0 n j 186 EX_csn_e_reverse_80cf5 j 187 d 0 1000 n j 187 EX_cu_e j 188 s 0 n j 188 EX_cu_e_reverse_829ec j 189 d 0 1000 n j 189 EX_cu2_e j 190 d 0 1000 n j 190 EX_cu2_e_reverse_02682 j 191 d 0 1000 n j 191 EX_cyan_e j 192 s 0 n j 192 EX_cyan_e_reverse_7cb7e j 193 d 0 1000 n j 193 EX_cynt_e j 194 s 0 n j 194 EX_cynt_e_reverse_b53e8 j 195 d 0 1000 n j 195 EX_cys__D_e j 196 s 0 n j 196 EX_cys__D_e_reverse_fd0d3 j 197 d 0 1000 n j 197 EX_cys__L_e j 198 s 0 n j 198 EX_cys__L_e_reverse_41339 j 199 d 0 1000 n j 199 EX_cytd_e j 200 s 0 n j 200 EX_cytd_e_reverse_5950b j 201 d 0 1000 n j 201 EX_dad_2_e j 202 s 0 n j 202 EX_dad_2_e_reverse_41c79 j 203 d 0 1000 n j 203 EX_damp_e j 204 s 0 n j 204 EX_damp_e_reverse_2e1bd j 205 d 0 1000 n j 205 EX_dca_e j 206 s 0 n j 206 EX_dca_e_reverse_4575c j 207 d 0 1000 n j 207 EX_dcmp_e j 208 s 0 n j 208 EX_dcmp_e_reverse_83ed5 j 209 d 0 1000 n j 209 EX_dcyt_e j 210 s 0 n j 210 EX_dcyt_e_reverse_13102 j 211 d 0 1000 n j 211 EX_ddca_e j 212 s 0 n j 212 EX_ddca_e_reverse_6b9d2 j 213 d 0 1000 n j 213 EX_dgmp_e j 214 s 0 n j 214 EX_dgmp_e_reverse_88fd2 j 215 d 0 1000 n j 215 EX_dgsn_e j 216 s 0 n j 216 EX_dgsn_e_reverse_1b175 j 217 d 0 1000 n j 217 EX_dha_e j 218 s 0 n j 218 EX_dha_e_reverse_63f6d j 219 d 0 1000 n j 219 EX_dimp_e j 220 s 0 n j 220 EX_dimp_e_reverse_7aa5e j 221 d 0 1000 n j 221 EX_din_e j 222 s 0 n j 222 EX_din_e_reverse_f97eb j 223 d 0 1000 n j 223 EX_dms_e j 224 s 0 n j 224 EX_dms_e_reverse_51567 j 225 d 0 1000 n j 225 EX_dmso_e j 226 s 0 n j 226 EX_dmso_e_reverse_f4576 j 227 d 0 1000 n j 227 EX_dopa_e j 228 s 0 n j 228 EX_dopa_e_reverse_71ff0 j 229 d 0 1000 n j 229 EX_doxrbcn_e j 230 s 0 n j 230 EX_doxrbcn_e_reverse_d7e41 j 231 d 0 1000 n j 231 EX_dtmp_e j 232 s 0 n j 232 EX_dtmp_e_reverse_7a713 j 233 d 0 1000 n j 233 EX_dump_e j 234 s 0 n j 234 EX_dump_e_reverse_2c836 j 235 d 0 1000 n j 235 EX_duri_e j 236 s 0 n j 236 EX_duri_e_reverse_d0523 j 237 d 0 1000 n j 237 EX_eca4colipa_e j 238 s 0 n j 238 EX_eca4colipa_e_reverse_579ab j 239 d 0 1000 n j 239 EX_enlipa_e j 240 s 0 n j 240 EX_enlipa_e_reverse_2dd41 j 241 d 0 1000 n j 241 EX_enter_e j 242 s 0 n j 242 EX_enter_e_reverse_7bf45 j 243 d 0 1000 n j 243 EX_etha_e j 244 s 0 n j 244 EX_etha_e_reverse_a3984 j 245 d 0 1000 n j 245 EX_ethso3_e j 246 s 0 n j 246 EX_ethso3_e_reverse_d2ed7 j 247 d 0 1000 n j 247 EX_etoh_e j 248 s 0 n j 248 EX_etoh_e_reverse_cc64f j 249 d 0 1000 n j 249 EX_f6p_e j 250 s 0 n j 250 EX_f6p_e_reverse_e362f j 251 d 0 1000 n j 251 EX_fald_e j 252 s 0 n j 252 EX_fald_e_reverse_57d26 j 253 d 0 1000 n j 253 EX_fe2_e j 254 d 0 1000 n j 254 EX_fe2_e_reverse_25e68 j 255 d 0 1000 n j 255 EX_fe3_e j 256 d 0 1000 n j 256 EX_fe3_e_reverse_8b617 j 257 d 0 1000 n j 257 EX_fe3dcit_e j 258 s 0 n j 258 EX_fe3dcit_e_reverse_a5939 j 259 d 0 1000 n j 259 EX_fe3dhbzs_e j 260 s 0 n j 260 EX_fe3dhbzs_e_reverse_93deb j 261 d 0 1000 n j 261 EX_fe3hox_e j 262 s 0 n j 262 EX_fe3hox_e_reverse_e323a j 263 d 0 1000 n j 263 EX_fe3hox_un_e j 264 s 0 n j 264 EX_fe3hox_un_e_reverse_8be12 j 265 d 0 1000 n j 265 EX_fecrm_e j 266 s 0 n j 266 EX_fecrm_e_reverse_26983 j 267 d 0 1000 n j 267 EX_fecrm_un_e j 268 s 0 n j 268 EX_fecrm_un_e_reverse_b58fc j 269 d 0 1000 n j 269 EX_feenter_e j 270 s 0 n j 270 EX_feenter_e_reverse_73fde j 271 d 0 1000 n j 271 EX_feoxam_e j 272 s 0 n j 272 EX_feoxam_e_reverse_97348 j 273 d 0 1000 n j 273 EX_feoxam_un_e j 274 s 0 n j 274 EX_feoxam_un_e_reverse_7f6ec j 275 d 0 1000 n j 275 EX_for_e j 276 s 0 n j 276 EX_for_e_reverse_23269 j 277 d 0 1000 n j 277 EX_fru_e j 278 s 0 n j 278 EX_fru_e_reverse_c3828 j 279 d 0 1000 n j 279 EX_frulys_e j 280 s 0 n j 280 EX_frulys_e_reverse_379f1 j 281 d 0 1000 n j 281 EX_fruur_e j 282 s 0 n j 282 EX_fruur_e_reverse_2763f j 283 d 0 1000 n j 283 EX_fuc__L_e j 284 s 0 n j 284 EX_fuc__L_e_reverse_e70a8 j 285 d 0 1000 n j 285 EX_fum_e j 286 s 0 n j 286 EX_fum_e_reverse_e3432 j 287 d 0 1000 n j 287 EX_fusa_e j 288 s 0 n j 288 EX_fusa_e_reverse_bf5b5 j 289 d 0 1000 n j 289 EX_g1p_e j 290 s 0 n j 290 EX_g1p_e_reverse_350fc j 291 d 0 1000 n j 291 EX_g3pc_e j 292 s 0 n j 292 EX_g3pc_e_reverse_ffe85 j 293 d 0 1000 n j 293 EX_g3pe_e j 294 s 0 n j 294 EX_g3pe_e_reverse_8f39b j 295 d 0 1000 n j 295 EX_g3pg_e j 296 s 0 n j 296 EX_g3pg_e_reverse_5e370 j 297 d 0 1000 n j 297 EX_g3pi_e j 298 s 0 n j 298 EX_g3pi_e_reverse_f11b2 j 299 d 0 1000 n j 299 EX_g3ps_e j 300 s 0 n j 300 EX_g3ps_e_reverse_70d70 j 301 d 0 1000 n j 301 EX_g6p_e j 302 s 0 n j 302 EX_g6p_e_reverse_c15e5 j 303 d 0 1000 n j 303 EX_gal_e j 304 s 0 n j 304 EX_gal_e_reverse_d166c j 305 d 0 1000 n j 305 EX_gal_bD_e j 306 s 0 n j 306 EX_gal_bD_e_reverse_bdf24 j 307 d 0 1000 n j 307 EX_gal1p_e j 308 s 0 n j 308 EX_gal1p_e_reverse_aab63 j 309 d 0 1000 n j 309 EX_galct__D_e j 310 s 0 n j 310 EX_galct__D_e_reverse_ff408 j 311 d 0 1000 n j 311 EX_galctn__D_e j 312 s 0 n j 312 EX_galctn__D_e_reverse_c58a3 j 313 d 0 1000 n j 313 EX_galctn__L_e j 314 s 0 n j 314 EX_galctn__L_e_reverse_998c0 j 315 d 0 1000 n j 315 EX_galt_e j 316 s 0 n j 316 EX_galt_e_reverse_4d1d0 j 317 d 0 1000 n j 317 EX_galur_e j 318 s 0 n j 318 EX_galur_e_reverse_f6a10 j 319 d 0 1000 n j 319 EX_gam_e j 320 s 0 n j 320 EX_gam_e_reverse_3d249 j 321 d 0 1000 n j 321 EX_gam6p_e j 322 s 0 n j 322 EX_gam6p_e_reverse_be0d8 j 323 d 0 1000 n j 323 EX_gbbtn_e j 324 s 0 n j 324 EX_gbbtn_e_reverse_767c1 j 325 d 0 1000 n j 325 EX_gdp_e j 326 s 0 n j 326 EX_gdp_e_reverse_950ab j 327 d 0 1000 n j 327 EX_glc__D_e j 328 d 0 10 n j 328 EX_glc__D_e_reverse_af641 j 329 d 0 1000 n j 329 EX_glcn_e j 330 s 0 n j 330 EX_glcn_e_reverse_9e36b j 331 d 0 1000 n j 331 EX_glcr_e j 332 s 0 n j 332 EX_glcr_e_reverse_ea55f j 333 d 0 1000 n j 333 EX_glcur_e j 334 s 0 n j 334 EX_glcur_e_reverse_0ab2d j 335 d 0 1000 n j 335 EX_glcur1p_e j 336 s 0 n j 336 EX_glcur1p_e_reverse_7a2e5 j 337 d 0 1000 n j 337 EX_gln__L_e j 338 s 0 n j 338 EX_gln__L_e_reverse_6a1a1 j 339 d 0 1000 n j 339 EX_glu__L_e j 340 s 0 n j 340 EX_glu__L_e_reverse_42f6c j 341 d 0 1000 n j 341 EX_gly_e j 342 s 0 n j 342 EX_gly_e_reverse_6956b j 343 d 0 1000 n j 343 EX_glyald_e j 344 s 0 n j 344 EX_glyald_e_reverse_f761f j 345 d 0 1000 n j 345 EX_glyb_e j 346 s 0 n j 346 EX_glyb_e_reverse_0bd65 j 347 d 0 1000 n j 347 EX_glyc_e j 348 s 0 n j 348 EX_glyc_e_reverse_c3ec2 j 349 d 0 1000 n j 349 EX_glyc__R_e j 350 s 0 n j 350 EX_glyc__R_e_reverse_91560 j 351 d 0 1000 n j 351 EX_glyc2p_e j 352 s 0 n j 352 EX_glyc2p_e_reverse_7a57e j 353 d 0 1000 n j 353 EX_glyc3p_e j 354 s 0 n j 354 EX_glyc3p_e_reverse_74e5f j 355 d 0 1000 n j 355 EX_glyclt_e j 356 s 0 n j 356 EX_glyclt_e_reverse_395e1 j 357 d 0 1000 n j 357 EX_gmp_e j 358 s 0 n j 358 EX_gmp_e_reverse_f6d0a j 359 d 0 1000 n j 359 EX_gsn_e j 360 s 0 n j 360 EX_gsn_e_reverse_cb307 j 361 d 0 1000 n j 361 EX_gthox_e j 362 s 0 n j 362 EX_gthox_e_reverse_ca051 j 363 d 0 1000 n j 363 EX_gthrd_e j 364 s 0 n j 364 EX_gthrd_e_reverse_be1ab j 365 d 0 1000 n j 365 EX_gtp_e j 366 s 0 n j 366 EX_gtp_e_reverse_9f3aa j 367 d 0 1000 n j 367 EX_gua_e j 368 s 0 n j 368 EX_gua_e_reverse_11317 j 369 d 0 1000 n j 369 EX_h_e j 370 d 0 1000 n j 370 EX_h_e_reverse_3e0c5 j 371 d 0 1000 n j 371 EX_h2_e j 372 s 0 n j 372 EX_h2_e_reverse_f55e9 j 373 d 0 1000 n j 373 EX_h2o_e j 374 d 0 1000 n j 374 EX_h2o_e_reverse_3ced4 j 375 d 0 1000 n j 375 EX_h2o2_e j 376 s 0 n j 376 EX_h2o2_e_reverse_d52c5 j 377 d 0 1000 n j 377 EX_h2s_e j 378 s 0 n j 378 EX_h2s_e_reverse_c847c j 379 d 0 1000 n j 379 EX_hacolipa_e j 380 s 0 n j 380 EX_hacolipa_e_reverse_4a785 j 381 d 0 1000 n j 381 EX_halipa_e j 382 s 0 n j 382 EX_halipa_e_reverse_f6505 j 383 d 0 1000 n j 383 EX_hdca_e j 384 s 0 n j 384 EX_hdca_e_reverse_9ab38 j 385 d 0 1000 n j 385 EX_hdcea_e j 386 s 0 n j 386 EX_hdcea_e_reverse_e9406 j 387 d 0 1000 n j 387 EX_hg2_e j 388 s 0 n j 388 EX_hg2_e_reverse_fc882 j 389 d 0 1000 n j 389 EX_his__L_e j 390 s 0 n j 390 EX_his__L_e_reverse_33439 j 391 d 0 1000 n j 391 EX_hom__L_e j 392 s 0 n j 392 EX_hom__L_e_reverse_3e4e5 j 393 d 0 1000 n j 393 EX_hxa_e j 394 s 0 n j 394 EX_hxa_e_reverse_e1287 j 395 d 0 1000 n j 395 EX_hxan_e j 396 s 0 n j 396 EX_hxan_e_reverse_90f99 j 397 d 0 1000 n j 397 EX_idon__L_e j 398 s 0 n j 398 EX_idon__L_e_reverse_54aa6 j 399 d 0 1000 n j 399 EX_ile__L_e j 400 s 0 n j 400 EX_ile__L_e_reverse_e862a j 401 d 0 1000 n j 401 EX_imp_e j 402 s 0 n j 402 EX_imp_e_reverse_a877a j 403 d 0 1000 n j 403 EX_indole_e j 404 s 0 n j 404 EX_indole_e_reverse_3cb9c j 405 d 0 1000 n j 405 EX_inost_e j 406 s 0 n j 406 EX_inost_e_reverse_4d21d j 407 d 0 1000 n j 407 EX_ins_e j 408 s 0 n j 408 EX_ins_e_reverse_989e1 j 409 d 0 1000 n j 409 EX_isetac_e j 410 s 0 n j 410 EX_isetac_e_reverse_62d58 j 411 d 0 1000 n j 411 EX_k_e j 412 d 0 1000 n j 412 EX_k_e_reverse_42613 j 413 d 0 1000 n j 413 EX_kdo2lipid4_e j 414 s 0 n j 414 EX_kdo2lipid4_e_reverse_fb486 j 415 d 0 1000 n j 415 EX_lac__D_e j 416 s 0 n j 416 EX_lac__D_e_reverse_f95b4 j 417 d 0 1000 n j 417 EX_lac__L_e j 418 s 0 n j 418 EX_lac__L_e_reverse_8586b j 419 d 0 1000 n j 419 EX_lcts_e j 420 s 0 n j 420 EX_lcts_e_reverse_13088 j 421 d 0 1000 n j 421 EX_leu__L_e j 422 s 0 n j 422 EX_leu__L_e_reverse_d40a5 j 423 d 0 1000 n j 423 EX_lipa_e j 424 s 0 n j 424 EX_lipa_e_reverse_1cc13 j 425 d 0 1000 n j 425 EX_lipa_cold_e j 426 s 0 n j 426 EX_lipa_cold_e_reverse_ce98e j 427 d 0 1000 n j 427 EX_lipoate_e j 428 s 0 n j 428 EX_lipoate_e_reverse_4faad j 429 d 0 1000 n j 429 EX_lys__L_e j 430 s 0 n j 430 EX_lys__L_e_reverse_4f08c j 431 d 0 1000 n j 431 EX_lyx__L_e j 432 s 0 n j 432 EX_lyx__L_e_reverse_693d8 j 433 d 0 1000 n j 433 EX_mal__D_e j 434 s 0 n j 434 EX_mal__D_e_reverse_ce476 j 435 d 0 1000 n j 435 EX_mal__L_e j 436 s 0 n j 436 EX_mal__L_e_reverse_af154 j 437 d 0 1000 n j 437 EX_malt_e j 438 s 0 n j 438 EX_malt_e_reverse_ab2d1 j 439 d 0 1000 n j 439 EX_malthx_e j 440 s 0 n j 440 EX_malthx_e_reverse_73b54 j 441 d 0 1000 n j 441 EX_maltpt_e j 442 s 0 n j 442 EX_maltpt_e_reverse_f82d5 j 443 d 0 1000 n j 443 EX_malttr_e j 444 s 0 n j 444 EX_malttr_e_reverse_241c6 j 445 d 0 1000 n j 445 EX_maltttr_e j 446 s 0 n j 446 EX_maltttr_e_reverse_90d23 j 447 d 0 1000 n j 447 EX_man_e j 448 s 0 n j 448 EX_man_e_reverse_48020 j 449 d 0 1000 n j 449 EX_man6p_e j 450 s 0 n j 450 EX_man6p_e_reverse_b160c j 451 d 0 1000 n j 451 EX_manglyc_e j 452 s 0 n j 452 EX_manglyc_e_reverse_51b57 j 453 d 0 1000 n j 453 EX_melib_e j 454 s 0 n j 454 EX_melib_e_reverse_68f2f j 455 d 0 1000 n j 455 EX_meoh_e j 456 s 0 n j 456 EX_meoh_e_reverse_45228 j 457 d 0 1000 n j 457 EX_met__D_e j 458 s 0 n j 458 EX_met__D_e_reverse_6e944 j 459 d 0 1000 n j 459 EX_met__L_e j 460 s 0 n j 460 EX_met__L_e_reverse_14908 j 461 d 0 1000 n j 461 EX_metsox_R__L_e j 462 s 0 n j 462 EX_metsox_R__L_e_reverse_f461d j 463 d 0 1000 n j 463 EX_metsox_S__L_e j 464 s 0 n j 464 EX_metsox_S__L_e_reverse_27066 j 465 d 0 1000 n j 465 EX_mg2_e j 466 d 0 1000 n j 466 EX_mg2_e_reverse_b1c98 j 467 d 0 1000 n j 467 EX_mincyc_e j 468 s 0 n j 468 EX_mincyc_e_reverse_10267 j 469 d 0 1000 n j 469 EX_minohp_e j 470 s 0 n j 470 EX_minohp_e_reverse_3ce45 j 471 d 0 1000 n j 471 EX_mmet_e j 472 s 0 n j 472 EX_mmet_e_reverse_b6b96 j 473 d 0 1000 n j 473 EX_mn2_e j 474 d 0 1000 n j 474 EX_mn2_e_reverse_48316 j 475 d 0 1000 n j 475 EX_mnl_e j 476 s 0 n j 476 EX_mnl_e_reverse_c8f2a j 477 d 0 1000 n j 477 EX_mobd_e j 478 d 0 1000 n j 478 EX_mobd_e_reverse_c6396 j 479 d 0 1000 n j 479 EX_mso3_e j 480 s 0 n j 480 EX_mso3_e_reverse_b23ed j 481 d 0 1000 n j 481 EX_n2o_e j 482 s 0 n j 482 EX_n2o_e_reverse_33405 j 483 d 0 1000 n j 483 EX_na1_e j 484 d 0 1000 n j 484 EX_na1_e_reverse_c64df j 485 d 0 1000 n j 485 EX_nac_e j 486 s 0 n j 486 EX_nac_e_reverse_23356 j 487 d 0 1000 n j 487 EX_nh4_e j 488 d 0 1000 n j 488 EX_nh4_e_reverse_f9cc6 j 489 d 0 1000 n j 489 EX_ni2_e j 490 d 0 1000 n j 490 EX_ni2_e_reverse_7ba33 j 491 d 0 1000 n j 491 EX_nmn_e j 492 s 0 n j 492 EX_nmn_e_reverse_59f7d j 493 d 0 1000 n j 493 EX_no_e j 494 s 0 n j 494 EX_no_e_reverse_ffd01 j 495 d 0 1000 n j 495 EX_no2_e j 496 s 0 n j 496 EX_no2_e_reverse_1ddec j 497 d 0 1000 n j 497 EX_no3_e j 498 s 0 n j 498 EX_no3_e_reverse_98d30 j 499 d 0 1000 n j 499 EX_novbcn_e j 500 s 0 n j 500 EX_novbcn_e_reverse_14cc2 j 501 d 0 1000 n j 501 EX_o16a4colipa_e j 502 s 0 n j 502 EX_o16a4colipa_e_reverse_48f50 j 503 d 0 1000 n j 503 EX_o2_e j 504 d 0 1000 n j 504 EX_o2_e_reverse_efa94 j 505 d 0 1000 n j 505 EX_o2s_e j 506 s 0 n j 506 EX_o2s_e_reverse_82732 j 507 d 0 1000 n j 507 EX_ocdca_e j 508 s 0 n j 508 EX_ocdca_e_reverse_86d1d j 509 d 0 1000 n j 509 EX_ocdcea_e j 510 s 0 n j 510 EX_ocdcea_e_reverse_5ebe2 j 511 d 0 1000 n j 511 EX_octa_e j 512 s 0 n j 512 EX_octa_e_reverse_38d1e j 513 d 0 1000 n j 513 EX_orn_e j 514 s 0 n j 514 EX_orn_e_reverse_269bb j 515 d 0 1000 n j 515 EX_orot_e j 516 s 0 n j 516 EX_orot_e_reverse_032e7 j 517 d 0 1000 n j 517 EX_pacald_e j 518 s 0 n j 518 EX_pacald_e_reverse_54b80 j 519 d 0 1000 n j 519 EX_peamn_e j 520 s 0 n j 520 EX_peamn_e_reverse_34c33 j 521 d 0 1000 n j 521 EX_phe__L_e j 522 s 0 n j 522 EX_phe__L_e_reverse_10066 j 523 d 0 1000 n j 523 EX_pheme_e j 524 s 0 n j 524 EX_pheme_e_reverse_8e123 j 525 d 0 1000 n j 525 EX_pi_e j 526 d 0 1000 n j 526 EX_pi_e_reverse_1fb09 j 527 d 0 1000 n j 527 EX_pnto__R_e j 528 s 0 n j 528 EX_pnto__R_e_reverse_a498c j 529 d 0 1000 n j 529 EX_ppa_e j 530 s 0 n j 530 EX_ppa_e_reverse_1faa3 j 531 d 0 1000 n j 531 EX_ppal_e j 532 s 0 n j 532 EX_ppal_e_reverse_bb983 j 533 d 0 1000 n j 533 EX_pppn_e j 534 s 0 n j 534 EX_pppn_e_reverse_4f187 j 535 d 0 1000 n j 535 EX_ppt_e j 536 s 0 n j 536 EX_ppt_e_reverse_b97a7 j 537 d 0 1000 n j 537 EX_pro__L_e j 538 s 0 n j 538 EX_pro__L_e_reverse_5f8c5 j 539 d 0 1000 n j 539 EX_progly_e j 540 s 0 n j 540 EX_progly_e_reverse_cccb4 j 541 d 0 1000 n j 541 EX_psclys_e j 542 s 0 n j 542 EX_psclys_e_reverse_4686a j 543 d 0 1000 n j 543 EX_pser__L_e j 544 s 0 n j 544 EX_pser__L_e_reverse_164b6 j 545 d 0 1000 n j 545 EX_ptrc_e j 546 s 0 n j 546 EX_ptrc_e_reverse_6c850 j 547 d 0 1000 n j 547 EX_pydam_e j 548 s 0 n j 548 EX_pydam_e_reverse_b5003 j 549 d 0 1000 n j 549 EX_pydx_e j 550 s 0 n j 550 EX_pydx_e_reverse_9cc71 j 551 d 0 1000 n j 551 EX_pydxn_e j 552 s 0 n j 552 EX_pydxn_e_reverse_ce451 j 553 d 0 1000 n j 553 EX_pyr_e j 554 s 0 n j 554 EX_pyr_e_reverse_1f6de j 555 d 0 1000 n j 555 EX_quin_e j 556 s 0 n j 556 EX_quin_e_reverse_45058 j 557 d 0 1000 n j 557 EX_r5p_e j 558 s 0 n j 558 EX_r5p_e_reverse_383fe j 559 d 0 1000 n j 559 EX_rfamp_e j 560 s 0 n j 560 EX_rfamp_e_reverse_6d14f j 561 d 0 1000 n j 561 EX_rib__D_e j 562 s 0 n j 562 EX_rib__D_e_reverse_4a19a j 563 d 0 1000 n j 563 EX_rmn_e j 564 s 0 n j 564 EX_rmn_e_reverse_b3160 j 565 d 0 1000 n j 565 EX_sbt__D_e j 566 s 0 n j 566 EX_sbt__D_e_reverse_52f23 j 567 d 0 1000 n j 567 EX_sel_e j 568 d 0 1000 n j 568 EX_sel_e_reverse_ffc35 j 569 d 0 1000 n j 569 EX_ser__D_e j 570 s 0 n j 570 EX_ser__D_e_reverse_99725 j 571 d 0 1000 n j 571 EX_ser__L_e j 572 s 0 n j 572 EX_ser__L_e_reverse_b0b37 j 573 d 0 1000 n j 573 EX_skm_e j 574 s 0 n j 574 EX_skm_e_reverse_ed122 j 575 d 0 1000 n j 575 EX_slnt_e j 576 d 0 1000 n j 576 EX_slnt_e_reverse_97bba j 577 d 0 1000 n j 577 EX_so2_e j 578 s 0 n j 578 EX_so2_e_reverse_de12f j 579 d 0 1000 n j 579 EX_so3_e j 580 s 0 n j 580 EX_so3_e_reverse_116a1 j 581 d 0 1000 n j 581 EX_so4_e j 582 d 0 1000 n j 582 EX_so4_e_reverse_5c8ed j 583 d 0 1000 n j 583 EX_spmd_e j 584 s 0 n j 584 EX_spmd_e_reverse_761ad j 585 d 0 1000 n j 585 EX_succ_e j 586 s 0 n j 586 EX_succ_e_reverse_a9039 j 587 d 0 1000 n j 587 EX_sucr_e j 588 s 0 n j 588 EX_sucr_e_reverse_0215c j 589 d 0 1000 n j 589 EX_sulfac_e j 590 s 0 n j 590 EX_sulfac_e_reverse_2695d j 591 d 0 1000 n j 591 EX_tartr__D_e j 592 s 0 n j 592 EX_tartr__D_e_reverse_93e71 j 593 d 0 1000 n j 593 EX_tartr__L_e j 594 s 0 n j 594 EX_tartr__L_e_reverse_6023a j 595 d 0 1000 n j 595 EX_taur_e j 596 s 0 n j 596 EX_taur_e_reverse_69949 j 597 d 0 1000 n j 597 EX_tcynt_e j 598 s 0 n j 598 EX_tcynt_e_reverse_739e6 j 599 d 0 1000 n j 599 EX_thm_e j 600 s 0 n j 600 EX_thm_e_reverse_96547 j 601 d 0 1000 n j 601 EX_thr__L_e j 602 s 0 n j 602 EX_thr__L_e_reverse_ddaf9 j 603 d 0 1000 n j 603 EX_thrp_e j 604 s 0 n j 604 EX_thrp_e_reverse_51d63 j 605 d 0 1000 n j 605 EX_thym_e j 606 s 0 n j 606 EX_thym_e_reverse_84e99 j 607 d 0 1000 n j 607 EX_thymd_e j 608 s 0 n j 608 EX_thymd_e_reverse_94c6c j 609 d 0 1000 n j 609 EX_tma_e j 610 s 0 n j 610 EX_tma_e_reverse_1429d j 611 d 0 1000 n j 611 EX_tmao_e j 612 s 0 n j 612 EX_tmao_e_reverse_18ba4 j 613 d 0 1000 n j 613 EX_tre_e j 614 s 0 n j 614 EX_tre_e_reverse_fb5f1 j 615 d 0 1000 n j 615 EX_trp__L_e j 616 s 0 n j 616 EX_trp__L_e_reverse_d9983 j 617 d 0 1000 n j 617 EX_tsul_e j 618 s 0 n j 618 EX_tsul_e_reverse_22ca1 j 619 d 0 1000 n j 619 EX_ttdca_e j 620 s 0 n j 620 EX_ttdca_e_reverse_c67f4 j 621 d 0 1000 n j 621 EX_ttdcea_e j 622 s 0 n j 622 EX_ttdcea_e_reverse_e3700 j 623 d 0 1000 n j 623 EX_ttrcyc_e j 624 s 0 n j 624 EX_ttrcyc_e_reverse_0d8fb j 625 d 0 1000 n j 625 EX_tungs_e j 626 d 0 1000 n j 626 EX_tungs_e_reverse_6ffff j 627 d 0 1000 n j 627 EX_tym_e j 628 s 0 n j 628 EX_tym_e_reverse_e1acb j 629 d 0 1000 n j 629 EX_tyr__L_e j 630 s 0 n j 630 EX_tyr__L_e_reverse_516d2 j 631 d 0 1000 n j 631 EX_tyrp_e j 632 s 0 n j 632 EX_tyrp_e_reverse_36480 j 633 d 0 1000 n j 633 EX_uacgam_e j 634 s 0 n j 634 EX_uacgam_e_reverse_abfb0 j 635 d 0 1000 n j 635 EX_udpacgal_e j 636 s 0 n j 636 EX_udpacgal_e_reverse_a02b1 j 637 d 0 1000 n j 637 EX_udpg_e j 638 s 0 n j 638 EX_udpg_e_reverse_a3f02 j 639 d 0 1000 n j 639 EX_udpgal_e j 640 s 0 n j 640 EX_udpgal_e_reverse_9c019 j 641 d 0 1000 n j 641 EX_udpglcur_e j 642 s 0 n j 642 EX_udpglcur_e_reverse_baf0e j 643 d 0 1000 n j 643 EX_ump_e j 644 s 0 n j 644 EX_ump_e_reverse_58471 j 645 d 0 1000 n j 645 EX_ura_e j 646 s 0 n j 646 EX_ura_e_reverse_98cbe j 647 d 0 1000 n j 647 EX_urea_e j 648 s 0 n j 648 EX_urea_e_reverse_02f51 j 649 d 0 1000 n j 649 EX_uri_e j 650 s 0 n j 650 EX_uri_e_reverse_019f3 j 651 d 0 1000 n j 651 EX_val__L_e j 652 s 0 n j 652 EX_val__L_e_reverse_9e0f7 j 653 d 0 1000 n j 653 EX_xan_e j 654 s 0 n j 654 EX_xan_e_reverse_7bb40 j 655 d 0 1000 n j 655 EX_xmp_e j 656 s 0 n j 656 EX_xmp_e_reverse_5fd31 j 657 d 0 1000 n j 657 EX_xtsn_e j 658 s 0 n j 658 EX_xtsn_e_reverse_33417 j 659 d 0 1000 n j 659 EX_xyl__D_e j 660 s 0 n j 660 EX_xyl__D_e_reverse_e202a j 661 d 0 1000 n j 661 EX_xylu__L_e j 662 s 0 n j 662 EX_xylu__L_e_reverse_a93ff j 663 d 0 1000 n j 663 EX_zn2_e j 664 d 0 1000 n j 664 EX_zn2_e_reverse_3c725 j 665 d 0 1000 n j 665 12DGR120tipp j 666 s 0 n j 666 12DGR120tipp_reverse_757ef j 667 d 0 1000 n j 667 12DGR140tipp j 668 s 0 n j 668 12DGR140tipp_reverse_aea46 j 669 d 0 1000 n j 669 12DGR141tipp j 670 s 0 n j 670 12DGR141tipp_reverse_a0957 j 671 d 0 1000 n j 671 12DGR160tipp j 672 s 0 n j 672 12DGR160tipp_reverse_eac16 j 673 d 0 1000 n j 673 12DGR161tipp j 674 s 0 n j 674 12DGR161tipp_reverse_dcd80 j 675 d 0 1000 n j 675 12DGR180tipp j 676 s 0 n j 676 12DGR180tipp_reverse_7a012 j 677 d 0 1000 n j 677 12DGR181tipp j 678 s 0 n j 678 12DGR181tipp_reverse_34fd5 j 679 d 0 1000 n j 679 12PPDRtex j 680 d 0 1000 n j 680 12PPDRtex_reverse_1d688 j 681 d 0 1000 n j 681 12PPDRtpp j 682 d 0 1000 n j 682 12PPDRtpp_reverse_c5519 j 683 d 0 1000 n j 683 12PPDStex j 684 d 0 1000 n j 684 12PPDStex_reverse_ec7d6 j 685 d 0 1000 n j 685 12PPDStpp j 686 d 0 1000 n j 686 12PPDStpp_reverse_81431 j 687 d 0 1000 n j 687 14GLUCANabcpp j 688 s 0 n j 688 14GLUCANabcpp_reverse_c6351 j 689 d 0 1000 n j 689 14GLUCANtexi j 690 s 0 n j 690 14GLUCANtexi_reverse_2d4f8 j 691 d 0 1000 n j 691 23CAMPtex j 692 d 0 1000 n j 692 23CAMPtex_reverse_1c6db j 693 d 0 1000 n j 693 23CCMPtex j 694 d 0 1000 n j 694 23CCMPtex_reverse_e7e14 j 695 d 0 1000 n j 695 23CGMPtex j 696 d 0 1000 n j 696 23CGMPtex_reverse_d266a j 697 d 0 1000 n j 697 23CUMPtex j 698 d 0 1000 n j 698 23CUMPtex_reverse_7c421 j 699 d 0 1000 n j 699 23DAPPAt2pp j 700 s 0 n j 700 23DAPPAt2pp_reverse_59b55 j 701 d 0 1000 n j 701 23DAPPAtex j 702 d 0 1000 n j 702 23DAPPAtex_reverse_0c987 j 703 d 0 1000 n j 703 23PDE2pp j 704 s 0 n j 704 23PDE2pp_reverse_9b346 j 705 d 0 1000 n j 705 23PDE4pp j 706 s 0 n j 706 23PDE4pp_reverse_2a6cf j 707 d 0 1000 n j 707 23PDE7pp j 708 s 0 n j 708 23PDE7pp_reverse_3f150 j 709 d 0 1000 n j 709 23PDE9pp j 710 s 0 n j 710 23PDE9pp_reverse_84c1d j 711 d 0 1000 n j 711 26DAHtex j 712 d 0 1000 n j 712 26DAHtex_reverse_3953d j 713 d 0 1000 n j 713 2AGPA120tipp j 714 s 0 n j 714 2AGPA120tipp_reverse_16d01 j 715 d 0 1000 n j 715 2AGPA140tipp j 716 s 0 n j 716 2AGPA140tipp_reverse_392f6 j 717 d 0 1000 n j 717 2AGPA141tipp j 718 s 0 n j 718 2AGPA141tipp_reverse_f9714 j 719 d 0 1000 n j 719 2AGPA160tipp j 720 s 0 n j 720 2AGPA160tipp_reverse_dc28a j 721 d 0 1000 n j 721 2AGPA161tipp j 722 s 0 n j 722 2AGPA161tipp_reverse_fa4fb j 723 d 0 1000 n j 723 2AGPA180tipp j 724 s 0 n j 724 2AGPA180tipp_reverse_b48ac j 725 d 0 1000 n j 725 2AGPA181tipp j 726 s 0 n j 726 2AGPA181tipp_reverse_3605a j 727 d 0 1000 n j 727 2AGPE120tipp j 728 s 0 n j 728 2AGPE120tipp_reverse_015fb j 729 d 0 1000 n j 729 2AGPE140tipp j 730 s 0 n j 730 2AGPE140tipp_reverse_d6097 j 731 d 0 1000 n j 731 2AGPE141tipp j 732 s 0 n j 732 2AGPE141tipp_reverse_ae862 j 733 d 0 1000 n j 733 2AGPE160tipp j 734 s 0 n j 734 2AGPE160tipp_reverse_2f53f j 735 d 0 1000 n j 735 2AGPE161tipp j 736 s 0 n j 736 2AGPE161tipp_reverse_39d2b j 737 d 0 1000 n j 737 2AGPE180tipp j 738 s 0 n j 738 2AGPE180tipp_reverse_242d6 j 739 d 0 1000 n j 739 2AGPE181tipp j 740 s 0 n j 740 2AGPE181tipp_reverse_a5fc9 j 741 d 0 1000 n j 741 2AGPEAT120 j 742 s 0 n j 742 2AGPEAT120_reverse_cbf7b j 743 d 0 1000 n j 743 2AGPEAT140 j 744 s 0 n j 744 2AGPEAT140_reverse_195ed j 745 d 0 1000 n j 745 2AGPEAT141 j 746 s 0 n j 746 2AGPEAT141_reverse_0e3c9 j 747 d 0 1000 n j 747 2AGPEAT160 j 748 s 0 n j 748 2AGPEAT160_reverse_16239 j 749 d 0 1000 n j 749 2AGPEAT161 j 750 s 0 n j 750 2AGPEAT161_reverse_5858e j 751 d 0 1000 n j 751 2AGPEAT180 j 752 s 0 n j 752 2AGPEAT180_reverse_47e42 j 753 d 0 1000 n j 753 2AGPEAT181 j 754 s 0 n j 754 2AGPEAT181_reverse_450ec j 755 d 0 1000 n j 755 2AGPG120tipp j 756 s 0 n j 756 2AGPG120tipp_reverse_16620 j 757 d 0 1000 n j 757 2AGPG140tipp j 758 s 0 n j 758 2AGPG140tipp_reverse_2834e j 759 d 0 1000 n j 759 2AGPG141tipp j 760 s 0 n j 760 2AGPG141tipp_reverse_44e94 j 761 d 0 1000 n j 761 2AGPG160tipp j 762 s 0 n j 762 2AGPG160tipp_reverse_a1e99 j 763 d 0 1000 n j 763 2AGPG161tipp j 764 s 0 n j 764 2AGPG161tipp_reverse_4c821 j 765 d 0 1000 n j 765 2AGPG180tipp j 766 s 0 n j 766 2AGPG180tipp_reverse_148cb j 767 d 0 1000 n j 767 2AGPG181tipp j 768 s 0 n j 768 2AGPG181tipp_reverse_7596e j 769 d 0 1000 n j 769 2AGPGAT120 j 770 s 0 n j 770 2AGPGAT120_reverse_83703 j 771 d 0 1000 n j 771 2AGPGAT140 j 772 s 0 n j 772 2AGPGAT140_reverse_94092 j 773 d 0 1000 n j 773 2AGPGAT141 j 774 s 0 n j 774 2AGPGAT141_reverse_82d91 j 775 d 0 1000 n j 775 2AGPGAT160 j 776 s 0 n j 776 2AGPGAT160_reverse_2888d j 777 d 0 1000 n j 777 2AGPGAT161 j 778 s 0 n j 778 2AGPGAT161_reverse_529fb j 779 d 0 1000 n j 779 2AGPGAT180 j 780 s 0 n j 780 2AGPGAT180_reverse_9d3e5 j 781 d 0 1000 n j 781 2AGPGAT181 j 782 s 0 n j 782 2AGPGAT181_reverse_802a9 j 783 d 0 1000 n j 783 2DGULRGx j 784 s 0 n j 784 2DGULRGx_reverse_a7f46 j 785 d 0 1000 n j 785 2DGULRGy j 786 s 0 n j 786 2DGULRGy_reverse_cbff4 j 787 d 0 1000 n j 787 2DGULRx j 788 s 0 n j 788 2DGULRx_reverse_d1a03 j 789 d 0 1000 n j 789 2DGULRy j 790 s 0 n j 790 2DGULRy_reverse_5d02d j 791 d 0 1000 n j 791 2MAHMP j 792 s 0 n j 792 2MAHMP_reverse_e4fd2 j 793 d 0 1000 n j 793 34dhpactex j 794 d 0 1000 n j 794 34dhpactex_reverse_51d40 j 795 d 0 1000 n j 795 3AMACHYD j 796 s 0 n j 796 3AMACHYD_reverse_1adff j 797 d 0 1000 n j 797 3AMPtex j 798 d 0 1000 n j 798 3AMPtex_reverse_62a3f j 799 d 0 1000 n j 799 3CMPtex j 800 d 0 1000 n j 800 3CMPtex_reverse_25d68 j 801 d 0 1000 n j 801 3GMPtex j 802 d 0 1000 n j 802 3GMPtex_reverse_c1214 j 803 d 0 1000 n j 803 3HAD100 j 804 s 0 n j 804 3HAD100_reverse_ca00f j 805 d 0 1000 n j 805 3HAD120 j 806 s 0 n j 806 3HAD120_reverse_82f09 j 807 d 0 1000 n j 807 3HAD121 j 808 s 0 n j 808 3HAD121_reverse_7ae0d j 809 d 0 1000 n j 809 3HAD140 j 810 s 0 n j 810 3HAD140_reverse_13958 j 811 d 0 1000 n j 811 3HAD141 j 812 s 0 n j 812 3HAD141_reverse_5738f j 813 d 0 1000 n j 813 3HAD160 j 814 s 0 n j 814 3HAD160_reverse_c0da0 j 815 d 0 1000 n j 815 3HAD161 j 816 s 0 n j 816 3HAD161_reverse_384de j 817 d 0 1000 n j 817 3HAD180 j 818 s 0 n j 818 3HAD180_reverse_c801d j 819 d 0 1000 n j 819 3HAD181 j 820 s 0 n j 820 3HAD181_reverse_63f21 j 821 d 0 1000 n j 821 3HAD40 j 822 s 0 n j 822 3HAD40_reverse_bc17f j 823 d 0 1000 n j 823 3HAD60 j 824 s 0 n j 824 3HAD60_reverse_d7fb5 j 825 d 0 1000 n j 825 3HAD80 j 826 s 0 n j 826 3HAD80_reverse_29891 j 827 d 0 1000 n j 827 3HCINNMH j 828 s 0 n j 828 3HCINNMH_reverse_e3e52 j 829 d 0 1000 n j 829 3HPPPNH j 830 s 0 n j 830 3HPPPNH_reverse_b005a j 831 d 0 1000 n j 831 3HPPtex j 832 d 0 1000 n j 832 3HPPtex_reverse_49e02 j 833 d 0 1000 n j 833 3HPPtpp j 834 s 0 n j 834 3HPPtpp_reverse_f463c j 835 d 0 1000 n j 835 3KGK j 836 s 0 n j 836 3KGK_reverse_d559b j 837 d 0 1000 n j 837 3NTD2pp j 838 s 0 n j 838 3NTD2pp_reverse_6ebac j 839 d 0 1000 n j 839 3NTD4pp j 840 s 0 n j 840 3NTD4pp_reverse_51bb5 j 841 d 0 1000 n j 841 3NTD7pp j 842 s 0 n j 842 3NTD7pp_reverse_9ab3e j 843 d 0 1000 n j 843 3NTD9pp j 844 s 0 n j 844 3NTD9pp_reverse_6286c j 845 d 0 1000 n j 845 3OAR100 j 846 d 0 1000 n j 846 3OAR100_reverse_d47e7 j 847 d 0 1000 n j 847 3OAR120 j 848 d 0 1000 n j 848 3OAR120_reverse_5a2b4 j 849 d 0 1000 n j 849 3OAR121 j 850 s 0 n j 850 3OAR121_reverse_82b84 j 851 d 0 1000 n j 851 3OAR140 j 852 d 0 1000 n j 852 3OAR140_reverse_4eb2a j 853 d 0 1000 n j 853 3OAR141 j 854 s 0 n j 854 3OAR141_reverse_755ec j 855 d 0 1000 n j 855 3OAR160 j 856 d 0 1000 n j 856 3OAR160_reverse_15066 j 857 d 0 1000 n j 857 3OAR161 j 858 s 0 n j 858 3OAR161_reverse_fc9b4 j 859 d 0 1000 n j 859 3OAR180 j 860 d 0 1000 n j 860 3OAR180_reverse_e234c j 861 d 0 1000 n j 861 3OAR181 j 862 s 0 n j 862 3OAR181_reverse_f8559 j 863 d 0 1000 n j 863 3OAR40 j 864 d 0 1000 n j 864 3OAR40_reverse_e075e j 865 d 0 1000 n j 865 3OAR60 j 866 d 0 1000 n j 866 3OAR60_reverse_40a5c j 867 d 0 1000 n j 867 3OAR80 j 868 d 0 1000 n j 868 3OAR80_reverse_fe338 j 869 d 0 1000 n j 869 3OAS100 j 870 s 0 n j 870 3OAS100_reverse_bfd7d j 871 d 0 1000 n j 871 3OAS120 j 872 s 0 n j 872 3OAS120_reverse_06658 j 873 d 0 1000 n j 873 3OAS121 j 874 s 0 n j 874 3OAS121_reverse_e6125 j 875 d 0 1000 n j 875 3OAS140 j 876 s 0 n j 876 3OAS140_reverse_65ddc j 877 d 0 1000 n j 877 3OAS141 j 878 s 0 n j 878 3OAS141_reverse_7f7b2 j 879 d 0 1000 n j 879 3OAS160 j 880 s 0 n j 880 3OAS160_reverse_08317 j 881 d 0 1000 n j 881 3OAS161 j 882 s 0 n j 882 3OAS161_reverse_ca056 j 883 d 0 1000 n j 883 3OAS180 j 884 s 0 n j 884 3OAS180_reverse_3d9f8 j 885 d 0 1000 n j 885 3OAS181 j 886 s 0 n j 886 3OAS181_reverse_2c21d j 887 d 0 1000 n j 887 3OAS60 j 888 s 0 n j 888 3OAS60_reverse_bff6a j 889 d 0 1000 n j 889 3OAS80 j 890 s 0 n j 890 3OAS80_reverse_c6446 j 891 d 0 1000 n j 891 3OXCOAT j 892 s 0 n j 892 3OXCOAT_reverse_2bb18 j 893 d 0 1000 n j 893 3PEPTabcpp j 894 s 0 n j 894 3PEPTabcpp_reverse_d34e9 j 895 d 0 1000 n j 895 3PEPTtex j 896 d 0 1000 n j 896 3PEPTtex_reverse_45a83 j 897 d 0 1000 n j 897 3UMPtex j 898 d 0 1000 n j 898 3UMPtex_reverse_c4def j 899 d 0 1000 n j 899 42A12BOOXpp j 900 s 0 n j 900 42A12BOOXpp_reverse_a8a8a j 901 d 0 1000 n j 901 4HOXPACDtex j 902 d 0 1000 n j 902 4HOXPACDtex_reverse_3798d j 903 d 0 1000 n j 903 4HTHRS j 904 s 0 n j 904 4HTHRS_reverse_6e403 j 905 d 0 1000 n j 905 4PCP j 906 s 0 n j 906 4PCP_reverse_c7c51 j 907 d 0 1000 n j 907 4PCPpp j 908 s 0 n j 908 4PCPpp_reverse_87e32 j 909 d 0 1000 n j 909 4PEPTabcpp j 910 s 0 n j 910 4PEPTabcpp_reverse_c63ab j 911 d 0 1000 n j 911 4PEPTtex j 912 d 0 1000 n j 912 4PEPTtex_reverse_025c0 j 913 d 0 1000 n j 913 5DGLCNR j 914 d 0 1000 n j 914 5DGLCNR_reverse_fbc12 j 915 d 0 1000 n j 915 5DGLCNt2rpp j 916 d 0 1000 n j 916 5DGLCNt2rpp_reverse_32e75 j 917 d 0 1000 n j 917 5DGLCNtex j 918 d 0 1000 n j 918 5DGLCNtex_reverse_d721b j 919 d 0 1000 n j 919 5DOAN j 920 s 0 n j 920 5DOAN_reverse_c8e7d j 921 d 0 1000 n j 921 5MTRtex j 922 d 0 1000 n j 922 5MTRtex_reverse_79096 j 923 d 0 1000 n j 923 5MTRtpp j 924 s 0 n j 924 5MTRtpp_reverse_d6113 j 925 d 0 1000 n j 925 A5PISO j 926 d 0 1000 n j 926 A5PISO_reverse_3adc0 j 927 d 0 1000 n j 927 AACPS1 j 928 s 0 n j 928 AACPS1_reverse_74228 j 929 d 0 1000 n j 929 AACPS2 j 930 s 0 n j 930 AACPS2_reverse_4df8a j 931 d 0 1000 n j 931 AACPS3 j 932 s 0 n j 932 AACPS3_reverse_de1e6 j 933 d 0 1000 n j 933 AACPS4 j 934 s 0 n j 934 AACPS4_reverse_2b927 j 935 d 0 1000 n j 935 AACPS5 j 936 s 0 n j 936 AACPS5_reverse_2077f j 937 d 0 1000 n j 937 AACPS6 j 938 s 0 n j 938 AACPS6_reverse_8fda3 j 939 d 0 1000 n j 939 AACPS7 j 940 s 0 n j 940 AACPS7_reverse_c7dd9 j 941 d 0 1000 n j 941 AACPS8 j 942 s 0 n j 942 AACPS8_reverse_375dc j 943 d 0 1000 n j 943 AACPS9 j 944 s 0 n j 944 AACPS9_reverse_c03ad j 945 d 0 1000 n j 945 AACTOOR j 946 s 0 n j 946 AACTOOR_reverse_e897c j 947 d 0 1000 n j 947 AADDGT j 948 s 0 n j 948 AADDGT_reverse_01165 j 949 d 0 1000 n j 949 AAMYL j 950 s 0 n j 950 AAMYL_reverse_58557 j 951 d 0 1000 n j 951 AAMYLpp j 952 s 0 n j 952 AAMYLpp_reverse_813aa j 953 d 0 1000 n j 953 AB6PGH j 954 s 0 n j 954 AB6PGH_reverse_9c1a3 j 955 d 0 1000 n j 955 ABTA j 956 s 0 n j 956 ABTA_reverse_48ba6 j 957 d 0 1000 n j 957 ABUTD j 958 s 0 n j 958 ABUTD_reverse_a69d2 j 959 d 0 1000 n j 959 ABUTt2pp j 960 s 0 n j 960 ABUTt2pp_reverse_b7c2d j 961 d 0 1000 n j 961 ABUTtex j 962 d 0 1000 n j 962 ABUTtex_reverse_f1b1f j 963 d 0 1000 n j 963 ACACCT j 964 s 0 n j 964 ACACCT_reverse_94e1e j 965 d 0 1000 n j 965 ACACT1r j 966 d 0 1000 n j 966 ACACT1r_reverse_7e2ab j 967 d 0 1000 n j 967 ACACT2r j 968 d 0 1000 n j 968 ACACT2r_reverse_b794d j 969 d 0 1000 n j 969 ACACT3r j 970 d 0 1000 n j 970 ACACT3r_reverse_b8079 j 971 d 0 1000 n j 971 ACACT4r j 972 d 0 1000 n j 972 ACACT4r_reverse_36b94 j 973 d 0 1000 n j 973 ACACT5r j 974 d 0 1000 n j 974 ACACT5r_reverse_49fec j 975 d 0 1000 n j 975 ACACT6r j 976 d 0 1000 n j 976 ACACT6r_reverse_a3ce9 j 977 d 0 1000 n j 977 ACACT7r j 978 d 0 1000 n j 978 ACACT7r_reverse_b44b4 j 979 d 0 1000 n j 979 ACACT8r j 980 d 0 1000 n j 980 ACACT8r_reverse_54705 j 981 d 0 1000 n j 981 ACACt2pp j 982 d 0 1000 n j 982 ACACt2pp_reverse_06302 j 983 d 0 1000 n j 983 ACACtex j 984 d 0 1000 n j 984 ACACtex_reverse_cc949 j 985 d 0 1000 n j 985 ACALD j 986 d 0 1000 n j 986 ACALD_reverse_fda2b j 987 d 0 1000 n j 987 ACALDtex j 988 d 0 1000 n j 988 ACALDtex_reverse_75f7f j 989 d 0 1000 n j 989 ACALDtpp j 990 d 0 1000 n j 990 ACALDtpp_reverse_52ae6 j 991 d 0 1000 n j 991 ACANTHAT j 992 s 0 n j 992 ACANTHAT_reverse_9fe7b j 993 d 0 1000 n j 993 ACBIPGT j 994 s 0 n j 994 ACBIPGT_reverse_bcc45 j 995 d 0 1000 n j 995 ACCOAC j 996 s 0 n j 996 ACCOAC_reverse_9d1cd j 997 d 0 1000 n j 997 ACCOAL j 998 s 0 n j 998 ACCOAL_reverse_ea444 j 999 d 0 1000 n j 999 ACGAL1PPpp j 1000 s 0 n j 1000 ACGAL1PPpp_reverse_88026 j 1001 d 0 1000 n j 1001 ACGAL1Ptex j 1002 d 0 1000 n j 1002 ACGAL1Ptex_reverse_c2cd1 j 1003 d 0 1000 n j 1003 ACGALtex j 1004 d 0 1000 n j 1004 ACGALtex_reverse_54263 j 1005 d 0 1000 n j 1005 ACGAM1PPpp j 1006 s 0 n j 1006 ACGAM1PPpp_reverse_fbc31 j 1007 d 0 1000 n j 1007 ACGAM1Ptex j 1008 d 0 1000 n j 1008 ACGAM1Ptex_reverse_d95c0 j 1009 d 0 1000 n j 1009 ACGAMK j 1010 s 0 n j 1010 ACGAMK_reverse_1e7a8 j 1011 d 0 1000 n j 1011 ACGAMT j 1012 s 0 n j 1012 ACGAMT_reverse_2307a j 1013 d 0 1000 n j 1013 ACGAptspp j 1014 s 0 n j 1014 ACGAptspp_reverse_e1a6e j 1015 d 0 1000 n j 1015 ACGAtex j 1016 d 0 1000 n j 1016 ACGAtex_reverse_5f438 j 1017 d 0 1000 n j 1017 ACGK j 1018 s 0 n j 1018 ACGK_reverse_684be j 1019 d 0 1000 n j 1019 ACGS j 1020 s 0 n j 1020 ACGS_reverse_c8939 j 1021 d 0 1000 n j 1021 ACHBS j 1022 s 0 n j 1022 ACHBS_reverse_13e5f j 1023 d 0 1000 n j 1023 ACKr j 1024 d 0 1000 n j 1024 ACKr_reverse_b49c0 j 1025 d 0 1000 n j 1025 ACLS j 1026 s 0 n j 1026 ACLS_reverse_66503 j 1027 d 0 1000 n j 1027 ACM6PH j 1028 s 0 n j 1028 ACM6PH_reverse_cff51 j 1029 d 0 1000 n j 1029 ACMAMUT j 1030 s 0 n j 1030 ACMAMUT_reverse_cf71b j 1031 d 0 1000 n j 1031 ACMANAptspp j 1032 s 0 n j 1032 ACMANAptspp_reverse_2111b j 1033 d 0 1000 n j 1033 ACMANAtex j 1034 d 0 1000 n j 1034 ACMANAtex_reverse_024da j 1035 d 0 1000 n j 1035 ACMUMptspp j 1036 s 0 n j 1036 ACMUMptspp_reverse_a323d j 1037 d 0 1000 n j 1037 ACMUMtex j 1038 s 0 n j 1038 ACMUMtex_reverse_17681 j 1039 d 0 1000 n j 1039 ACNAMt2pp j 1040 s 0 n j 1040 ACNAMt2pp_reverse_c08e0 j 1041 d 0 1000 n j 1041 ACNAMtex j 1042 d 0 1000 n j 1042 ACNAMtex_reverse_b7b93 j 1043 d 0 1000 n j 1043 ACNML j 1044 s 0 n j 1044 ACNML_reverse_9634f j 1045 d 0 1000 n j 1045 ACOAD1f j 1046 d 0 1000 n j 1046 ACOAD1f_reverse_e656c j 1047 d 0 1000 n j 1047 ACOAD2f j 1048 d 0 1000 n j 1048 ACOAD2f_reverse_6e942 j 1049 d 0 1000 n j 1049 ACOAD3f j 1050 d 0 1000 n j 1050 ACOAD3f_reverse_ba3fe j 1051 d 0 1000 n j 1051 ACOAD4f j 1052 d 0 1000 n j 1052 ACOAD4f_reverse_4d6cc j 1053 d 0 1000 n j 1053 ACOAD5f j 1054 d 0 1000 n j 1054 ACOAD5f_reverse_2359c j 1055 d 0 1000 n j 1055 ACOAD6f j 1056 d 0 1000 n j 1056 ACOAD6f_reverse_11aee j 1057 d 0 1000 n j 1057 ACOAD7f j 1058 d 0 1000 n j 1058 ACOAD7f_reverse_16a6a j 1059 d 0 1000 n j 1059 ACOAD8f j 1060 d 0 1000 n j 1060 ACOAD8f_reverse_fb781 j 1061 d 0 1000 n j 1061 ACOATA j 1062 d 0 1000 n j 1062 ACOATA_reverse_8c02f j 1063 d 0 1000 n j 1063 ACODA j 1064 s 0 n j 1064 ACODA_reverse_504cc j 1065 d 0 1000 n j 1065 ACOLIPAabctex j 1066 s 0 n j 1066 ACOLIPAabctex_reverse_4e0f1 j 1067 d 0 1000 n j 1067 ACONIs j 1068 d 0 1000 n j 1068 ACONIs_reverse_3e324 j 1069 d 0 1000 n j 1069 ACONMT j 1070 s 0 n j 1070 ACONMT_reverse_5a6e2 j 1071 d 0 1000 n j 1071 ACONTa j 1072 d 0 1000 n j 1072 ACONTa_reverse_cad6d j 1073 d 0 1000 n j 1073 ACONTb j 1074 d 0 1000 n j 1074 ACONTb_reverse_e198a j 1075 d 0 1000 n j 1075 ACOTA j 1076 d 0 1000 n j 1076 ACOTA_reverse_c4379 j 1077 d 0 1000 n j 1077 ACPPAT120 j 1078 s 0 n j 1078 ACPPAT120_reverse_b878c j 1079 d 0 1000 n j 1079 ACPPAT140 j 1080 s 0 n j 1080 ACPPAT140_reverse_24730 j 1081 d 0 1000 n j 1081 ACPPAT141 j 1082 s 0 n j 1082 ACPPAT141_reverse_94594 j 1083 d 0 1000 n j 1083 ACPPAT160 j 1084 s 0 n j 1084 ACPPAT160_reverse_620e6 j 1085 d 0 1000 n j 1085 ACPPAT161 j 1086 s 0 n j 1086 ACPPAT161_reverse_0a33f j 1087 d 0 1000 n j 1087 ACPPAT180 j 1088 s 0 n j 1088 ACPPAT180_reverse_bf624 j 1089 d 0 1000 n j 1089 ACPPAT181 j 1090 s 0 n j 1090 ACPPAT181_reverse_ac461 j 1091 d 0 1000 n j 1091 ACPS1 j 1092 s 0 n j 1092 ACPS1_reverse_56be7 j 1093 d 0 1000 n j 1093 ACS j 1094 s 0 n j 1094 ACS_reverse_37635 j 1095 d 0 1000 n j 1095 ACSERtex j 1096 d 0 1000 n j 1096 ACSERtex_reverse_d18a5 j 1097 d 0 1000 n j 1097 ACSERtpp j 1098 s 0 n j 1098 ACSERtpp_reverse_e0a16 j 1099 d 0 1000 n j 1099 ACt2rpp j 1100 d 0 1000 n j 1100 ACt2rpp_reverse_213f1 j 1101 d 0 1000 n j 1101 ACt4pp j 1102 s 0 n j 1102 ACt4pp_reverse_a779a j 1103 d 0 1000 n j 1103 ACtex j 1104 d 0 1000 n j 1104 ACtex_reverse_c7bfd j 1105 d 0 1000 n j 1105 ADA j 1106 s 0 n j 1106 ADA_reverse_347cb j 1107 d 0 1000 n j 1107 ADCL j 1108 s 0 n j 1108 ADCL_reverse_0051f j 1109 d 0 1000 n j 1109 ADCS j 1110 s 0 n j 1110 ADCS_reverse_5303c j 1111 d 0 1000 n j 1111 ADD j 1112 s 0 n j 1112 ADD_reverse_9eeb5 j 1113 d 0 1000 n j 1113 ADEt2rpp j 1114 d 0 1000 n j 1114 ADEt2rpp_reverse_6f767 j 1115 d 0 1000 n j 1115 ADEtex j 1116 d 0 1000 n j 1116 ADEtex_reverse_56687 j 1117 d 0 1000 n j 1117 ADK1 j 1118 d 0 1000 n j 1118 ADK1_reverse_a6f90 j 1119 d 0 1000 n j 1119 ADK3 j 1120 d 0 1000 n j 1120 ADK3_reverse_6b5fb j 1121 d 0 1000 n j 1121 ADK4 j 1122 d 0 1000 n j 1122 ADK4_reverse_dfbdf j 1123 d 0 1000 n j 1123 ADMDC j 1124 s 0 n j 1124 ADMDC_reverse_e2782 j 1125 d 0 1000 n j 1125 ADNCYC j 1126 s 0 n j 1126 ADNCYC_reverse_013dc j 1127 d 0 1000 n j 1127 ADNK1 j 1128 s 0 n j 1128 ADNK1_reverse_fe466 j 1129 d 0 1000 n j 1129 ADNUC j 1130 s 0 n j 1130 ADNUC_reverse_7ef46 j 1131 d 0 1000 n j 1131 ADNt2pp_copy1 j 1132 s 0 n j 1132 ADNt2pp_copy1_reverse_8a606 j 1133 d 0 1000 n j 1133 ADNt2pp_copy2 j 1134 d 0 1000 n j 1134 ADNt2pp_copy2_reverse_8da4d j 1135 d 0 1000 n j 1135 ADNtex j 1136 d 0 1000 n j 1136 ADNtex_reverse_43080 j 1137 d 0 1000 n j 1137 ADOCBIK j 1138 s 0 n j 1138 ADOCBIK_reverse_50143 j 1139 d 0 1000 n j 1139 ADOCBLS j 1140 s 0 n j 1140 ADOCBLS_reverse_005a5 j 1141 d 0 1000 n j 1141 ADOCBLabcpp j 1142 s 0 n j 1142 ADOCBLabcpp_reverse_68dcd j 1143 d 0 1000 n j 1143 ADOCBLtonex j 1144 s 0 n j 1144 ADOCBLtonex_reverse_baebb j 1145 d 0 1000 n j 1145 ADPRDP j 1146 s 0 n j 1146 ADPRDP_reverse_6f5d7 j 1147 d 0 1000 n j 1147 ADPT j 1148 s 0 n j 1148 ADPT_reverse_567cf j 1149 d 0 1000 n j 1149 ADSK j 1150 s 0 n j 1150 ADSK_reverse_6806d j 1151 d 0 1000 n j 1151 ADSL1r j 1152 d 0 1000 n j 1152 ADSL1r_reverse_2ae14 j 1153 d 0 1000 n j 1153 ADSL2r j 1154 d 0 1000 n j 1154 ADSL2r_reverse_42348 j 1155 d 0 1000 n j 1155 ADSS j 1156 s 0 n j 1156 ADSS_reverse_c75bb j 1157 d 0 1000 n j 1157 AGDC j 1158 s 0 n j 1158 AGDC_reverse_2ab9d j 1159 d 0 1000 n j 1159 AGM3PA j 1160 s 0 n j 1160 AGM3PA_reverse_07960 j 1161 d 0 1000 n j 1161 AGM3PApp j 1162 s 0 n j 1162 AGM3PApp_reverse_74a9d j 1163 d 0 1000 n j 1163 AGM3PH j 1164 s 0 n j 1164 AGM3PH_reverse_3acde j 1165 d 0 1000 n j 1165 AGM3Pt2pp j 1166 s 0 n j 1166 AGM3Pt2pp_reverse_5e873 j 1167 d 0 1000 n j 1167 AGM4PA j 1168 s 0 n j 1168 AGM4PA_reverse_cc387 j 1169 d 0 1000 n j 1169 AGM4PApp j 1170 s 0 n j 1170 AGM4PApp_reverse_5ec54 j 1171 d 0 1000 n j 1171 AGM4PCP j 1172 s 0 n j 1172 AGM4PCP_reverse_4f872 j 1173 d 0 1000 n j 1173 AGM4PCPpp j 1174 s 0 n j 1174 AGM4PCPpp_reverse_26bad j 1175 d 0 1000 n j 1175 AGM4PH j 1176 s 0 n j 1176 AGM4PH_reverse_b09ff j 1177 d 0 1000 n j 1177 AGM4Pt2pp j 1178 s 0 n j 1178 AGM4Pt2pp_reverse_58aad j 1179 d 0 1000 n j 1179 AGMH j 1180 s 0 n j 1180 AGMH_reverse_d371c j 1181 d 0 1000 n j 1181 AGMHE j 1182 s 0 n j 1182 AGMHE_reverse_dfad6 j 1183 d 0 1000 n j 1183 AGMT j 1184 s 0 n j 1184 AGMT_reverse_1c107 j 1185 d 0 1000 n j 1185 AGMt2pp j 1186 s 0 n j 1186 AGMt2pp_reverse_23bf9 j 1187 d 0 1000 n j 1187 AGMtex j 1188 d 0 1000 n j 1188 AGMtex_reverse_969a4 j 1189 d 0 1000 n j 1189 AGPAT120 j 1190 s 0 n j 1190 AGPAT120_reverse_7811c j 1191 d 0 1000 n j 1191 AGPAT140 j 1192 s 0 n j 1192 AGPAT140_reverse_73ea4 j 1193 d 0 1000 n j 1193 AGPAT141 j 1194 s 0 n j 1194 AGPAT141_reverse_fd2b9 j 1195 d 0 1000 n j 1195 AGPAT160 j 1196 s 0 n j 1196 AGPAT160_reverse_22d12 j 1197 d 0 1000 n j 1197 AGPAT161 j 1198 s 0 n j 1198 AGPAT161_reverse_debc5 j 1199 d 0 1000 n j 1199 AGPAT180 j 1200 s 0 n j 1200 AGPAT180_reverse_57c04 j 1201 d 0 1000 n j 1201 AGPAT181 j 1202 s 0 n j 1202 AGPAT181_reverse_93f51 j 1203 d 0 1000 n j 1203 AGPR j 1204 d 0 1000 n j 1204 AGPR_reverse_5dce4 j 1205 d 0 1000 n j 1205 AGt3 j 1206 s 0 n j 1206 AGt3_reverse_00449 j 1207 d 0 1000 n j 1207 AHCYSNS j 1208 s 0 n j 1208 AHCYSNS_reverse_38446 j 1209 d 0 1000 n j 1209 AICART j 1210 d 0 1000 n j 1210 AICART_reverse_b7b59 j 1211 d 0 1000 n j 1211 AIRC2 j 1212 s 0 n j 1212 AIRC2_reverse_50d74 j 1213 d 0 1000 n j 1213 AIRC3 j 1214 d 0 1000 n j 1214 AIRC3_reverse_f015f j 1215 d 0 1000 n j 1215 AKGDH j 1216 s 0 n j 1216 AKGDH_reverse_08bdc j 1217 d 0 1000 n j 1217 AKGt2rpp j 1218 d 0 1000 n j 1218 AKGt2rpp_reverse_9046e j 1219 d 0 1000 n j 1219 AKGtex j 1220 d 0 1000 n j 1220 AKGtex_reverse_06c87 j 1221 d 0 1000 n j 1221 ALAALAD j 1222 s 0 n j 1222 ALAALAD_reverse_ddcdd j 1223 d 0 1000 n j 1223 ALAALAabcpp j 1224 s 0 n j 1224 ALAALAabcpp_reverse_75b27 j 1225 d 0 1000 n j 1225 ALAALAr j 1226 d 0 1000 n j 1226 ALAALAr_reverse_18faa j 1227 d 0 1000 n j 1227 ALAALAtex j 1228 d 0 1000 n j 1228 ALAALAtex_reverse_2ebb9 j 1229 d 0 1000 n j 1229 ALAGLUE j 1230 d 0 1000 n j 1230 ALAGLUE_reverse_83285 j 1231 d 0 1000 n j 1231 ALAR j 1232 d 0 1000 n j 1232 ALAR_reverse_77133 j 1233 d 0 1000 n j 1233 ALATA_D2 j 1234 s 0 n j 1234 ALATA_D2_reverse_13566 j 1235 d 0 1000 n j 1235 ALATA_L j 1236 d 0 1000 n j 1236 ALATA_L_reverse_e54ff j 1237 d 0 1000 n j 1237 ALATA_L2 j 1238 s 0 n j 1238 ALATA_L2_reverse_ef76c j 1239 d 0 1000 n j 1239 ALATRS j 1240 s 0 n j 1240 ALATRS_reverse_de5e9 j 1241 d 0 1000 n j 1241 ALAabcpp j 1242 s 0 n j 1242 ALAabcpp_reverse_90425 j 1243 d 0 1000 n j 1243 ALAt2pp_copy1 j 1244 s 0 n j 1244 ALAt2pp_copy1_reverse_54b2f j 1245 d 0 1000 n j 1245 ALAt2pp_copy2 j 1246 d 0 1000 n j 1246 ALAt2pp_copy2_reverse_fa7a2 j 1247 d 0 1000 n j 1247 ALAt4pp j 1248 s 0 n j 1248 ALAt4pp_reverse_d9615 j 1249 d 0 1000 n j 1249 ALAtex j 1250 d 0 1000 n j 1250 ALAtex_reverse_33163 j 1251 d 0 1000 n j 1251 ALCD19 j 1252 d 0 1000 n j 1252 ALCD19_reverse_d90b5 j 1253 d 0 1000 n j 1253 ALCD2x j 1254 d 0 1000 n j 1254 ALCD2x_reverse_5d107 j 1255 d 0 1000 n j 1255 ALDD19xr j 1256 d 0 1000 n j 1256 ALDD19xr_reverse_1b96d j 1257 d 0 1000 n j 1257 ALDD2x j 1258 s 0 n j 1258 ALDD2x_reverse_90781 j 1259 d 0 1000 n j 1259 ALDD2y j 1260 s 0 n j 1260 ALDD2y_reverse_03afb j 1261 d 0 1000 n j 1261 ALDD3y j 1262 s 0 n j 1262 ALDD3y_reverse_27133 j 1263 d 0 1000 n j 1263 ALDD4 j 1264 s 0 n j 1264 ALDD4_reverse_3aaf5 j 1265 d 0 1000 n j 1265 ALLK j 1266 s 0 n j 1266 ALLK_reverse_42c6c j 1267 d 0 1000 n j 1267 ALLPI j 1268 d 0 1000 n j 1268 ALLPI_reverse_0c720 j 1269 d 0 1000 n j 1269 ALLTAMH j 1270 s 0 n j 1270 ALLTAMH_reverse_4519b j 1271 d 0 1000 n j 1271 ALLTN j 1272 s 0 n j 1272 ALLTN_reverse_d7d9e j 1273 d 0 1000 n j 1273 ALLTNt2rpp j 1274 d 0 1000 n j 1274 ALLTNt2rpp_reverse_62e9a j 1275 d 0 1000 n j 1275 ALLTNtex j 1276 d 0 1000 n j 1276 ALLTNtex_reverse_f7ace j 1277 d 0 1000 n j 1277 ALLULPE j 1278 d 0 1000 n j 1278 ALLULPE_reverse_f154c j 1279 d 0 1000 n j 1279 ALLabcpp j 1280 s 0 n j 1280 ALLabcpp_reverse_fd443 j 1281 d 0 1000 n j 1281 ALLtex j 1282 d 0 1000 n j 1282 ALLtex_reverse_73b82 j 1283 d 0 1000 n j 1283 ALPATE160pp j 1284 s 0 n j 1284 ALPATE160pp_reverse_39e01 j 1285 d 0 1000 n j 1285 ALPATG160pp j 1286 s 0 n j 1286 ALPATG160pp_reverse_f7766 j 1287 d 0 1000 n j 1287 ALR2 j 1288 s 0 n j 1288 ALR2_reverse_10b0a j 1289 d 0 1000 n j 1289 ALR4x j 1290 s 0 n j 1290 ALR4x_reverse_17ebf j 1291 d 0 1000 n j 1291 ALTRH j 1292 s 0 n j 1292 ALTRH_reverse_fca7e j 1293 d 0 1000 n j 1293 AM3PA j 1294 s 0 n j 1294 AM3PA_reverse_086bd j 1295 d 0 1000 n j 1295 AM4PA j 1296 s 0 n j 1296 AM4PA_reverse_b660b j 1297 d 0 1000 n j 1297 AM4PCP j 1298 s 0 n j 1298 AM4PCP_reverse_9f67b j 1299 d 0 1000 n j 1299 AMALT1 j 1300 s 0 n j 1300 AMALT1_reverse_f685c j 1301 d 0 1000 n j 1301 AMALT2 j 1302 s 0 n j 1302 AMALT2_reverse_20c24 j 1303 d 0 1000 n j 1303 AMALT3 j 1304 s 0 n j 1304 AMALT3_reverse_f2bc2 j 1305 d 0 1000 n j 1305 AMALT4 j 1306 s 0 n j 1306 AMALT4_reverse_934fc j 1307 d 0 1000 n j 1307 AMANAPEr j 1308 d 0 1000 n j 1308 AMANAPEr_reverse_8310c j 1309 d 0 1000 n j 1309 AMANK j 1310 s 0 n j 1310 AMANK_reverse_8ea5e j 1311 d 0 1000 n j 1311 AMAOTr j 1312 d 0 1000 n j 1312 AMAOTr_reverse_a5426 j 1313 d 0 1000 n j 1313 AMMQLT8 j 1314 s 0 n j 1314 AMMQLT8_reverse_6da73 j 1315 d 0 1000 n j 1315 AMPMS2 j 1316 s 0 n j 1316 AMPMS2_reverse_56a45 j 1317 d 0 1000 n j 1317 AMPN j 1318 s 0 n j 1318 AMPN_reverse_1b917 j 1319 d 0 1000 n j 1319 AMPTASECG j 1320 s 0 n j 1320 AMPTASECG_reverse_11d5d j 1321 d 0 1000 n j 1321 AMPTASEPG j 1322 s 0 n j 1322 AMPTASEPG_reverse_1fe90 j 1323 d 0 1000 n j 1323 AMPtex j 1324 d 0 1000 n j 1324 AMPtex_reverse_bec2c j 1325 d 0 1000 n j 1325 ANHGMtex j 1326 d 0 1000 n j 1326 ANHGMtex_reverse_89969 j 1327 d 0 1000 n j 1327 ANHMK j 1328 s 0 n j 1328 ANHMK_reverse_f8dfd j 1329 d 0 1000 n j 1329 ANPRT j 1330 s 0 n j 1330 ANPRT_reverse_e2684 j 1331 d 0 1000 n j 1331 ANS j 1332 s 0 n j 1332 ANS_reverse_4e062 j 1333 d 0 1000 n j 1333 AOBUTDs j 1334 s 0 n j 1334 AOBUTDs_reverse_2ac3d j 1335 d 0 1000 n j 1335 AOXSr2 j 1336 s 0 n j 1336 AOXSr2_reverse_0c982 j 1337 d 0 1000 n j 1337 AP4AH j 1338 s 0 n j 1338 AP4AH_reverse_ccff7 j 1339 d 0 1000 n j 1339 AP4AS j 1340 s 0 n j 1340 AP4AS_reverse_5ff3e j 1341 d 0 1000 n j 1341 AP5AH j 1342 s 0 n j 1342 AP5AH_reverse_ec0fb j 1343 d 0 1000 n j 1343 APCS j 1344 s 0 n j 1344 APCS_reverse_d7e40 j 1345 d 0 1000 n j 1345 APG3PAT120 j 1346 s 0 n j 1346 APG3PAT120_reverse_36529 j 1347 d 0 1000 n j 1347 APG3PAT140 j 1348 s 0 n j 1348 APG3PAT140_reverse_0280f j 1349 d 0 1000 n j 1349 APG3PAT141 j 1350 s 0 n j 1350 APG3PAT141_reverse_f3ee9 j 1351 d 0 1000 n j 1351 APG3PAT160 j 1352 s 0 n j 1352 APG3PAT160_reverse_19c9f j 1353 d 0 1000 n j 1353 APG3PAT161 j 1354 s 0 n j 1354 APG3PAT161_reverse_a7b12 j 1355 d 0 1000 n j 1355 APG3PAT180 j 1356 s 0 n j 1356 APG3PAT180_reverse_279d3 j 1357 d 0 1000 n j 1357 APG3PAT181 j 1358 s 0 n j 1358 APG3PAT181_reverse_ba91b j 1359 d 0 1000 n j 1359 APH120 j 1360 s 0 n j 1360 APH120_reverse_0e75d j 1361 d 0 1000 n j 1361 APH140 j 1362 s 0 n j 1362 APH140_reverse_fdf10 j 1363 d 0 1000 n j 1363 APH141 j 1364 s 0 n j 1364 APH141_reverse_10b9f j 1365 d 0 1000 n j 1365 APH160 j 1366 s 0 n j 1366 APH160_reverse_868b2 j 1367 d 0 1000 n j 1367 APH161 j 1368 s 0 n j 1368 APH161_reverse_38db8 j 1369 d 0 1000 n j 1369 APH180 j 1370 s 0 n j 1370 APH180_reverse_00cc5 j 1371 d 0 1000 n j 1371 APH181 j 1372 s 0 n j 1372 APH181_reverse_b327d j 1373 d 0 1000 n j 1373 APPLDHr j 1374 d 0 1000 n j 1374 APPLDHr_reverse_3ac58 j 1375 d 0 1000 n j 1375 APRAUR j 1376 s 0 n j 1376 APRAUR_reverse_e674d j 1377 d 0 1000 n j 1377 ARAI j 1378 d 0 1000 n j 1378 ARAI_reverse_f1762 j 1379 d 0 1000 n j 1379 ARBTNR1 j 1380 s 0 n j 1380 ARBTNR1_reverse_05ba1 j 1381 d 0 1000 n j 1381 ARBTNR2 j 1382 s 0 n j 1382 ARBTNR2_reverse_68a05 j 1383 d 0 1000 n j 1383 ARBTNR3 j 1384 s 0 n j 1384 ARBTNR3_reverse_e8402 j 1385 d 0 1000 n j 1385 ARBTNabcpp j 1386 s 0 n j 1386 ARBTNabcpp_reverse_a90a7 j 1387 d 0 1000 n j 1387 ARBTNexs j 1388 s 0 n j 1388 ARBTNexs_reverse_da1bb j 1389 d 0 1000 n j 1389 ARBTNtex j 1390 s 0 n j 1390 ARBTNtex_reverse_98dad j 1391 d 0 1000 n j 1391 ARBTNtonex j 1392 s 0 n j 1392 ARBTNtonex_reverse_292f7 j 1393 d 0 1000 n j 1393 ARBTNtpp j 1394 s 0 n j 1394 ARBTNtpp_reverse_6a35a j 1395 d 0 1000 n j 1395 ARBTptspp j 1396 s 0 n j 1396 ARBTptspp_reverse_7fd6c j 1397 d 0 1000 n j 1397 ARBTtex j 1398 s 0 n j 1398 ARBTtex_reverse_6822c j 1399 d 0 1000 n j 1399 ARBabcpp j 1400 s 0 n j 1400 ARBabcpp_reverse_ae03e j 1401 d 0 1000 n j 1401 ARBt2rpp j 1402 d 0 1000 n j 1402 ARBt2rpp_reverse_7d924 j 1403 d 0 1000 n j 1403 ARBt3ipp j 1404 s 0 n j 1404 ARBt3ipp_reverse_7c622 j 1405 d 0 1000 n j 1405 ARBtex j 1406 d 0 1000 n j 1406 ARBtex_reverse_2c0f8 j 1407 d 0 1000 n j 1407 ARGAGMt7pp j 1408 d 0 1000 n j 1408 ARGAGMt7pp_reverse_135dc j 1409 d 0 1000 n j 1409 ARGDC j 1410 s 0 n j 1410 ARGDC_reverse_08faf j 1411 d 0 1000 n j 1411 ARGDCpp j 1412 s 0 n j 1412 ARGDCpp_reverse_54b23 j 1413 d 0 1000 n j 1413 ARGORNt7pp j 1414 d 0 1000 n j 1414 ARGORNt7pp_reverse_eabec j 1415 d 0 1000 n j 1415 ARGSL j 1416 d 0 1000 n j 1416 ARGSL_reverse_1b949 j 1417 d 0 1000 n j 1417 ARGSS j 1418 s 0 n j 1418 ARGSS_reverse_5760d j 1419 d 0 1000 n j 1419 ARGTRS j 1420 s 0 n j 1420 ARGTRS_reverse_1ecbf j 1421 d 0 1000 n j 1421 ARGabcpp j 1422 s 0 n j 1422 ARGabcpp_reverse_2f37a j 1423 d 0 1000 n j 1423 ARGt3pp j 1424 s 0 n j 1424 ARGt3pp_reverse_a8971 j 1425 d 0 1000 n j 1425 ARGtex j 1426 d 0 1000 n j 1426 ARGtex_reverse_244d5 j 1427 d 0 1000 n j 1427 ASAD j 1428 d 0 1000 n j 1428 ASAD_reverse_39a64 j 1429 d 0 1000 n j 1429 ASCBPL j 1430 s 0 n j 1430 ASCBPL_reverse_f9eb9 j 1431 d 0 1000 n j 1431 ASCBptspp j 1432 s 0 n j 1432 ASCBptspp_reverse_99732 j 1433 d 0 1000 n j 1433 ASCBtex j 1434 d 0 1000 n j 1434 ASCBtex_reverse_b49d4 j 1435 d 0 1000 n j 1435 ASNN j 1436 s 0 n j 1436 ASNN_reverse_515f8 j 1437 d 0 1000 n j 1437 ASNNpp j 1438 s 0 n j 1438 ASNNpp_reverse_dd54e j 1439 d 0 1000 n j 1439 ASNS1 j 1440 s 0 n j 1440 ASNS1_reverse_90309 j 1441 d 0 1000 n j 1441 ASNS2 j 1442 s 0 n j 1442 ASNS2_reverse_85dd4 j 1443 d 0 1000 n j 1443 ASNTRS j 1444 s 0 n j 1444 ASNTRS_reverse_ee3aa j 1445 d 0 1000 n j 1445 ASNabcpp j 1446 s 0 n j 1446 ASNabcpp_reverse_47353 j 1447 d 0 1000 n j 1447 ASNt2rpp j 1448 d 0 1000 n j 1448 ASNt2rpp_reverse_144ff j 1449 d 0 1000 n j 1449 ASNtex j 1450 d 0 1000 n j 1450 ASNtex_reverse_e8ab8 j 1451 d 0 1000 n j 1451 ASO3t8pp j 1452 s 0 n j 1452 ASO3t8pp_reverse_9efd4 j 1453 d 0 1000 n j 1453 ASO3tex j 1454 d 0 1000 n j 1454 ASO3tex_reverse_eeec8 j 1455 d 0 1000 n j 1455 ASP1DC j 1456 s 0 n j 1456 ASP1DC_reverse_5dad1 j 1457 d 0 1000 n j 1457 ASPCT j 1458 s 0 n j 1458 ASPCT_reverse_c18b9 j 1459 d 0 1000 n j 1459 ASPK j 1460 d 0 1000 n j 1460 ASPK_reverse_115d7 j 1461 d 0 1000 n j 1461 ASPO3 j 1462 s 0 n j 1462 ASPO3_reverse_594c1 j 1463 d 0 1000 n j 1463 ASPO4 j 1464 s 0 n j 1464 ASPO4_reverse_aacc5 j 1465 d 0 1000 n j 1465 ASPO5 j 1466 s 0 n j 1466 ASPO5_reverse_4d759 j 1467 d 0 1000 n j 1467 ASPO6 j 1468 s 0 n j 1468 ASPO6_reverse_ec15c j 1469 d 0 1000 n j 1469 ASPT j 1470 s 0 n j 1470 ASPT_reverse_c6d74 j 1471 d 0 1000 n j 1471 ASPTA j 1472 d 0 1000 n j 1472 ASPTA_reverse_36525 j 1473 d 0 1000 n j 1473 ASPTRS j 1474 s 0 n j 1474 ASPTRS_reverse_8f6e6 j 1475 d 0 1000 n j 1475 ASPabcpp j 1476 s 0 n j 1476 ASPabcpp_reverse_faa73 j 1477 d 0 1000 n j 1477 ASPt2_2pp j 1478 s 0 n j 1478 ASPt2_2pp_reverse_990a0 j 1479 d 0 1000 n j 1479 ASPt2_3pp j 1480 s 0 n j 1480 ASPt2_3pp_reverse_e453b j 1481 d 0 1000 n j 1481 ASPt2pp_copy1 j 1482 s 0 n j 1482 ASPt2pp_copy1_reverse_6d689 j 1483 d 0 1000 n j 1483 ASPt2pp_copy2 j 1484 d 0 1000 n j 1484 ASPt2pp_copy2_reverse_6ec49 j 1485 d 0 1000 n j 1485 ASPtex j 1486 d 0 1000 n j 1486 ASPtex_reverse_35e4c j 1487 d 0 1000 n j 1487 ASR j 1488 s 0 n j 1488 ASR_reverse_1a3cf j 1489 d 0 1000 n j 1489 AST j 1490 s 0 n j 1490 AST_reverse_c77bb j 1491 d 0 1000 n j 1491 ATHRDHr j 1492 d 0 1000 n j 1492 ATHRDHr_reverse_f7ea2 j 1493 d 0 1000 n j 1493 ATPHs j 1494 s 0 n j 1494 ATPHs_reverse_ad499 j 1495 d 3.15 1000 n j 1495 ATPM j 1496 s 0 n j 1496 ATPM_reverse_5b752 j 1497 d 0 1000 n j 1497 ATPPRT j 1498 s 0 n j 1498 ATPPRT_reverse_00060 j 1499 d 0 1000 n j 1499 ATPS4rpp j 1500 d 0 1000 n j 1500 ATPS4rpp_reverse_18786 j 1501 d 0 1000 n j 1501 BALAt2pp j 1502 s 0 n j 1502 BALAt2pp_reverse_e7f95 j 1503 d 0 1000 n j 1503 BALAtex j 1504 d 0 1000 n j 1504 BALAtex_reverse_10000 j 1505 d 0 1000 n j 1505 BETALDHx j 1506 s 0 n j 1506 BETALDHx_reverse_30760 j 1507 d 0 1000 n j 1507 BETALDHy j 1508 s 0 n j 1508 BETALDHy_reverse_a4dbc j 1509 d 0 1000 n j 1509 BMOCOS j 1510 s 0 n j 1510 BMOCOS_reverse_a8c6b j 1511 d 0 1000 n j 1511 BMOGDS1 j 1512 s 0 n j 1512 BMOGDS1_reverse_83047 j 1513 d 0 1000 n j 1513 BMOGDS2 j 1514 s 0 n j 1514 BMOGDS2_reverse_1d2b7 j 1515 d 0 1000 n j 1515 BPNT j 1516 s 0 n j 1516 BPNT_reverse_53108 j 1517 d 0 1000 n j 1517 BSORx j 1518 s 0 n j 1518 BSORx_reverse_2339f j 1519 d 0 1000 n j 1519 BSORy j 1520 s 0 n j 1520 BSORy_reverse_89c33 j 1521 d 0 1000 n j 1521 BTNt2ipp j 1522 s 0 n j 1522 BTNt2ipp_reverse_72ae5 j 1523 d 0 1000 n j 1523 BTNtex j 1524 d 0 1000 n j 1524 BTNtex_reverse_cd491 j 1525 d 0 1000 n j 1525 BTS5 j 1526 s 0 n j 1526 BTS5_reverse_459c1 j 1527 d 0 1000 n j 1527 BUTCT j 1528 s 0 n j 1528 BUTCT_reverse_64a8b j 1529 d 0 1000 n j 1529 BUTSO3abcpp j 1530 s 0 n j 1530 BUTSO3abcpp_reverse_6dd1b j 1531 d 0 1000 n j 1531 BUTSO3tex j 1532 d 0 1000 n j 1532 BUTSO3tex_reverse_90916 j 1533 d 0 1000 n j 1533 BUTt2rpp j 1534 d 0 1000 n j 1534 BUTt2rpp_reverse_571fb j 1535 d 0 1000 n j 1535 BUTtex j 1536 d 0 1000 n j 1536 BUTtex_reverse_59ad6 j 1537 d 0 1000 n j 1537 BWCOGDS1 j 1538 s 0 n j 1538 BWCOGDS1_reverse_0fca6 j 1539 d 0 1000 n j 1539 BWCOGDS2 j 1540 s 0 n j 1540 BWCOGDS2_reverse_e74c3 j 1541 d 0 1000 n j 1541 BWCOS j 1542 s 0 n j 1542 BWCOS_reverse_cdbae j 1543 d 0 1000 n j 1543 CA2t3pp j 1544 s 0 n j 1544 CA2t3pp_reverse_0a9ad j 1545 d 0 1000 n j 1545 CA2tex j 1546 d 0 1000 n j 1546 CA2tex_reverse_27b69 j 1547 d 0 1000 n j 1547 CADVtpp j 1548 s 0 n j 1548 CADVtpp_reverse_9e7cc j 1549 s 0 n j 1549 CAT j 1550 s 0 n j 1550 CAT_reverse_c01ae j 1551 d 0 1000 n j 1551 CAt6pp j 1552 d 0 1000 n j 1552 CAt6pp_reverse_e05ae j 1553 d 0 1000 n j 1553 CBIAT j 1554 d 0 1000 n j 1554 CBIAT_reverse_1e649 j 1555 d 0 1000 n j 1555 CBItonex j 1556 s 0 n j 1556 CBItonex_reverse_bb4e5 j 1557 d 0 1000 n j 1557 CBIuabcpp j 1558 s 0 n j 1558 CBIuabcpp_reverse_ea68b j 1559 d 0 1000 n j 1559 CBL1abcpp j 1560 s 0 n j 1560 CBL1abcpp_reverse_18983 j 1561 d 0 1000 n j 1561 CBL1tonex j 1562 s 0 n j 1562 CBL1tonex_reverse_0c490 j 1563 d 0 1000 n j 1563 CBLAT j 1564 d 0 1000 n j 1564 CBLAT_reverse_0bf85 j 1565 d 0 1000 n j 1565 CBMD j 1566 s 0 n j 1566 CBMD_reverse_4f3e7 j 1567 d 0 1000 n j 1567 CBMKr j 1568 d 0 1000 n j 1568 CBMKr_reverse_fe98a j 1569 d 0 1000 n j 1569 CBPS j 1570 s 0 n j 1570 CBPS_reverse_80907 j 1571 d 0 1000 n j 1571 CCGS j 1572 s 0 n j 1572 CCGS_reverse_3ff79 j 1573 d 0 1000 n j 1573 CD2abcpp j 1574 s 0 n j 1574 CD2abcpp_reverse_d0330 j 1575 d 0 1000 n j 1575 CD2t3pp j 1576 s 0 n j 1576 CD2t3pp_reverse_47616 j 1577 d 0 1000 n j 1577 CD2tex j 1578 d 0 1000 n j 1578 CD2tex_reverse_400a6 j 1579 d 0 1000 n j 1579 CD2tpp j 1580 s 0 n j 1580 CD2tpp_reverse_04be1 j 1581 d 0 1000 n j 1581 CDAPPA120 j 1582 s 0 n j 1582 CDAPPA120_reverse_bf679 j 1583 d 0 1000 n j 1583 CDAPPA140 j 1584 s 0 n j 1584 CDAPPA140_reverse_00a04 j 1585 d 0 1000 n j 1585 CDAPPA141 j 1586 s 0 n j 1586 CDAPPA141_reverse_9da9e j 1587 d 0 1000 n j 1587 CDAPPA160 j 1588 s 0 n j 1588 CDAPPA160_reverse_e2daf j 1589 d 0 1000 n j 1589 CDAPPA161 j 1590 s 0 n j 1590 CDAPPA161_reverse_1276c j 1591 d 0 1000 n j 1591 CDAPPA180 j 1592 s 0 n j 1592 CDAPPA180_reverse_9cd52 j 1593 d 0 1000 n j 1593 CDAPPA181 j 1594 s 0 n j 1594 CDAPPA181_reverse_c0aa5 j 1595 d 0 1000 n j 1595 CDGR j 1596 s 0 n j 1596 CDGR_reverse_e4464 j 1597 d 0 1000 n j 1597 CDGS j 1598 s 0 n j 1598 CDGS_reverse_6b7cb j 1599 d 0 1000 n j 1599 CDPMEK j 1600 s 0 n j 1600 CDPMEK_reverse_01872 j 1601 d 0 1000 n j 1601 CFAS160E j 1602 s 0 n j 1602 CFAS160E_reverse_d8e06 j 1603 d 0 1000 n j 1603 CFAS160G j 1604 s 0 n j 1604 CFAS160G_reverse_ce748 j 1605 d 0 1000 n j 1605 CFAS180E j 1606 s 0 n j 1606 CFAS180E_reverse_6ab2c j 1607 d 0 1000 n j 1607 CFAS180G j 1608 s 0 n j 1608 CFAS180G_reverse_ab16a j 1609 d 0 1000 n j 1609 CGLYabcpp j 1610 s 0 n j 1610 CGLYabcpp_reverse_8e5ba j 1611 d 0 1000 n j 1611 CGLYtex j 1612 d 0 1000 n j 1612 CGLYtex_reverse_c0fb9 j 1613 d 0 1000 n j 1613 CHLabcpp j 1614 s 0 n j 1614 CHLabcpp_reverse_37887 j 1615 d 0 1000 n j 1615 CHLt2pp j 1616 s 0 n j 1616 CHLt2pp_reverse_5e5a5 j 1617 d 0 1000 n j 1617 CHLtex j 1618 d 0 1000 n j 1618 CHLtex_reverse_36af5 j 1619 d 0 1000 n j 1619 CHOLD j 1620 s 0 n j 1620 CHOLD_reverse_a176e j 1621 d 0 1000 n j 1621 CHORM j 1622 s 0 n j 1622 CHORM_reverse_38aac j 1623 d 0 1000 n j 1623 CHORS j 1624 s 0 n j 1624 CHORS_reverse_17772 j 1625 d 0 1000 n j 1625 CHRPL j 1626 s 0 n j 1626 CHRPL_reverse_46f50 j 1627 d 0 1000 n j 1627 CHTBSptspp j 1628 s 0 n j 1628 CHTBSptspp_reverse_c1fa2 j 1629 d 0 1000 n j 1629 CHTBStex j 1630 d 0 1000 n j 1630 CHTBStex_reverse_76a7e j 1631 d 0 1000 n j 1631 CINNDO j 1632 s 0 n j 1632 CINNDO_reverse_2153f j 1633 d 0 1000 n j 1633 CITL j 1634 s 0 n j 1634 CITL_reverse_4d27f j 1635 d 0 1000 n j 1635 CITt3pp j 1636 s 0 n j 1636 CITt3pp_reverse_08444 j 1637 d 0 1000 n j 1637 CITt7pp j 1638 s 0 n j 1638 CITt7pp_reverse_75ac8 j 1639 d 0 1000 n j 1639 CITtex j 1640 d 0 1000 n j 1640 CITtex_reverse_2ae27 j 1641 d 0 1000 n j 1641 CLIPAabctex j 1642 s 0 n j 1642 CLIPAabctex_reverse_fd06a j 1643 d 0 1000 n j 1643 CLPNH120pp j 1644 s 0 n j 1644 CLPNH120pp_reverse_463b7 j 1645 d 0 1000 n j 1645 CLPNH140pp j 1646 s 0 n j 1646 CLPNH140pp_reverse_200c3 j 1647 d 0 1000 n j 1647 CLPNH141pp j 1648 s 0 n j 1648 CLPNH141pp_reverse_c8b31 j 1649 d 0 1000 n j 1649 CLPNH160pp j 1650 s 0 n j 1650 CLPNH160pp_reverse_5a184 j 1651 d 0 1000 n j 1651 CLPNH161pp j 1652 s 0 n j 1652 CLPNH161pp_reverse_9f3a8 j 1653 d 0 1000 n j 1653 CLPNH180pp j 1654 s 0 n j 1654 CLPNH180pp_reverse_14f2a j 1655 d 0 1000 n j 1655 CLPNH181pp j 1656 s 0 n j 1656 CLPNH181pp_reverse_bcc62 j 1657 d 0 1000 n j 1657 CLPNS120pp j 1658 d 0 1000 n j 1658 CLPNS120pp_reverse_0e640 j 1659 d 0 1000 n j 1659 CLPNS140pp j 1660 d 0 1000 n j 1660 CLPNS140pp_reverse_3811c j 1661 d 0 1000 n j 1661 CLPNS141pp j 1662 d 0 1000 n j 1662 CLPNS141pp_reverse_2eb8f j 1663 d 0 1000 n j 1663 CLPNS160pp j 1664 d 0 1000 n j 1664 CLPNS160pp_reverse_2d06f j 1665 d 0 1000 n j 1665 CLPNS161pp j 1666 d 0 1000 n j 1666 CLPNS161pp_reverse_9806d j 1667 d 0 1000 n j 1667 CLPNS180pp j 1668 d 0 1000 n j 1668 CLPNS180pp_reverse_937c0 j 1669 d 0 1000 n j 1669 CLPNS181pp j 1670 d 0 1000 n j 1670 CLPNS181pp_reverse_92c7e j 1671 d 0 1000 n j 1671 CLt3_2pp j 1672 s 0 n j 1672 CLt3_2pp_reverse_e5246 j 1673 d 0 1000 n j 1673 CLtex j 1674 d 0 1000 n j 1674 CLtex_reverse_f6cf5 j 1675 d 0 1000 n j 1675 CMPN j 1676 s 0 n j 1676 CMPN_reverse_7ba04 j 1677 d 0 1000 n j 1677 CMPtex j 1678 d 0 1000 n j 1678 CMPtex_reverse_9db58 j 1679 d 0 1000 n j 1679 CMtex j 1680 d 0 1000 n j 1680 CMtex_reverse_de1ce j 1681 d 0 1000 n j 1681 CMtpp j 1682 s 0 n j 1682 CMtpp_reverse_be4c6 j 1683 d 0 1000 n j 1683 CO2tex j 1684 d 0 1000 n j 1684 CO2tex_reverse_3d081 j 1685 d 0 1000 n j 1685 CO2tpp j 1686 d 0 1000 n j 1686 CO2tpp_reverse_d9a27 j 1687 d 0 1000 n j 1687 COBALT2abcpp j 1688 s 0 n j 1688 COBALT2abcpp_reverse_76f3d j 1689 d 0 1000 n j 1689 COBALT2t3pp j 1690 s 0 n j 1690 COBALT2t3pp_reverse_70d7a j 1691 d 0 1000 n j 1691 COBALT2tex j 1692 d 0 1000 n j 1692 COBALT2tex_reverse_0862d j 1693 d 0 1000 n j 1693 COBALT2tpp j 1694 s 0 n j 1694 COBALT2tpp_reverse_077ed j 1695 d 0 1000 n j 1695 COLIPAKpp j 1696 s 0 n j 1696 COLIPAKpp_reverse_a30f0 j 1697 d 0 1000 n j 1697 COLIPAPabctex j 1698 s 0 n j 1698 COLIPAPabctex_reverse_e5b51 j 1699 d 0 1000 n j 1699 COLIPAabcpp j 1700 s 0 n j 1700 COLIPAabcpp_reverse_3d3cf j 1701 d 0 1000 n j 1701 COLIPAabctex j 1702 s 0 n j 1702 COLIPAabctex_reverse_39037 j 1703 d 0 1000 n j 1703 CPGNR1 j 1704 s 0 n j 1704 CPGNR1_reverse_e1a22 j 1705 d 0 1000 n j 1705 CPGNR2 j 1706 s 0 n j 1706 CPGNR2_reverse_276e7 j 1707 d 0 1000 n j 1707 CPGNR3 j 1708 s 0 n j 1708 CPGNR3_reverse_c6b90 j 1709 d 0 1000 n j 1709 CPGNUtex j 1710 s 0 n j 1710 CPGNUtex_reverse_66ff6 j 1711 d 0 1000 n j 1711 CPGNUtpp j 1712 s 0 n j 1712 CPGNUtpp_reverse_30f4e j 1713 d 0 1000 n j 1713 CPGNabcpp j 1714 s 0 n j 1714 CPGNabcpp_reverse_958fe j 1715 d 0 1000 n j 1715 CPGNexs j 1716 s 0 n j 1716 CPGNexs_reverse_93618 j 1717 d 0 1000 n j 1717 CPGNtonex j 1718 s 0 n j 1718 CPGNtonex_reverse_06ef2 j 1719 d 0 1000 n j 1719 CPH4S j 1720 s 0 n j 1720 CPH4S_reverse_542c3 j 1721 d 0 1000 n j 1721 CPMPS j 1722 s 0 n j 1722 CPMPS_reverse_2260b j 1723 d 0 1000 n j 1723 CPPPGO j 1724 s 0 n j 1724 CPPPGO_reverse_f858f j 1725 d 0 1000 n j 1725 CPPPGO2 j 1726 s 0 n j 1726 CPPPGO2_reverse_e5000 j 1727 d 0 1000 n j 1727 CRNBTCT j 1728 d 0 1000 n j 1728 CRNBTCT_reverse_0fcbb j 1729 d 0 1000 n j 1729 CRNCAL2 j 1730 s 0 n j 1730 CRNCAL2_reverse_800b4 j 1731 d 0 1000 n j 1731 CRNCAR j 1732 d 0 1000 n j 1732 CRNCAR_reverse_9f0cd j 1733 d 0 1000 n j 1733 CRNCBCT j 1734 d 0 1000 n j 1734 CRNCBCT_reverse_c4ebb j 1735 d 0 1000 n j 1735 CRNCDH j 1736 d 0 1000 n j 1736 CRNCDH_reverse_9743e j 1737 d 0 1000 n j 1737 CRNDCAL2 j 1738 s 0 n j 1738 CRNDCAL2_reverse_2dbe0 j 1739 d 0 1000 n j 1739 CRNDabcpp j 1740 s 0 n j 1740 CRNDabcpp_reverse_eaa22 j 1741 d 0 1000 n j 1741 CRNDt2rpp j 1742 d 0 1000 n j 1742 CRNDt2rpp_reverse_03da9 j 1743 d 0 1000 n j 1743 CRNDtex j 1744 d 0 1000 n j 1744 CRNDtex_reverse_29215 j 1745 d 0 1000 n j 1745 CRNabcpp j 1746 s 0 n j 1746 CRNabcpp_reverse_603cb j 1747 d 0 1000 n j 1747 CRNt2rpp j 1748 d 0 1000 n j 1748 CRNt2rpp_reverse_c7737 j 1749 d 0 1000 n j 1749 CRNt7pp j 1750 s 0 n j 1750 CRNt7pp_reverse_71e7f j 1751 d 0 1000 n j 1751 CRNt8pp j 1752 s 0 n j 1752 CRNt8pp_reverse_0e546 j 1753 d 0 1000 n j 1753 CRNtex j 1754 d 0 1000 n j 1754 CRNtex_reverse_34fd9 j 1755 d 0 1000 n j 1755 CS j 1756 s 0 n j 1756 CS_reverse_8d7e9 j 1757 d 0 1000 n j 1757 CSND j 1758 s 0 n j 1758 CSND_reverse_77bd2 j 1759 d 0 1000 n j 1759 CSNt2pp j 1760 s 0 n j 1760 CSNt2pp_reverse_0d959 j 1761 d 0 1000 n j 1761 CSNtex j 1762 d 0 1000 n j 1762 CSNtex_reverse_be886 j 1763 d 0 1000 n j 1763 CTBTCAL2 j 1764 s 0 n j 1764 CTBTCAL2_reverse_21850 j 1765 d 0 1000 n j 1765 CTBTabcpp j 1766 s 0 n j 1766 CTBTabcpp_reverse_299d5 j 1767 d 0 1000 n j 1767 CTBTt2rpp j 1768 d 0 1000 n j 1768 CTBTt2rpp_reverse_d330c j 1769 d 0 1000 n j 1769 CTECOAI6 j 1770 d 0 1000 n j 1770 CTECOAI6_reverse_de0db j 1771 d 0 1000 n j 1771 CTECOAI7 j 1772 d 0 1000 n j 1772 CTECOAI7_reverse_745a0 j 1773 d 0 1000 n j 1773 CTECOAI8 j 1774 d 0 1000 n j 1774 CTECOAI8_reverse_0323d j 1775 d 0 1000 n j 1775 CTPS2 j 1776 s 0 n j 1776 CTPS2_reverse_9c0ad j 1777 d 0 1000 n j 1777 CU1Opp j 1778 s 0 n j 1778 CU1Opp_reverse_50676 j 1779 d 0 1000 n j 1779 CU1abcpp j 1780 s 0 n j 1780 CU1abcpp_reverse_83c5f j 1781 d 0 1000 n j 1781 CU2abcpp j 1782 s 0 n j 1782 CU2abcpp_reverse_245f3 j 1783 d 0 1000 n j 1783 CU2tex j 1784 d 0 1000 n j 1784 CU2tex_reverse_521e6 j 1785 d 0 1000 n j 1785 CU2tpp j 1786 s 0 n j 1786 CU2tpp_reverse_630ab j 1787 d 0 1000 n j 1787 CUt3 j 1788 s 0 n j 1788 CUt3_reverse_036c0 j 1789 d 0 1000 n j 1789 CUtex j 1790 d 0 1000 n j 1790 CUtex_reverse_6d61f j 1791 d 0 1000 n j 1791 CYANST j 1792 s 0 n j 1792 CYANST_reverse_46415 j 1793 d 0 1000 n j 1793 CYANSTpp j 1794 s 0 n j 1794 CYANSTpp_reverse_8b1ae j 1795 d 0 1000 n j 1795 CYANtex j 1796 d 0 1000 n j 1796 CYANtex_reverse_b6a36 j 1797 d 0 1000 n j 1797 CYNTAH j 1798 s 0 n j 1798 CYNTAH_reverse_ca69d j 1799 d 0 1000 n j 1799 CYNTt2pp j 1800 s 0 n j 1800 CYNTt2pp_reverse_28968 j 1801 d 0 1000 n j 1801 CYNTtex j 1802 d 0 1000 n j 1802 CYNTtex_reverse_8c4ef j 1803 d 0 1000 n j 1803 CYSDDS j 1804 s 0 n j 1804 CYSDDS_reverse_f19f8 j 1805 d 0 1000 n j 1805 CYSDS j 1806 s 0 n j 1806 CYSDS_reverse_c49c8 j 1807 d 0 1000 n j 1807 CYSDabcpp j 1808 s 0 n j 1808 CYSDabcpp_reverse_07068 j 1809 d 0 1000 n j 1809 CYSDtex j 1810 d 0 1000 n j 1810 CYSDtex_reverse_184ff j 1811 d 0 1000 n j 1811 CYSS j 1812 s 0 n j 1812 CYSS_reverse_62727 j 1813 d 0 1000 n j 1813 CYSSADS j 1814 s 0 n j 1814 CYSSADS_reverse_c8340 j 1815 d 0 1000 n j 1815 CYSTL j 1816 s 0 n j 1816 CYSTL_reverse_b8b9a j 1817 d 0 1000 n j 1817 CYSTRS j 1818 s 0 n j 1818 CYSTRS_reverse_08992 j 1819 d 0 1000 n j 1819 CYSabc2pp j 1820 s 0 n j 1820 CYSabc2pp_reverse_285bf j 1821 d 0 1000 n j 1821 CYSabcpp j 1822 s 0 n j 1822 CYSabcpp_reverse_5f06e j 1823 d 0 1000 n j 1823 CYStex j 1824 d 0 1000 n j 1824 CYStex_reverse_00cf0 j 1825 d 0 1000 n j 1825 CYStpp j 1826 s 0 n j 1826 CYStpp_reverse_22bd9 j 1827 d 0 1000 n j 1827 CYTBD2pp j 1828 s 0 n j 1828 CYTBD2pp_reverse_d2eae j 1829 d 0 1000 n j 1829 CYTBDpp j 1830 s 0 n j 1830 CYTBDpp_reverse_79f50 j 1831 d 0 1000 n j 1831 CYTBO3_4pp j 1832 s 0 n j 1832 CYTBO3_4pp_reverse_4d2e1 j 1833 d 0 1000 n j 1833 CYTD j 1834 s 0 n j 1834 CYTD_reverse_256d9 j 1835 d 0 1000 n j 1835 CYTDH j 1836 s 0 n j 1836 CYTDH_reverse_bf08d j 1837 d 0 1000 n j 1837 CYTDK2 j 1838 s 0 n j 1838 CYTDK2_reverse_fd949 j 1839 d 0 1000 n j 1839 CYTDt2pp_copy1 j 1840 s 0 n j 1840 CYTDt2pp_copy1_reverse_43e26 j 1841 d 0 1000 n j 1841 CYTDt2pp_copy2 j 1842 d 0 1000 n j 1842 CYTDt2pp_copy2_reverse_4073e j 1843 d 0 1000 n j 1843 CYTDtex j 1844 d 0 1000 n j 1844 CYTDtex_reverse_b7852 j 1845 d 0 1000 n j 1845 CYTK1 j 1846 d 0 1000 n j 1846 CYTK1_reverse_2fa21 j 1847 d 0 1000 n j 1847 CYTK2 j 1848 d 0 1000 n j 1848 CYTK2_reverse_bee82 j 1849 d 0 1000 n j 1849 D_LACt2pp j 1850 d 0 1000 n j 1850 D_LACt2pp_reverse_bdd3b j 1851 d 0 1000 n j 1851 D_LACtex j 1852 d 0 1000 n j 1852 D_LACtex_reverse_4946c j 1853 d 0 1000 n j 1853 DAAD j 1854 s 0 n j 1854 DAAD_reverse_c84a4 j 1855 d 0 1000 n j 1855 DADA j 1856 s 0 n j 1856 DADA_reverse_ead02 j 1857 d 0 1000 n j 1857 DADK j 1858 d 0 1000 n j 1858 DADK_reverse_006ea j 1859 d 0 1000 n j 1859 DADNt2pp j 1860 s 0 n j 1860 DADNt2pp_reverse_fd695 j 1861 d 0 1000 n j 1861 DADNtex j 1862 d 0 1000 n j 1862 DADNtex_reverse_d7a49 j 1863 d 0 1000 n j 1863 DAGK120 j 1864 s 0 n j 1864 DAGK120_reverse_7cd00 j 1865 d 0 1000 n j 1865 DAGK140 j 1866 s 0 n j 1866 DAGK140_reverse_87f8f j 1867 d 0 1000 n j 1867 DAGK141 j 1868 s 0 n j 1868 DAGK141_reverse_f6e5f j 1869 d 0 1000 n j 1869 DAGK160 j 1870 s 0 n j 1870 DAGK160_reverse_0238d j 1871 d 0 1000 n j 1871 DAGK161 j 1872 s 0 n j 1872 DAGK161_reverse_9bfe7 j 1873 d 0 1000 n j 1873 DAGK180 j 1874 s 0 n j 1874 DAGK180_reverse_eb3e3 j 1875 d 0 1000 n j 1875 DAGK181 j 1876 s 0 n j 1876 DAGK181_reverse_8c0c8 j 1877 d 0 1000 n j 1877 DALAt2pp j 1878 s 0 n j 1878 DALAt2pp_reverse_2e5f8 j 1879 d 0 1000 n j 1879 DALAtex j 1880 d 0 1000 n j 1880 DALAtex_reverse_8fc1b j 1881 d 0 1000 n j 1881 DAMPtex j 1882 d 0 1000 n j 1882 DAMPtex_reverse_8dfbc j 1883 d 0 1000 n j 1883 DAPAL j 1884 s 0 n j 1884 DAPAL_reverse_14901 j 1885 d 0 1000 n j 1885 DAPDC j 1886 s 0 n j 1886 DAPDC_reverse_d3ab8 j 1887 d 0 1000 n j 1887 DAPE j 1888 d 0 1000 n j 1888 DAPE_reverse_e08be j 1889 d 0 1000 n j 1889 DAPabcpp j 1890 s 0 n j 1890 DAPabcpp_reverse_bfa35 j 1891 d 0 1000 n j 1891 DAPtex j 1892 d 0 1000 n j 1892 DAPtex_reverse_c7721 j 1893 d 0 1000 n j 1893 DASYN120 j 1894 s 0 n j 1894 DASYN120_reverse_769b1 j 1895 d 0 1000 n j 1895 DASYN140 j 1896 s 0 n j 1896 DASYN140_reverse_791b2 j 1897 d 0 1000 n j 1897 DASYN141 j 1898 s 0 n j 1898 DASYN141_reverse_0654c j 1899 d 0 1000 n j 1899 DASYN160 j 1900 s 0 n j 1900 DASYN160_reverse_c2bf4 j 1901 d 0 1000 n j 1901 DASYN161 j 1902 s 0 n j 1902 DASYN161_reverse_08434 j 1903 d 0 1000 n j 1903 DASYN180 j 1904 s 0 n j 1904 DASYN180_reverse_75973 j 1905 d 0 1000 n j 1905 DASYN181 j 1906 s 0 n j 1906 DASYN181_reverse_ebb48 j 1907 d 0 1000 n j 1907 DATPHs j 1908 s 0 n j 1908 DATPHs_reverse_3fb29 j 1909 d 0 1000 n j 1909 DB4PS j 1910 s 0 n j 1910 DB4PS_reverse_43dd1 j 1911 d 0 1000 n j 1911 DBTS j 1912 s 0 n j 1912 DBTS_reverse_b5da6 j 1913 d 0 1000 n j 1913 DC6PH j 1914 s 0 n j 1914 DC6PH_reverse_72b16 j 1915 d 0 1000 n j 1915 DCAtex j 1916 d 0 1000 n j 1916 DCAtex_reverse_5237b j 1917 d 0 1000 n j 1917 DCMPtex j 1918 d 0 1000 n j 1918 DCMPtex_reverse_6a30a j 1919 d 0 1000 n j 1919 DCTPD j 1920 s 0 n j 1920 DCTPD_reverse_a48d6 j 1921 d 0 1000 n j 1921 DCYTD j 1922 s 0 n j 1922 DCYTD_reverse_27b45 j 1923 d 0 1000 n j 1923 DCYTt2pp j 1924 s 0 n j 1924 DCYTt2pp_reverse_cbb22 j 1925 d 0 1000 n j 1925 DCYTtex j 1926 d 0 1000 n j 1926 DCYTtex_reverse_cc4b9 j 1927 d 0 1000 n j 1927 DDCAtexi j 1928 s 0 n j 1928 DDCAtexi_reverse_ea0a7 j 1929 d 0 1000 n j 1929 DDGALK j 1930 s 0 n j 1930 DDGALK_reverse_ee6c3 j 1931 d 0 1000 n j 1931 DDGLCNt2rpp j 1932 d 0 1000 n j 1932 DDGLCNt2rpp_reverse_1bf30 j 1933 d 0 1000 n j 1933 DDGLCNtex j 1934 d 0 1000 n j 1934 DDGLCNtex_reverse_cb7f7 j 1935 d 0 1000 n j 1935 DDGLK j 1936 s 0 n j 1936 DDGLK_reverse_9d6e1 j 1937 d 0 1000 n j 1937 DDPA j 1938 s 0 n j 1938 DDPA_reverse_575e8 j 1939 d 0 1000 n j 1939 DDPGALA j 1940 d 0 1000 n j 1940 DDPGALA_reverse_4e5af j 1941 d 0 1000 n j 1941 DGK1 j 1942 d 0 1000 n j 1942 DGK1_reverse_3266e j 1943 d 0 1000 n j 1943 DGMPtex j 1944 d 0 1000 n j 1944 DGMPtex_reverse_f8176 j 1945 d 0 1000 n j 1945 DGSNt2pp j 1946 s 0 n j 1946 DGSNt2pp_reverse_085fe j 1947 d 0 1000 n j 1947 DGSNtex j 1948 d 0 1000 n j 1948 DGSNtex_reverse_d8452 j 1949 d 0 1000 n j 1949 DHACOAH j 1950 d 0 1000 n j 1950 DHACOAH_reverse_1376f j 1951 d 0 1000 n j 1951 DHAD1 j 1952 s 0 n j 1952 DHAD1_reverse_39dca j 1953 d 0 1000 n j 1953 DHAD2 j 1954 s 0 n j 1954 DHAD2_reverse_755c6 j 1955 d 0 1000 n j 1955 DHAPT j 1956 s 0 n j 1956 DHAPT_reverse_62f68 j 1957 d 0 1000 n j 1957 DHAtex j 1958 d 0 1000 n j 1958 DHAtex_reverse_b3ad5 j 1959 d 0 1000 n j 1959 DHAtpp j 1960 d 0 1000 n j 1960 DHAtpp_reverse_cfe59 j 1961 d 0 1000 n j 1961 DHBD j 1962 d 0 1000 n j 1962 DHBD_reverse_07e1f j 1963 d 0 1000 n j 1963 DHBS j 1964 s 0 n j 1964 DHBS_reverse_e570e j 1965 d 0 1000 n j 1965 DHBSH j 1966 s 0 n j 1966 DHBSH_reverse_e4369 j 1967 d 0 1000 n j 1967 DHCIND j 1968 s 0 n j 1968 DHCIND_reverse_1bec4 j 1969 d 0 1000 n j 1969 DHCINDO j 1970 s 0 n j 1970 DHCINDO_reverse_12b57 j 1971 d 0 1000 n j 1971 DHDPRy j 1972 s 0 n j 1972 DHDPRy_reverse_8346a j 1973 d 0 1000 n j 1973 DHDPS j 1974 s 0 n j 1974 DHDPS_reverse_e10c0 j 1975 d 0 1000 n j 1975 DHFR j 1976 d 0 1000 n j 1976 DHFR_reverse_65c32 j 1977 d 0 1000 n j 1977 DHFS j 1978 s 0 n j 1978 DHFS_reverse_f7920 j 1979 d 0 1000 n j 1979 DHMPTR j 1980 s 0 n j 1980 DHMPTR_reverse_90b26 j 1981 d 0 1000 n j 1981 DHNAOT4 j 1982 s 0 n j 1982 DHNAOT4_reverse_16f9d j 1983 d 0 1000 n j 1983 DHNCOAS j 1984 s 0 n j 1984 DHNCOAS_reverse_af3a9 j 1985 d 0 1000 n j 1985 DHNCOAT j 1986 s 0 n j 1986 DHNCOAT_reverse_58c26 j 1987 d 0 1000 n j 1987 DHNPA2r j 1988 d 0 1000 n j 1988 DHNPA2r_reverse_475b3 j 1989 d 0 1000 n j 1989 DHNPTE j 1990 d 0 1000 n j 1990 DHNPTE_reverse_0732e j 1991 d 0 1000 n j 1991 DHORD2 j 1992 s 0 n j 1992 DHORD2_reverse_22a13 j 1993 d 0 1000 n j 1993 DHORD5 j 1994 s 0 n j 1994 DHORD5_reverse_e7a65 j 1995 d 0 1000 n j 1995 DHORDfum j 1996 s 0 n j 1996 DHORDfum_reverse_d4b11 j 1997 d 0 1000 n j 1997 DHORTS j 1998 d 0 1000 n j 1998 DHORTS_reverse_82d73 j 1999 d 0 1000 n j 1999 DHPPD j 2000 s 0 n j 2000 DHPPD_reverse_f0de8 j 2001 d 0 1000 n j 2001 DHPPDA2 j 2002 s 0 n j 2002 DHPPDA2_reverse_9e131 j 2003 d 0 1000 n j 2003 DHPS2 j 2004 s 0 n j 2004 DHPS2_reverse_8974a j 2005 d 0 1000 n j 2005 DHPTDCs2 j 2006 s 0 n j 2006 DHPTDCs2_reverse_26a0a j 2007 s 0 n j 2007 DHPTDNR j 2008 s 0 n j 2008 DHPTDNR_reverse_d97d7 j 2009 s 0 n j 2009 DHPTDNRN j 2010 s 0 n j 2010 DHPTDNRN_reverse_a94c7 j 2011 d 0 1000 n j 2011 DHPTPE j 2012 d 0 1000 n j 2012 DHPTPE_reverse_dd6d2 j 2013 d 0 1000 n j 2013 DHQS j 2014 s 0 n j 2014 DHQS_reverse_3d16b j 2015 d 0 1000 n j 2015 DHQTi j 2016 s 0 n j 2016 DHQTi_reverse_c4498 j 2017 d 0 1000 n j 2017 DIMPtex j 2018 d 0 1000 n j 2018 DIMPtex_reverse_2bafb j 2019 d 0 1000 n j 2019 DINSt2pp j 2020 s 0 n j 2020 DINSt2pp_reverse_da91c j 2021 d 0 1000 n j 2021 DINStex j 2022 d 0 1000 n j 2022 DINStex_reverse_e04be j 2023 d 0 1000 n j 2023 DKGLCNR1 j 2024 s 0 n j 2024 DKGLCNR1_reverse_5f829 j 2025 d 0 1000 n j 2025 DKGLCNR2x j 2026 s 0 n j 2026 DKGLCNR2x_reverse_1e5cd j 2027 d 0 1000 n j 2027 DKGLCNR2y j 2028 s 0 n j 2028 DKGLCNR2y_reverse_33a59 j 2029 d 0 1000 n j 2029 DMATT j 2030 s 0 n j 2030 DMATT_reverse_3a731 j 2031 d 0 1000 n j 2031 DMPPS j 2032 s 0 n j 2032 DMPPS_reverse_c6082 j 2033 d 0 1000 n j 2033 DMQMT j 2034 s 0 n j 2034 DMQMT_reverse_2490b j 2035 d 0 1000 n j 2035 DMSOR1 j 2036 s 0 n j 2036 DMSOR1_reverse_470fa j 2037 d 0 1000 n j 2037 DMSOR1pp j 2038 s 0 n j 2038 DMSOR1pp_reverse_8bb05 j 2039 d 0 1000 n j 2039 DMSOR2 j 2040 s 0 n j 2040 DMSOR2_reverse_001f6 j 2041 d 0 1000 n j 2041 DMSOR2pp j 2042 s 0 n j 2042 DMSOR2pp_reverse_b876b j 2043 d 0 1000 n j 2043 DMSOtex j 2044 d 0 1000 n j 2044 DMSOtex_reverse_aff1c j 2045 d 0 1000 n j 2045 DMSOtpp j 2046 d 0 1000 n j 2046 DMSOtpp_reverse_eddb5 j 2047 d 0 1000 n j 2047 DMStex j 2048 d 0 1000 n j 2048 DMStex_reverse_100fa j 2049 d 0 1000 n j 2049 DNMPPA j 2050 s 0 n j 2050 DNMPPA_reverse_131b7 j 2051 d 0 1000 n j 2051 DNTPPA j 2052 s 0 n j 2052 DNTPPA_reverse_7e624 j 2053 d 0 1000 n j 2053 DOGULNR j 2054 s 0 n j 2054 DOGULNR_reverse_c4196 j 2055 d 0 1000 n j 2055 DOPAtex j 2056 d 0 1000 n j 2056 DOPAtex_reverse_b578f j 2057 d 0 1000 n j 2057 DOXRBCNtex j 2058 d 0 1000 n j 2058 DOXRBCNtex_reverse_5ba24 j 2059 d 0 1000 n j 2059 DOXRBCNtpp j 2060 s 0 n j 2060 DOXRBCNtpp_reverse_88f4a j 2061 d 0 1000 n j 2061 DPCOAK j 2062 s 0 n j 2062 DPCOAK_reverse_56ab9 j 2063 d 0 1000 n j 2063 DPR j 2064 s 0 n j 2064 DPR_reverse_691d8 j 2065 d 0 1000 n j 2065 DRPA j 2066 s 0 n j 2066 DRPA_reverse_66bfb j 2067 d 0 1000 n j 2067 DSBAO1 j 2068 s 0 n j 2068 DSBAO1_reverse_9c7d6 j 2069 d 0 1000 n j 2069 DSBAO2 j 2070 s 0 n j 2070 DSBAO2_reverse_f2c04 j 2071 d 0 1000 n j 2071 DSBCGT j 2072 s 0 n j 2072 DSBCGT_reverse_58858 j 2073 d 0 1000 n j 2073 DSBDR j 2074 s 0 n j 2074 DSBDR_reverse_7e26e j 2075 d 0 1000 n j 2075 DSBGGT j 2076 s 0 n j 2076 DSBGGT_reverse_f8e4c j 2077 d 0 1000 n j 2077 DSERDHr j 2078 d 0 1000 n j 2078 DSERDHr_reverse_c44ee j 2079 d 0 1000 n j 2079 DSERt2pp j 2080 s 0 n j 2080 DSERt2pp_reverse_d3685 j 2081 d 0 1000 n j 2081 DSERtex j 2082 d 0 1000 n j 2082 DSERtex_reverse_5ab31 j 2083 d 0 1000 n j 2083 DTARTD j 2084 s 0 n j 2084 DTARTD_reverse_0d68b j 2085 d 0 1000 n j 2085 DTMPK j 2086 d 0 1000 n j 2086 DTMPK_reverse_44d5a j 2087 d 0 1000 n j 2087 DTMPtex j 2088 d 0 1000 n j 2088 DTMPtex_reverse_581e0 j 2089 d 0 1000 n j 2089 DUMPtex j 2090 d 0 1000 n j 2090 DUMPtex_reverse_a35ee j 2091 d 0 1000 n j 2091 DURADx j 2092 d 0 1000 n j 2092 DURADx_reverse_224c5 j 2093 d 0 1000 n j 2093 DURIK1 j 2094 s 0 n j 2094 DURIK1_reverse_f4aee j 2095 d 0 1000 n j 2095 DURIPP j 2096 d 0 1000 n j 2096 DURIPP_reverse_e8f8a j 2097 d 0 1000 n j 2097 DURIt2pp j 2098 s 0 n j 2098 DURIt2pp_reverse_a9066 j 2099 d 0 1000 n j 2099 DURItex j 2100 d 0 1000 n j 2100 DURItex_reverse_bdbc1 j 2101 d 0 1000 n j 2101 DUTPDP j 2102 s 0 n j 2102 DUTPDP_reverse_1eccd j 2103 d 0 1000 n j 2103 DXPRIi j 2104 s 0 n j 2104 DXPRIi_reverse_85956 j 2105 d 0 1000 n j 2105 DXPS j 2106 s 0 n j 2106 DXPS_reverse_86aca j 2107 d 0 1000 n j 2107 DXYLK j 2108 s 0 n j 2108 DXYLK_reverse_fb109 j 2109 d 0 1000 n j 2109 E4PD j 2110 d 0 1000 n j 2110 E4PD_reverse_babdb j 2111 d 0 1000 n j 2111 EAR100x j 2112 s 0 n j 2112 EAR100x_reverse_d973f j 2113 d 0 1000 n j 2113 EAR100y j 2114 s 0 n j 2114 EAR100y_reverse_863b6 j 2115 d 0 1000 n j 2115 EAR120x j 2116 s 0 n j 2116 EAR120x_reverse_72a18 j 2117 d 0 1000 n j 2117 EAR120y j 2118 s 0 n j 2118 EAR120y_reverse_c7353 j 2119 d 0 1000 n j 2119 EAR121x j 2120 s 0 n j 2120 EAR121x_reverse_d2e6c j 2121 d 0 1000 n j 2121 EAR121y j 2122 s 0 n j 2122 EAR121y_reverse_9014d j 2123 d 0 1000 n j 2123 EAR140x j 2124 s 0 n j 2124 EAR140x_reverse_01529 j 2125 d 0 1000 n j 2125 EAR140y j 2126 s 0 n j 2126 EAR140y_reverse_dff77 j 2127 d 0 1000 n j 2127 EAR141x j 2128 s 0 n j 2128 EAR141x_reverse_3a0ef j 2129 d 0 1000 n j 2129 EAR141y j 2130 s 0 n j 2130 EAR141y_reverse_496a8 j 2131 d 0 1000 n j 2131 EAR160x j 2132 s 0 n j 2132 EAR160x_reverse_07017 j 2133 d 0 1000 n j 2133 EAR160y j 2134 s 0 n j 2134 EAR160y_reverse_e0622 j 2135 d 0 1000 n j 2135 EAR161x j 2136 s 0 n j 2136 EAR161x_reverse_4d0d0 j 2137 d 0 1000 n j 2137 EAR161y j 2138 s 0 n j 2138 EAR161y_reverse_fb8a8 j 2139 d 0 1000 n j 2139 EAR180x j 2140 s 0 n j 2140 EAR180x_reverse_fbf60 j 2141 d 0 1000 n j 2141 EAR180y j 2142 s 0 n j 2142 EAR180y_reverse_2cedd j 2143 d 0 1000 n j 2143 EAR181x j 2144 s 0 n j 2144 EAR181x_reverse_3d2a4 j 2145 d 0 1000 n j 2145 EAR181y j 2146 s 0 n j 2146 EAR181y_reverse_6d40a j 2147 d 0 1000 n j 2147 EAR40x j 2148 s 0 n j 2148 EAR40x_reverse_ebbbc j 2149 d 0 1000 n j 2149 EAR40y j 2150 s 0 n j 2150 EAR40y_reverse_0f912 j 2151 d 0 1000 n j 2151 EAR60x j 2152 s 0 n j 2152 EAR60x_reverse_9e2fc j 2153 d 0 1000 n j 2153 EAR60y j 2154 s 0 n j 2154 EAR60y_reverse_02e5e j 2155 d 0 1000 n j 2155 EAR80x j 2156 s 0 n j 2156 EAR80x_reverse_1065c j 2157 d 0 1000 n j 2157 EAR80y j 2158 s 0 n j 2158 EAR80y_reverse_2df0a j 2159 d 0 1000 n j 2159 ECA4COLIPAabctex j 2160 s 0 n j 2160 ECA4COLIPAabctex_reverse_20e46 j 2161 d 0 1000 n j 2161 ECA4OALpp j 2162 s 0 n j 2162 ECA4OALpp_reverse_71af3 j 2163 d 0 1000 n j 2163 ECAP1pp j 2164 s 0 n j 2164 ECAP1pp_reverse_74a2b j 2165 d 0 1000 n j 2165 ECAP2pp j 2166 s 0 n j 2166 ECAP2pp_reverse_3b2a7 j 2167 d 0 1000 n j 2167 ECAP3pp j 2168 s 0 n j 2168 ECAP3pp_reverse_83672 j 2169 d 0 1000 n j 2169 ECAtpp j 2170 s 0 n j 2170 ECAtpp_reverse_3630a j 2171 d 0 1000 n j 2171 ECOAH1 j 2172 d 0 1000 n j 2172 ECOAH1_reverse_6e99c j 2173 d 0 1000 n j 2173 ECOAH2 j 2174 d 0 1000 n j 2174 ECOAH2_reverse_fa31c j 2175 d 0 1000 n j 2175 ECOAH3 j 2176 d 0 1000 n j 2176 ECOAH3_reverse_fede1 j 2177 d 0 1000 n j 2177 ECOAH4 j 2178 d 0 1000 n j 2178 ECOAH4_reverse_b3830 j 2179 d 0 1000 n j 2179 ECOAH5 j 2180 d 0 1000 n j 2180 ECOAH5_reverse_0cdd7 j 2181 d 0 1000 n j 2181 ECOAH6 j 2182 d 0 1000 n j 2182 ECOAH6_reverse_9bf56 j 2183 d 0 1000 n j 2183 ECOAH7 j 2184 d 0 1000 n j 2184 ECOAH7_reverse_b3898 j 2185 d 0 1000 n j 2185 ECOAH8 j 2186 d 0 1000 n j 2186 ECOAH8_reverse_19c39 j 2187 d 0 1000 n j 2187 EDA j 2188 s 0 n j 2188 EDA_reverse_81f1b j 2189 d 0 1000 n j 2189 EDD j 2190 s 0 n j 2190 EDD_reverse_007a2 j 2191 d 0 1000 n j 2191 EDTXS1 j 2192 s 0 n j 2192 EDTXS1_reverse_2f111 j 2193 d 0 1000 n j 2193 EDTXS2 j 2194 s 0 n j 2194 EDTXS2_reverse_119c0 j 2195 d 0 1000 n j 2195 EDTXS3 j 2196 s 0 n j 2196 EDTXS3_reverse_998ec j 2197 d 0 1000 n j 2197 EDTXS4 j 2198 s 0 n j 2198 EDTXS4_reverse_86896 j 2199 d 0 1000 n j 2199 EGMEACPR j 2200 s 0 n j 2200 EGMEACPR_reverse_1b486 j 2201 d 0 1000 n j 2201 ENLIPAabctex j 2202 s 0 n j 2202 ENLIPAabctex_reverse_31d4e j 2203 d 0 1000 n j 2203 ENO j 2204 d 0 1000 n j 2204 ENO_reverse_40eea j 2205 d 0 1000 n j 2205 ENTCS j 2206 s 0 n j 2206 ENTCS_reverse_2c03a j 2207 d 0 1000 n j 2207 ENTERES j 2208 s 0 n j 2208 ENTERES_reverse_43d02 j 2209 d 0 1000 n j 2209 ENTERES2 j 2210 s 0 n j 2210 ENTERES2_reverse_17eef j 2211 d 0 1000 n j 2211 EPMEACPR j 2212 s 0 n j 2212 EPMEACPR_reverse_794bd j 2213 d 0 1000 n j 2213 ETHAAL j 2214 s 0 n j 2214 ETHAAL_reverse_df637 j 2215 d 0 1000 n j 2215 ETHAt2pp j 2216 s 0 n j 2216 ETHAt2pp_reverse_2d3e3 j 2217 d 0 1000 n j 2217 ETHAtex j 2218 d 0 1000 n j 2218 ETHAtex_reverse_10a5e j 2219 d 0 1000 n j 2219 ETHSO3abcpp j 2220 s 0 n j 2220 ETHSO3abcpp_reverse_31ebf j 2221 d 0 1000 n j 2221 ETHSO3tex j 2222 d 0 1000 n j 2222 ETHSO3tex_reverse_cc017 j 2223 d 0 1000 n j 2223 ETOHtex j 2224 d 0 1000 n j 2224 ETOHtex_reverse_462e1 j 2225 d 0 1000 n j 2225 ETOHtrpp j 2226 d 0 1000 n j 2226 ETOHtrpp_reverse_6a5ec j 2227 d 0 1000 n j 2227 F6PA j 2228 d 0 1000 n j 2228 F6PA_reverse_816ed j 2229 d 0 1000 n j 2229 F6PP j 2230 s 0 n j 2230 F6PP_reverse_6022a j 2231 d 0 1000 n j 2231 F6Pt6_2pp j 2232 s 0 n j 2232 F6Pt6_2pp_reverse_2b592 j 2233 d 0 1000 n j 2233 F6Ptex j 2234 d 0 1000 n j 2234 F6Ptex_reverse_b4bbc j 2235 d 0 1000 n j 2235 FA100ACPHi j 2236 s 0 n j 2236 FA100ACPHi_reverse_838d9 j 2237 d 0 1000 n j 2237 FA120ACPHi j 2238 s 0 n j 2238 FA120ACPHi_reverse_4e287 j 2239 d 0 1000 n j 2239 FA140ACPHi j 2240 s 0 n j 2240 FA140ACPHi_reverse_c7f9a j 2241 d 0 1000 n j 2241 FA141ACPHi j 2242 s 0 n j 2242 FA141ACPHi_reverse_c0f63 j 2243 d 0 1000 n j 2243 FA160ACPHi j 2244 s 0 n j 2244 FA160ACPHi_reverse_e7083 j 2245 d 0 1000 n j 2245 FA161ACPHi j 2246 s 0 n j 2246 FA161ACPHi_reverse_c2d36 j 2247 d 0 1000 n j 2247 FA80ACPHi j 2248 s 0 n j 2248 FA80ACPHi_reverse_11bcc j 2249 d 0 1000 n j 2249 FACOAE100 j 2250 s 0 n j 2250 FACOAE100_reverse_4e2b1 j 2251 d 0 1000 n j 2251 FACOAE120 j 2252 s 0 n j 2252 FACOAE120_reverse_5b66f j 2253 d 0 1000 n j 2253 FACOAE140 j 2254 s 0 n j 2254 FACOAE140_reverse_a2f77 j 2255 d 0 1000 n j 2255 FACOAE141 j 2256 s 0 n j 2256 FACOAE141_reverse_53f9d j 2257 d 0 1000 n j 2257 FACOAE160 j 2258 s 0 n j 2258 FACOAE160_reverse_cf5f6 j 2259 d 0 1000 n j 2259 FACOAE161 j 2260 s 0 n j 2260 FACOAE161_reverse_eee30 j 2261 d 0 1000 n j 2261 FACOAE180 j 2262 s 0 n j 2262 FACOAE180_reverse_7e403 j 2263 d 0 1000 n j 2263 FACOAE181 j 2264 s 0 n j 2264 FACOAE181_reverse_801e1 j 2265 d 0 1000 n j 2265 FACOAE60 j 2266 s 0 n j 2266 FACOAE60_reverse_69a9a j 2267 d 0 1000 n j 2267 FACOAE80 j 2268 s 0 n j 2268 FACOAE80_reverse_fab77 j 2269 d 0 1000 n j 2269 FACOAL100t2pp j 2270 s 0 n j 2270 FACOAL100t2pp_reverse_8cd18 j 2271 d 0 1000 n j 2271 FACOAL120t2pp j 2272 s 0 n j 2272 FACOAL120t2pp_reverse_7fbe8 j 2273 d 0 1000 n j 2273 FACOAL140t2pp j 2274 s 0 n j 2274 FACOAL140t2pp_reverse_134cb j 2275 d 0 1000 n j 2275 FACOAL141t2pp j 2276 s 0 n j 2276 FACOAL141t2pp_reverse_a4489 j 2277 d 0 1000 n j 2277 FACOAL160t2pp j 2278 s 0 n j 2278 FACOAL160t2pp_reverse_57f27 j 2279 d 0 1000 n j 2279 FACOAL161t2pp j 2280 s 0 n j 2280 FACOAL161t2pp_reverse_19e85 j 2281 d 0 1000 n j 2281 FACOAL180t2pp j 2282 s 0 n j 2282 FACOAL180t2pp_reverse_4b889 j 2283 d 0 1000 n j 2283 FACOAL181t2pp j 2284 s 0 n j 2284 FACOAL181t2pp_reverse_74ac3 j 2285 d 0 1000 n j 2285 FACOAL60t2pp j 2286 s 0 n j 2286 FACOAL60t2pp_reverse_f9af5 j 2287 d 0 1000 n j 2287 FACOAL80t2pp j 2288 s 0 n j 2288 FACOAL80t2pp_reverse_a6beb j 2289 d 0 1000 n j 2289 FADRx j 2290 s 0 n j 2290 FADRx_reverse_48623 j 2291 d 0 1000 n j 2291 FADRx2 j 2292 s 0 n j 2292 FADRx2_reverse_f1eff j 2293 d 0 1000 n j 2293 FALDH2 j 2294 d 0 1000 n j 2294 FALDH2_reverse_f1aae j 2295 d 0 1000 n j 2295 FALDtex j 2296 d 0 1000 n j 2296 FALDtex_reverse_1317b j 2297 d 0 1000 n j 2297 FALDtpp j 2298 d 0 1000 n j 2298 FALDtpp_reverse_a8bc3 j 2299 d 0 1000 n j 2299 FALGTHLs j 2300 d 0 1000 n j 2300 FALGTHLs_reverse_1514d j 2301 d 0 1000 n j 2301 FBA j 2302 d 0 1000 n j 2302 FBA_reverse_84806 j 2303 d 0 1000 n j 2303 FBA3 j 2304 d 0 1000 n j 2304 FBA3_reverse_0d49f j 2305 d 0 1000 n j 2305 FBP j 2306 s 0 n j 2306 FBP_reverse_bf2c9 j 2307 d 0 1000 n j 2307 FCI j 2308 d 0 1000 n j 2308 FCI_reverse_74198 j 2309 d 0 1000 n j 2309 FCLK j 2310 s 0 n j 2310 FCLK_reverse_8faf5 j 2311 d 0 1000 n j 2311 FCLPA j 2312 d 0 1000 n j 2312 FCLPA_reverse_df7f9 j 2313 d 0 1000 n j 2313 FCLT j 2314 s 0 n j 2314 FCLT_reverse_1a6b6 j 2315 d 0 1000 n j 2315 FDH4pp j 2316 s 0 n j 2316 FDH4pp_reverse_2bad3 j 2317 d 0 1000 n j 2317 FDH5pp j 2318 s 0 n j 2318 FDH5pp_reverse_ab9f8 j 2319 d 0 1000 n j 2319 FDMO j 2320 s 0 n j 2320 FDMO_reverse_0d455 j 2321 d 0 1000 n j 2321 FDMO2 j 2322 s 0 n j 2322 FDMO2_reverse_b2043 j 2323 d 0 1000 n j 2323 FDMO3 j 2324 s 0 n j 2324 FDMO3_reverse_1830e j 2325 d 0 1000 n j 2325 FDMO4 j 2326 s 0 n j 2326 FDMO4_reverse_0b2e6 j 2327 d 0 1000 n j 2327 FDMO6 j 2328 s 0 n j 2328 FDMO6_reverse_68143 j 2329 d 0 1000 n j 2329 FE2abcpp j 2330 s 0 n j 2330 FE2abcpp_reverse_fbca1 j 2331 d 0 1000 n j 2331 FE2t2pp j 2332 s 0 n j 2332 FE2t2pp_reverse_50348 j 2333 d 0 1000 n j 2333 FE2t3pp j 2334 s 0 n j 2334 FE2t3pp_reverse_f87e0 j 2335 d 0 1000 n j 2335 FE2tex j 2336 d 0 1000 n j 2336 FE2tex_reverse_62032 j 2337 d 0 1000 n j 2337 FE2tpp j 2338 s 0 n j 2338 FE2tpp_reverse_07828 j 2339 d 0 1000 n j 2339 FE3DCITabcpp j 2340 s 0 n j 2340 FE3DCITabcpp_reverse_80761 j 2341 d 0 1000 n j 2341 FE3DCITtonex j 2342 s 0 n j 2342 FE3DCITtonex_reverse_1655d j 2343 d 0 1000 n j 2343 FE3DHBZR j 2344 s 0 n j 2344 FE3DHBZR_reverse_14b6c j 2345 d 0 1000 n j 2345 FE3DHBZSabcpp j 2346 s 0 n j 2346 FE3DHBZSabcpp_reverse_ca936 j 2347 d 0 1000 n j 2347 FE3DHBZStonex j 2348 s 0 n j 2348 FE3DHBZStonex_reverse_5e203 j 2349 d 0 1000 n j 2349 FE3HOXR1 j 2350 s 0 n j 2350 FE3HOXR1_reverse_26ea5 j 2351 d 0 1000 n j 2351 FE3HOXR2 j 2352 s 0 n j 2352 FE3HOXR2_reverse_872a1 j 2353 d 0 1000 n j 2353 FE3HOXR3 j 2354 s 0 n j 2354 FE3HOXR3_reverse_fc59b j 2355 d 0 1000 n j 2355 FE3HOXUtex j 2356 s 0 n j 2356 FE3HOXUtex_reverse_6114b j 2357 d 0 1000 n j 2357 FE3HOXUtpp j 2358 s 0 n j 2358 FE3HOXUtpp_reverse_03898 j 2359 d 0 1000 n j 2359 FE3HOXabcpp j 2360 s 0 n j 2360 FE3HOXabcpp_reverse_784a3 j 2361 d 0 1000 n j 2361 FE3HOXexs j 2362 s 0 n j 2362 FE3HOXexs_reverse_6e61e j 2363 d 0 1000 n j 2363 FE3HOXtonex j 2364 s 0 n j 2364 FE3HOXtonex_reverse_1dfd1 j 2365 d 0 1000 n j 2365 FE3Ri j 2366 s 0 n j 2366 FE3Ri_reverse_dd1fc j 2367 d 0 1000 n j 2367 FE3abcpp j 2368 s 0 n j 2368 FE3abcpp_reverse_4aad8 j 2369 d 0 1000 n j 2369 FE3tex j 2370 d 0 1000 n j 2370 FE3tex_reverse_d0931 j 2371 d 0 1000 n j 2371 FECRMR1 j 2372 s 0 n j 2372 FECRMR1_reverse_ac48a j 2373 d 0 1000 n j 2373 FECRMR2 j 2374 s 0 n j 2374 FECRMR2_reverse_c664c j 2375 d 0 1000 n j 2375 FECRMR3 j 2376 s 0 n j 2376 FECRMR3_reverse_0579c j 2377 d 0 1000 n j 2377 FECRMUtex j 2378 s 0 n j 2378 FECRMUtex_reverse_6c617 j 2379 d 0 1000 n j 2379 FECRMUtpp j 2380 s 0 n j 2380 FECRMUtpp_reverse_d3b60 j 2381 d 0 1000 n j 2381 FECRMabcpp j 2382 s 0 n j 2382 FECRMabcpp_reverse_7f712 j 2383 d 0 1000 n j 2383 FECRMexs j 2384 s 0 n j 2384 FECRMexs_reverse_04dda j 2385 d 0 1000 n j 2385 FECRMtonex j 2386 s 0 n j 2386 FECRMtonex_reverse_4ef83 j 2387 d 0 1000 n j 2387 FEENTERR1 j 2388 s 0 n j 2388 FEENTERR1_reverse_a3dda j 2389 d 0 1000 n j 2389 FEENTERR2 j 2390 s 0 n j 2390 FEENTERR2_reverse_0af6f j 2391 d 0 1000 n j 2391 FEENTERR3 j 2392 s 0 n j 2392 FEENTERR3_reverse_d410a j 2393 d 0 1000 n j 2393 FEENTERabcpp j 2394 s 0 n j 2394 FEENTERabcpp_reverse_a4ab4 j 2395 d 0 1000 n j 2395 FEENTERexs j 2396 s 0 n j 2396 FEENTERexs_reverse_3df6f j 2397 d 0 1000 n j 2397 FEENTERtex j 2398 s 0 n j 2398 FEENTERtex_reverse_60b33 j 2399 d 0 1000 n j 2399 FEENTERtonex j 2400 s 0 n j 2400 FEENTERtonex_reverse_aa732 j 2401 d 0 1000 n j 2401 FEENTERtpp j 2402 s 0 n j 2402 FEENTERtpp_reverse_35de8 j 2403 d 0 1000 n j 2403 FEOXAMR1 j 2404 s 0 n j 2404 FEOXAMR1_reverse_5efcf j 2405 d 0 1000 n j 2405 FEOXAMR2 j 2406 s 0 n j 2406 FEOXAMR2_reverse_3ce4c j 2407 d 0 1000 n j 2407 FEOXAMR3 j 2408 s 0 n j 2408 FEOXAMR3_reverse_a2b98 j 2409 d 0 1000 n j 2409 FEOXAMUtex j 2410 s 0 n j 2410 FEOXAMUtex_reverse_ce967 j 2411 d 0 1000 n j 2411 FEOXAMUtpp j 2412 s 0 n j 2412 FEOXAMUtpp_reverse_a25af j 2413 d 0 1000 n j 2413 FEOXAMabcpp j 2414 s 0 n j 2414 FEOXAMabcpp_reverse_5457e j 2415 d 0 1000 n j 2415 FEOXAMexs j 2416 s 0 n j 2416 FEOXAMexs_reverse_1312b j 2417 d 0 1000 n j 2417 FEOXAMtonex j 2418 s 0 n j 2418 FEOXAMtonex_reverse_c1ce4 j 2419 d 0 1000 n j 2419 FEROpp j 2420 s 0 n j 2420 FEROpp_reverse_a433b j 2421 d 0 1000 n j 2421 FESD1s j 2422 s 0 n j 2422 FESD1s_reverse_ea904 j 2423 d 0 1000 n j 2423 FESD2s j 2424 s 0 n j 2424 FESD2s_reverse_3b4bc j 2425 d 0 1000 n j 2425 FESR j 2426 s 0 n j 2426 FESR_reverse_9387b j 2427 d 0 1000 n j 2427 FFSD j 2428 s 0 n j 2428 FFSD_reverse_d9ea6 j 2429 s 0 n j 2429 FHL j 2430 s 0 n j 2430 FHL_reverse_2a0cb j 2431 d 0 1000 n j 2431 FLDR2 j 2432 s 0 n j 2432 FLDR2_reverse_31926 j 2433 d 0 1000 n j 2433 FLVR j 2434 s 0 n j 2434 FLVR_reverse_e5074 j 2435 d 0 1000 n j 2435 FLVRx j 2436 s 0 n j 2436 FLVRx_reverse_e0efc j 2437 d 0 1000 n j 2437 FMETTRS j 2438 s 0 n j 2438 FMETTRS_reverse_3b6c6 j 2439 d 0 1000 n j 2439 FMNAT j 2440 s 0 n j 2440 FMNAT_reverse_50ba1 j 2441 d 0 1000 n j 2441 FMNRx j 2442 s 0 n j 2442 FMNRx_reverse_d06bb j 2443 d 0 1000 n j 2443 FMNRx2 j 2444 s 0 n j 2444 FMNRx2_reverse_5e09f j 2445 d 0 1000 n j 2445 FOMETRi j 2446 s 0 n j 2446 FOMETRi_reverse_bd8b6 j 2447 d 0 1000 n j 2447 FORCT j 2448 d 0 1000 n j 2448 FORCT_reverse_45a87 j 2449 d 0 1000 n j 2449 FORt2pp j 2450 s 0 n j 2450 FORt2pp_reverse_c6a6b j 2451 d 0 1000 n j 2451 FORtex j 2452 d 0 1000 n j 2452 FORtex_reverse_0935f j 2453 d 0 1000 n j 2453 FORtppi j 2454 s 0 n j 2454 FORtppi_reverse_ddf9e j 2455 d 0 1000 n j 2455 FRD2 j 2456 s 0 n j 2456 FRD2_reverse_9a9f9 j 2457 d 0 1000 n j 2457 FRD3 j 2458 s 0 n j 2458 FRD3_reverse_78134 j 2459 d 0 1000 n j 2459 FRUK j 2460 s 0 n j 2460 FRUK_reverse_e5cfd j 2461 d 0 1000 n j 2461 FRULYSDG j 2462 d 0 1000 n j 2462 FRULYSDG_reverse_857ca j 2463 d 0 1000 n j 2463 FRULYSE j 2464 d 0 1000 n j 2464 FRULYSE_reverse_0a8b5 j 2465 d 0 1000 n j 2465 FRULYSK j 2466 s 0 n j 2466 FRULYSK_reverse_57742 j 2467 d 0 1000 n j 2467 FRULYSt2pp j 2468 s 0 n j 2468 FRULYSt2pp_reverse_04928 j 2469 d 0 1000 n j 2469 FRULYStex j 2470 d 0 1000 n j 2470 FRULYStex_reverse_d8a3d j 2471 d 0 1000 n j 2471 FRUURt2rpp j 2472 d 0 1000 n j 2472 FRUURt2rpp_reverse_5ad4f j 2473 d 0 1000 n j 2473 FRUURtex j 2474 d 0 1000 n j 2474 FRUURtex_reverse_7b42b j 2475 d 0 1000 n j 2475 FRUpts2pp j 2476 s 0 n j 2476 FRUpts2pp_reverse_55dac j 2477 d 0 1000 n j 2477 FRUptspp j 2478 s 0 n j 2478 FRUptspp_reverse_8cdda j 2479 d 0 1000 n j 2479 FRUtex j 2480 d 0 1000 n j 2480 FRUtex_reverse_0160a j 2481 d 0 1000 n j 2481 FTHFD j 2482 s 0 n j 2482 FTHFD_reverse_44321 j 2483 d 0 1000 n j 2483 FTHFLi j 2484 s 0 n j 2484 FTHFLi_reverse_a6dc7 j 2485 d 0 1000 n j 2485 FUCtex j 2486 d 0 1000 n j 2486 FUCtex_reverse_eebba j 2487 d 0 1000 n j 2487 FUCtpp j 2488 d 0 1000 n j 2488 FUCtpp_reverse_d2289 j 2489 d 0 1000 n j 2489 FUM j 2490 d 0 1000 n j 2490 FUM_reverse_d3642 j 2491 d 0 1000 n j 2491 FUMt2_2pp j 2492 s 0 n j 2492 FUMt2_2pp_reverse_fb621 j 2493 d 0 1000 n j 2493 FUMt2_3pp j 2494 s 0 n j 2494 FUMt2_3pp_reverse_af3fe j 2495 d 0 1000 n j 2495 FUMtex j 2496 d 0 1000 n j 2496 FUMtex_reverse_556a3 j 2497 d 0 1000 n j 2497 FUSAtex j 2498 d 0 1000 n j 2498 FUSAtex_reverse_71203 j 2499 d 0 1000 n j 2499 FUSAtpp j 2500 s 0 n j 2500 FUSAtpp_reverse_f302d j 2501 d 0 1000 n j 2501 G1PACT j 2502 s 0 n j 2502 G1PACT_reverse_51580 j 2503 d 0 1000 n j 2503 G1PPpp j 2504 s 0 n j 2504 G1PPpp_reverse_c08b7 j 2505 d 0 1000 n j 2505 G1PTT j 2506 s 0 n j 2506 G1PTT_reverse_acd22 j 2507 d 0 1000 n j 2507 G1Ptex j 2508 d 0 1000 n j 2508 G1Ptex_reverse_6b1be j 2509 d 0 1000 n j 2509 G1SAT j 2510 d 0 1000 n j 2510 G1SAT_reverse_2ec2f j 2511 d 0 1000 n j 2511 G2PP j 2512 s 0 n j 2512 G2PP_reverse_24ccd j 2513 d 0 1000 n j 2513 G2PPpp j 2514 s 0 n j 2514 G2PPpp_reverse_db88f j 2515 d 0 1000 n j 2515 G3PAT120 j 2516 s 0 n j 2516 G3PAT120_reverse_34d68 j 2517 d 0 1000 n j 2517 G3PAT140 j 2518 s 0 n j 2518 G3PAT140_reverse_70922 j 2519 d 0 1000 n j 2519 G3PAT141 j 2520 s 0 n j 2520 G3PAT141_reverse_b50c4 j 2521 d 0 1000 n j 2521 G3PAT160 j 2522 s 0 n j 2522 G3PAT160_reverse_446d0 j 2523 d 0 1000 n j 2523 G3PAT161 j 2524 s 0 n j 2524 G3PAT161_reverse_1ef08 j 2525 d 0 1000 n j 2525 G3PAT180 j 2526 s 0 n j 2526 G3PAT180_reverse_e7ff1 j 2527 d 0 1000 n j 2527 G3PAT181 j 2528 s 0 n j 2528 G3PAT181_reverse_89dcb j 2529 d 0 1000 n j 2529 G3PCabcpp j 2530 s 0 n j 2530 G3PCabcpp_reverse_533c2 j 2531 d 0 1000 n j 2531 G3PCtex j 2532 d 0 1000 n j 2532 G3PCtex_reverse_12db0 j 2533 d 0 1000 n j 2533 G3PD2 j 2534 d 0 1000 n j 2534 G3PD2_reverse_0c363 j 2535 d 0 1000 n j 2535 G3PD5 j 2536 s 0 n j 2536 G3PD5_reverse_cbf7e j 2537 d 0 1000 n j 2537 G3PD6 j 2538 s 0 n j 2538 G3PD6_reverse_a5207 j 2539 d 0 1000 n j 2539 G3PD7 j 2540 s 0 n j 2540 G3PD7_reverse_74364 j 2541 d 0 1000 n j 2541 G3PEabcpp j 2542 s 0 n j 2542 G3PEabcpp_reverse_86805 j 2543 d 0 1000 n j 2543 G3PEtex j 2544 d 0 1000 n j 2544 G3PEtex_reverse_2e8db j 2545 d 0 1000 n j 2545 G3PGabcpp j 2546 s 0 n j 2546 G3PGabcpp_reverse_603fd j 2547 d 0 1000 n j 2547 G3PGtex j 2548 d 0 1000 n j 2548 G3PGtex_reverse_56fc1 j 2549 d 0 1000 n j 2549 G3PIabcpp j 2550 s 0 n j 2550 G3PIabcpp_reverse_8097b j 2551 d 0 1000 n j 2551 G3PItex j 2552 d 0 1000 n j 2552 G3PItex_reverse_cf34b j 2553 d 0 1000 n j 2553 G3PSabcpp j 2554 s 0 n j 2554 G3PSabcpp_reverse_55636 j 2555 d 0 1000 n j 2555 G3PStex j 2556 d 0 1000 n j 2556 G3PStex_reverse_d8e57 j 2557 d 0 1000 n j 2557 G3PT j 2558 s 0 n j 2558 G3PT_reverse_0c714 j 2559 d 0 1000 n j 2559 G5SADs j 2560 s 0 n j 2560 G5SADs_reverse_c7fa4 j 2561 d 0 1000 n j 2561 G5SD j 2562 s 0 n j 2562 G5SD_reverse_af8c0 j 2563 d 0 1000 n j 2563 G6PDA j 2564 s 0 n j 2564 G6PDA_reverse_74cec j 2565 d 0 1000 n j 2565 G6PDH2r j 2566 d 0 1000 n j 2566 G6PDH2r_reverse_19ddf j 2567 d 0 1000 n j 2567 G6PP j 2568 s 0 n j 2568 G6PP_reverse_0ca97 j 2569 d 0 1000 n j 2569 G6Pt6_2pp j 2570 s 0 n j 2570 G6Pt6_2pp_reverse_d2a32 j 2571 d 0 1000 n j 2571 G6Ptex j 2572 d 0 1000 n j 2572 G6Ptex_reverse_18275 j 2573 d 0 1000 n j 2573 GAL1PPpp j 2574 s 0 n j 2574 GAL1PPpp_reverse_20a5d j 2575 d 0 1000 n j 2575 GAL1Ptex j 2576 d 0 1000 n j 2576 GAL1Ptex_reverse_59516 j 2577 d 0 1000 n j 2577 GALBDtex j 2578 d 0 1000 n j 2578 GALBDtex_reverse_20d94 j 2579 d 0 1000 n j 2579 GALCTD j 2580 s 0 n j 2580 GALCTD_reverse_50f26 j 2581 d 0 1000 n j 2581 GALCTLO j 2582 s 0 n j 2582 GALCTLO_reverse_6fb0b j 2583 d 0 1000 n j 2583 GALCTND j 2584 s 0 n j 2584 GALCTND_reverse_72513 j 2585 d 0 1000 n j 2585 GALCTNLt2pp j 2586 s 0 n j 2586 GALCTNLt2pp_reverse_9ab31 j 2587 d 0 1000 n j 2587 GALCTNLtex j 2588 d 0 1000 n j 2588 GALCTNLtex_reverse_0beec j 2589 d 0 1000 n j 2589 GALCTNt2pp j 2590 s 0 n j 2590 GALCTNt2pp_reverse_0033f j 2591 d 0 1000 n j 2591 GALCTNtex j 2592 d 0 1000 n j 2592 GALCTNtex_reverse_3d1b9 j 2593 d 0 1000 n j 2593 GALCTt2rpp j 2594 d 0 1000 n j 2594 GALCTt2rpp_reverse_3d443 j 2595 d 0 1000 n j 2595 GALCTtex j 2596 d 0 1000 n j 2596 GALCTtex_reverse_17864 j 2597 d 0 1000 n j 2597 GALKr j 2598 d 0 1000 n j 2598 GALKr_reverse_f2812 j 2599 d 0 1000 n j 2599 GALM2pp j 2600 s 0 n j 2600 GALM2pp_reverse_3782e j 2601 d 0 1000 n j 2601 GALS3 j 2602 s 0 n j 2602 GALS3_reverse_0876a j 2603 d 0 1000 n j 2603 GALT1 j 2604 s 0 n j 2604 GALT1_reverse_8f23f j 2605 d 0 1000 n j 2605 GALTptspp j 2606 s 0 n j 2606 GALTptspp_reverse_9b8ec j 2607 d 0 1000 n j 2607 GALTtex j 2608 d 0 1000 n j 2608 GALTtex_reverse_6effe j 2609 d 0 1000 n j 2609 GALURt2rpp j 2610 d 0 1000 n j 2610 GALURt2rpp_reverse_ab541 j 2611 d 0 1000 n j 2611 GALURtex j 2612 d 0 1000 n j 2612 GALURtex_reverse_76987 j 2613 d 0 1000 n j 2613 GALUi j 2614 s 0 n j 2614 GALUi_reverse_c40d5 j 2615 d 0 1000 n j 2615 GALabcpp j 2616 s 0 n j 2616 GALabcpp_reverse_5f3e3 j 2617 d 0 1000 n j 2617 GALt2pp j 2618 s 0 n j 2618 GALt2pp_reverse_a17c6 j 2619 d 0 1000 n j 2619 GALtex j 2620 d 0 1000 n j 2620 GALtex_reverse_3707a j 2621 d 0 1000 n j 2621 GAM6Pt6_2pp j 2622 s 0 n j 2622 GAM6Pt6_2pp_reverse_fb76b j 2623 d 0 1000 n j 2623 GAMAN6Ptex j 2624 d 0 1000 n j 2624 GAMAN6Ptex_reverse_9152e j 2625 d 0 1000 n j 2625 GAMptspp j 2626 s 0 n j 2626 GAMptspp_reverse_3e396 j 2627 d 0 1000 n j 2627 GAMtex j 2628 d 0 1000 n j 2628 GAMtex_reverse_bc147 j 2629 d 0 1000 n j 2629 GAPD j 2630 d 0 1000 n j 2630 GAPD_reverse_459c1 j 2631 d 0 1000 n j 2631 GARFT j 2632 d 0 1000 n j 2632 GARFT_reverse_7ecb6 j 2633 d 0 1000 n j 2633 GART j 2634 s 0 n j 2634 GART_reverse_61742 j 2635 d 0 1000 n j 2635 GBBTNtex j 2636 d 0 1000 n j 2636 GBBTNtex_reverse_b2a35 j 2637 d 0 1000 n j 2637 GCALDD j 2638 s 0 n j 2638 GCALDD_reverse_d2641 j 2639 d 0 1000 n j 2639 GDMANE j 2640 s 0 n j 2640 GDMANE_reverse_b86aa j 2641 d 0 1000 n j 2641 GDPDPK j 2642 s 0 n j 2642 GDPDPK_reverse_382cc j 2643 d 0 1000 n j 2643 GDPMNH j 2644 s 0 n j 2644 GDPMNH_reverse_65ff7 j 2645 d 0 1000 n j 2645 GDPMNP j 2646 s 0 n j 2646 GDPMNP_reverse_21010 j 2647 d 0 1000 n j 2647 GDPTPDP j 2648 s 0 n j 2648 GDPTPDP_reverse_a6cbf j 2649 d 0 1000 n j 2649 GDPtex j 2650 d 0 1000 n j 2650 GDPtex_reverse_749cd j 2651 d 0 1000 n j 2651 GF6PTA j 2652 s 0 n j 2652 GF6PTA_reverse_21fb1 j 2653 d 0 1000 n j 2653 GGGABADr j 2654 d 0 1000 n j 2654 GGGABADr_reverse_906f4 j 2655 d 0 1000 n j 2655 GGGABAH j 2656 s 0 n j 2656 GGGABAH_reverse_3eda1 j 2657 d 0 1000 n j 2657 GGPTRCO j 2658 s 0 n j 2658 GGPTRCO_reverse_9665d j 2659 d 0 1000 n j 2659 GGPTRCS j 2660 s 0 n j 2660 GGPTRCS_reverse_e991c j 2661 d 0 1000 n j 2661 GHBDHx j 2662 d 0 1000 n j 2662 GHBDHx_reverse_f0ecc j 2663 d 0 1000 n j 2663 GHMT2r j 2664 d 0 1000 n j 2664 GHMT2r_reverse_d977f j 2665 d 0 1000 n j 2665 GK1 j 2666 d 0 1000 n j 2666 GK1_reverse_11a40 j 2667 d 0 1000 n j 2667 GLBRAN2 j 2668 s 0 n j 2668 GLBRAN2_reverse_1b8be j 2669 d 0 1000 n j 2669 GLCATr j 2670 d 0 1000 n j 2670 GLCATr_reverse_9af93 j 2671 d 0 1000 n j 2671 GLCDpp j 2672 s 0 n j 2672 GLCDpp_reverse_d9944 j 2673 d 0 1000 n j 2673 GLCNt2rpp j 2674 d 0 1000 n j 2674 GLCNt2rpp_reverse_056bf j 2675 d 0 1000 n j 2675 GLCNtex j 2676 d 0 1000 n j 2676 GLCNtex_reverse_2dd9c j 2677 d 0 1000 n j 2677 GLCP j 2678 s 0 n j 2678 GLCP_reverse_c3987 j 2679 d 0 1000 n j 2679 GLCP2 j 2680 s 0 n j 2680 GLCP2_reverse_550c1 j 2681 d 0 1000 n j 2681 GLCRAL j 2682 s 0 n j 2682 GLCRAL_reverse_e887c j 2683 d 0 1000 n j 2683 GLCRD j 2684 s 0 n j 2684 GLCRD_reverse_f42e2 j 2685 d 0 1000 n j 2685 GLCRt2rpp j 2686 d 0 1000 n j 2686 GLCRt2rpp_reverse_e7168 j 2687 d 0 1000 n j 2687 GLCRtex j 2688 d 0 1000 n j 2688 GLCRtex_reverse_91298 j 2689 d 0 1000 n j 2689 GLCS1 j 2690 s 0 n j 2690 GLCS1_reverse_6cce0 j 2691 d 0 1000 n j 2691 GLCTR1 j 2692 s 0 n j 2692 GLCTR1_reverse_7108b j 2693 d 0 1000 n j 2693 GLCTR2 j 2694 s 0 n j 2694 GLCTR2_reverse_0d51e j 2695 d 0 1000 n j 2695 GLCTR3 j 2696 s 0 n j 2696 GLCTR3_reverse_40321 j 2697 d 0 1000 n j 2697 GLCUR1Ptex j 2698 d 0 1000 n j 2698 GLCUR1Ptex_reverse_77625 j 2699 d 0 1000 n j 2699 GLCURt2rpp j 2700 d 0 1000 n j 2700 GLCURt2rpp_reverse_15d52 j 2701 d 0 1000 n j 2701 GLCURtex j 2702 d 0 1000 n j 2702 GLCURtex_reverse_45707 j 2703 d 0 1000 n j 2703 GLCabcpp j 2704 s 0 n j 2704 GLCabcpp_reverse_fb087 j 2705 d 0 1000 n j 2705 GLCptspp j 2706 s 0 n j 2706 GLCptspp_reverse_9cf76 j 2707 d 0 1000 n j 2707 GLCt2pp j 2708 s 0 n j 2708 GLCt2pp_reverse_b9e3b j 2709 d 0 1000 n j 2709 GLCtex_copy1 j 2710 d 0 1000 n j 2710 GLCtex_copy1_reverse_c825d j 2711 d 0 1000 n j 2711 GLCtex_copy2 j 2712 s 0 n j 2712 GLCtex_copy2_reverse_a0b11 j 2713 d 0 1000 n j 2713 GLDBRAN2 j 2714 s 0 n j 2714 GLDBRAN2_reverse_149c3 j 2715 d 0 1000 n j 2715 GLGC j 2716 s 0 n j 2716 GLGC_reverse_f6fb0 j 2717 d 0 1000 n j 2717 GLNS j 2718 s 0 n j 2718 GLNS_reverse_59581 j 2719 d 0 1000 n j 2719 GLNTRS j 2720 s 0 n j 2720 GLNTRS_reverse_062f1 j 2721 d 0 1000 n j 2721 GLNabcpp j 2722 s 0 n j 2722 GLNabcpp_reverse_c0546 j 2723 d 0 1000 n j 2723 GLNtex j 2724 d 0 1000 n j 2724 GLNtex_reverse_7b7bb j 2725 d 0 1000 n j 2725 GLTPD j 2726 d 0 1000 n j 2726 GLTPD_reverse_03e44 j 2727 d 0 1000 n j 2727 GLU5K j 2728 s 0 n j 2728 GLU5K_reverse_0d895 j 2729 d 0 1000 n j 2729 GLUABUTt7pp j 2730 d 0 1000 n j 2730 GLUABUTt7pp_reverse_5005a j 2731 d 0 1000 n j 2731 GLUCYS j 2732 s 0 n j 2732 GLUCYS_reverse_f13d6 j 2733 d 0 1000 n j 2733 GLUDC j 2734 s 0 n j 2734 GLUDC_reverse_c4aae j 2735 d 0 1000 n j 2735 GLUDy j 2736 d 0 1000 n j 2736 GLUDy_reverse_fa4e7 j 2737 d 0 1000 n j 2737 GLUN j 2738 s 0 n j 2738 GLUN_reverse_4ccdb j 2739 d 0 1000 n j 2739 GLUNpp j 2740 s 0 n j 2740 GLUNpp_reverse_b9a17 j 2741 d 0 1000 n j 2741 GLUPRT j 2742 s 0 n j 2742 GLUPRT_reverse_1f180 j 2743 d 0 1000 n j 2743 GLUR j 2744 d 0 1000 n j 2744 GLUR_reverse_6b3bf j 2745 d 0 1000 n j 2745 GLUSy j 2746 s 0 n j 2746 GLUSy_reverse_6a00f j 2747 d 0 1000 n j 2747 GLUTRR j 2748 s 0 n j 2748 GLUTRR_reverse_355d5 j 2749 d 0 1000 n j 2749 GLUTRS j 2750 s 0 n j 2750 GLUTRS_reverse_b214d j 2751 d 0 1000 n j 2751 GLUabcpp j 2752 s 0 n j 2752 GLUabcpp_reverse_31e5a j 2753 d 0 1000 n j 2753 GLUt2rpp j 2754 d 0 1000 n j 2754 GLUt2rpp_reverse_6203a j 2755 d 0 1000 n j 2755 GLUt4pp j 2756 s 0 n j 2756 GLUt4pp_reverse_e5533 j 2757 d 0 1000 n j 2757 GLUtex j 2758 d 0 1000 n j 2758 GLUtex_reverse_556e0 j 2759 d 0 1000 n j 2759 GLXCL j 2760 s 0 n j 2760 GLXCL_reverse_ea654 j 2761 d 0 1000 n j 2761 GLYALDtex j 2762 d 0 1000 n j 2762 GLYALDtex_reverse_9a060 j 2763 d 0 1000 n j 2763 GLYALDtpp j 2764 d 0 1000 n j 2764 GLYALDtpp_reverse_e48d4 j 2765 d 0 1000 n j 2765 GLYAT j 2766 d 0 1000 n j 2766 GLYAT_reverse_9e240 j 2767 d 0 1000 n j 2767 GLYBabcpp j 2768 s 0 n j 2768 GLYBabcpp_reverse_db5e6 j 2769 d 0 1000 n j 2769 GLYBt2pp j 2770 s 0 n j 2770 GLYBt2pp_reverse_8e061 j 2771 d 0 1000 n j 2771 GLYBtex j 2772 d 0 1000 n j 2772 GLYBtex_reverse_f31f5 j 2773 d 0 1000 n j 2773 GLYC2Pabcpp j 2774 s 0 n j 2774 GLYC2Pabcpp_reverse_40c01 j 2775 d 0 1000 n j 2775 GLYC2Ptex j 2776 d 0 1000 n j 2776 GLYC2Ptex_reverse_12c3e j 2777 d 0 1000 n j 2777 GLYC3Pabcpp j 2778 s 0 n j 2778 GLYC3Pabcpp_reverse_4dfe0 j 2779 d 0 1000 n j 2779 GLYC3Pt6pp j 2780 s 0 n j 2780 GLYC3Pt6pp_reverse_1e468 j 2781 d 0 1000 n j 2781 GLYC3Ptex j 2782 d 0 1000 n j 2782 GLYC3Ptex_reverse_6c7e7 j 2783 d 0 1000 n j 2783 GLYCAt2rpp j 2784 d 0 1000 n j 2784 GLYCAt2rpp_reverse_6cc5b j 2785 d 0 1000 n j 2785 GLYCAtex j 2786 d 0 1000 n j 2786 GLYCAtex_reverse_8cc71 j 2787 d 0 1000 n j 2787 GLYCDx j 2788 s 0 n j 2788 GLYCDx_reverse_1c0b9 j 2789 d 0 1000 n j 2789 GLYCK j 2790 s 0 n j 2790 GLYCK_reverse_c3ee2 j 2791 d 0 1000 n j 2791 GLYCK2 j 2792 s 0 n j 2792 GLYCK2_reverse_31342 j 2793 d 0 1000 n j 2793 GLYCL j 2794 s 0 n j 2794 GLYCL_reverse_e418f j 2795 d 0 1000 n j 2795 GLYCLTDx j 2796 s 0 n j 2796 GLYCLTDx_reverse_d2f71 j 2797 d 0 1000 n j 2797 GLYCLTDy j 2798 s 0 n j 2798 GLYCLTDy_reverse_c2d09 j 2799 d 0 1000 n j 2799 GLYCLTt2rpp j 2800 d 0 1000 n j 2800 GLYCLTt2rpp_reverse_8d806 j 2801 d 0 1000 n j 2801 GLYCLTt4pp j 2802 s 0 n j 2802 GLYCLTt4pp_reverse_e1135 j 2803 d 0 1000 n j 2803 GLYCLTtex j 2804 d 0 1000 n j 2804 GLYCLTtex_reverse_0f859 j 2805 d 0 1000 n j 2805 GLYCTO2 j 2806 s 0 n j 2806 GLYCTO2_reverse_b9aca j 2807 d 0 1000 n j 2807 GLYCTO3 j 2808 s 0 n j 2808 GLYCTO3_reverse_59bab j 2809 d 0 1000 n j 2809 GLYCTO4 j 2810 s 0 n j 2810 GLYCTO4_reverse_9c086 j 2811 d 0 1000 n j 2811 GLYCtex j 2812 d 0 1000 n j 2812 GLYCtex_reverse_8d161 j 2813 d 0 1000 n j 2813 GLYCtpp j 2814 d 0 1000 n j 2814 GLYCtpp_reverse_da8b3 j 2815 d 0 1000 n j 2815 GLYK j 2816 s 0 n j 2816 GLYK_reverse_bda48 j 2817 d 0 1000 n j 2817 GLYOX j 2818 s 0 n j 2818 GLYOX_reverse_6ab0a j 2819 d 0 1000 n j 2819 GLYOX3 j 2820 s 0 n j 2820 GLYOX3_reverse_1055b j 2821 d 0 1000 n j 2821 GLYTRS j 2822 s 0 n j 2822 GLYTRS_reverse_b4742 j 2823 d 0 1000 n j 2823 GLYt2pp_copy1 j 2824 s 0 n j 2824 GLYt2pp_copy1_reverse_19ba1 j 2825 d 0 1000 n j 2825 GLYt2pp_copy2 j 2826 d 0 1000 n j 2826 GLYt2pp_copy2_reverse_e2259 j 2827 d 0 1000 n j 2827 GLYt4pp j 2828 s 0 n j 2828 GLYt4pp_reverse_72e49 j 2829 d 0 1000 n j 2829 GLYtex j 2830 d 0 1000 n j 2830 GLYtex_reverse_52f38 j 2831 d 0 1000 n j 2831 GMAND j 2832 s 0 n j 2832 GMAND_reverse_b3087 j 2833 d 0 1000 n j 2833 GMHEPAT j 2834 s 0 n j 2834 GMHEPAT_reverse_681cc j 2835 d 0 1000 n j 2835 GMHEPK j 2836 s 0 n j 2836 GMHEPK_reverse_6f80f j 2837 d 0 1000 n j 2837 GMHEPPA j 2838 s 0 n j 2838 GMHEPPA_reverse_7f337 j 2839 d 0 1000 n j 2839 GMPR j 2840 s 0 n j 2840 GMPR_reverse_dd594 j 2841 d 0 1000 n j 2841 GMPS2 j 2842 s 0 n j 2842 GMPS2_reverse_aa6c4 j 2843 d 0 1000 n j 2843 GMPtex j 2844 d 0 1000 n j 2844 GMPtex_reverse_8f9e8 j 2845 d 0 1000 n j 2845 GND j 2846 s 0 n j 2846 GND_reverse_eec5c j 2847 d 0 1000 n j 2847 GNK j 2848 s 0 n j 2848 GNK_reverse_b04ef j 2849 d 0 1000 n j 2849 GOFUCR j 2850 s 0 n j 2850 GOFUCR_reverse_d2648 j 2851 d 0 1000 n j 2851 GP4GH j 2852 s 0 n j 2852 GP4GH_reverse_271d3 j 2853 d 0 1000 n j 2853 GPDDA1 j 2854 s 0 n j 2854 GPDDA1_reverse_306eb j 2855 d 0 1000 n j 2855 GPDDA1pp j 2856 s 0 n j 2856 GPDDA1pp_reverse_761d9 j 2857 d 0 1000 n j 2857 GPDDA2 j 2858 s 0 n j 2858 GPDDA2_reverse_2a1d6 j 2859 d 0 1000 n j 2859 GPDDA2pp j 2860 s 0 n j 2860 GPDDA2pp_reverse_1fc2e j 2861 d 0 1000 n j 2861 GPDDA3 j 2862 s 0 n j 2862 GPDDA3_reverse_f91a3 j 2863 d 0 1000 n j 2863 GPDDA3pp j 2864 s 0 n j 2864 GPDDA3pp_reverse_cb226 j 2865 d 0 1000 n j 2865 GPDDA4 j 2866 s 0 n j 2866 GPDDA4_reverse_bf732 j 2867 d 0 1000 n j 2867 GPDDA4pp j 2868 s 0 n j 2868 GPDDA4pp_reverse_39af6 j 2869 d 0 1000 n j 2869 GPDDA5 j 2870 s 0 n j 2870 GPDDA5_reverse_1db22 j 2871 d 0 1000 n j 2871 GPDDA5pp j 2872 s 0 n j 2872 GPDDA5pp_reverse_87dae j 2873 d 0 1000 n j 2873 GRTT j 2874 s 0 n j 2874 GRTT_reverse_f3afe j 2875 d 0 1000 n j 2875 GRXR j 2876 s 0 n j 2876 GRXR_reverse_e354b j 2877 d 0 1000 n j 2877 GSNK j 2878 s 0 n j 2878 GSNK_reverse_e6c23 j 2879 d 0 1000 n j 2879 GSNt2pp j 2880 s 0 n j 2880 GSNt2pp_reverse_8e537 j 2881 d 0 1000 n j 2881 GSNtex j 2882 d 0 1000 n j 2882 GSNtex_reverse_b0797 j 2883 d 0 1000 n j 2883 GSPMDA j 2884 s 0 n j 2884 GSPMDA_reverse_cebb4 j 2885 d 0 1000 n j 2885 GSPMDS j 2886 s 0 n j 2886 GSPMDS_reverse_d437d j 2887 d 0 1000 n j 2887 GTHOXtex j 2888 d 0 1000 n j 2888 GTHOXtex_reverse_f2d27 j 2889 d 0 1000 n j 2889 GTHOr j 2890 d 0 1000 n j 2890 GTHOr_reverse_8f1f9 j 2891 d 0 1000 n j 2891 GTHPi j 2892 s 0 n j 2892 GTHPi_reverse_0b1e5 j 2893 d 0 1000 n j 2893 GTHRDHpp j 2894 s 0 n j 2894 GTHRDHpp_reverse_1186e j 2895 d 0 1000 n j 2895 GTHRDabc2pp j 2896 s 0 n j 2896 GTHRDabc2pp_reverse_c2215 j 2897 d 0 1000 n j 2897 GTHRDabcpp j 2898 s 0 n j 2898 GTHRDabcpp_reverse_27f15 j 2899 d 0 1000 n j 2899 GTHRDtex j 2900 d 0 1000 n j 2900 GTHRDtex_reverse_3bfd2 j 2901 d 0 1000 n j 2901 GTHS j 2902 s 0 n j 2902 GTHS_reverse_172f9 j 2903 d 0 1000 n j 2903 GTPCI j 2904 s 0 n j 2904 GTPCI_reverse_1ee86 j 2905 d 0 1000 n j 2905 GTPCII2 j 2906 s 0 n j 2906 GTPCII2_reverse_63cd8 j 2907 d 0 1000 n j 2907 GTPDPDP j 2908 s 0 n j 2908 GTPDPDP_reverse_9d492 j 2909 d 0 1000 n j 2909 GTPDPK j 2910 s 0 n j 2910 GTPDPK_reverse_f4450 j 2911 d 0 1000 n j 2911 GTPHs j 2912 s 0 n j 2912 GTPHs_reverse_79d11 j 2913 d 0 1000 n j 2913 GTPtex j 2914 d 0 1000 n j 2914 GTPtex_reverse_cea0b j 2915 d 0 1000 n j 2915 GUACYC j 2916 s 0 n j 2916 GUACYC_reverse_c8366 j 2917 d 0 1000 n j 2917 GUAD j 2918 s 0 n j 2918 GUAD_reverse_6dce3 j 2919 d 0 1000 n j 2919 GUAPRT j 2920 s 0 n j 2920 GUAPRT_reverse_ac1f5 j 2921 d 0 1000 n j 2921 GUAt2pp j 2922 s 0 n j 2922 GUAt2pp_reverse_9e865 j 2923 d 0 1000 n j 2923 GUAtex j 2924 d 0 1000 n j 2924 GUAtex_reverse_9765a j 2925 d 0 1000 n j 2925 GUAtpp j 2926 d 0 1000 n j 2926 GUAtpp_reverse_8f625 j 2927 d 0 1000 n j 2927 GUI1 j 2928 d 0 1000 n j 2928 GUI1_reverse_3d62c j 2929 d 0 1000 n j 2929 GUI2 j 2930 d 0 1000 n j 2930 GUI2_reverse_bb47c j 2931 d 0 1000 n j 2931 GUR1PPpp j 2932 s 0 n j 2932 GUR1PPpp_reverse_b6a53 j 2933 d 0 1000 n j 2933 H2O2tex j 2934 d 0 1000 n j 2934 H2O2tex_reverse_c5e91 j 2935 d 0 1000 n j 2935 H2Otex j 2936 d 0 1000 n j 2936 H2Otex_reverse_57da2 j 2937 d 0 1000 n j 2937 H2Otpp j 2938 d 0 1000 n j 2938 H2Otpp_reverse_01d15 j 2939 d 0 1000 n j 2939 H2SO j 2940 s 0 n j 2940 H2SO_reverse_f76c7 j 2941 d 0 1000 n j 2941 H2St1pp j 2942 s 0 n j 2942 H2St1pp_reverse_d701f j 2943 d 0 1000 n j 2943 H2Stex j 2944 d 0 1000 n j 2944 H2Stex_reverse_67951 j 2945 d 0 1000 n j 2945 H2tex j 2946 d 0 1000 n j 2946 H2tex_reverse_89d9b j 2947 d 0 1000 n j 2947 H2tpp j 2948 d 0 1000 n j 2948 H2tpp_reverse_d5688 j 2949 d 0 1000 n j 2949 HACD1 j 2950 d 0 1000 n j 2950 HACD1_reverse_204fb j 2951 d 0 1000 n j 2951 HACD2 j 2952 d 0 1000 n j 2952 HACD2_reverse_c9c37 j 2953 d 0 1000 n j 2953 HACD3 j 2954 d 0 1000 n j 2954 HACD3_reverse_9961c j 2955 d 0 1000 n j 2955 HACD4 j 2956 d 0 1000 n j 2956 HACD4_reverse_f1c33 j 2957 d 0 1000 n j 2957 HACD5 j 2958 d 0 1000 n j 2958 HACD5_reverse_bd367 j 2959 d 0 1000 n j 2959 HACD6 j 2960 d 0 1000 n j 2960 HACD6_reverse_eec8e j 2961 d 0 1000 n j 2961 HACD7 j 2962 d 0 1000 n j 2962 HACD7_reverse_6a28d j 2963 d 0 1000 n j 2963 HACD8 j 2964 d 0 1000 n j 2964 HACD8_reverse_f3f2b j 2965 d 0 1000 n j 2965 HADPCOADH3 j 2966 d 0 1000 n j 2966 HADPCOADH3_reverse_76ce0 j 2967 d 0 1000 n j 2967 HBZOPT j 2968 s 0 n j 2968 HBZOPT_reverse_ad95f j 2969 d 0 1000 n j 2969 HCINNMt2rpp j 2970 d 0 1000 n j 2970 HCINNMt2rpp_reverse_9b834 j 2971 d 0 1000 n j 2971 HCINNMtex j 2972 d 0 1000 n j 2972 HCINNMtex_reverse_baa63 j 2973 d 0 1000 n j 2973 HCO3E j 2974 d 0 1000 n j 2974 HCO3E_reverse_97ea5 j 2975 d 0 1000 n j 2975 HCYSMT j 2976 s 0 n j 2976 HCYSMT_reverse_5de5b j 2977 d 0 1000 n j 2977 HCYSMT2 j 2978 s 0 n j 2978 HCYSMT2_reverse_a19d5 j 2979 d 0 1000 n j 2979 HDCAtexi j 2980 s 0 n j 2980 HDCAtexi_reverse_86dae j 2981 d 0 1000 n j 2981 HDCEAtexi j 2982 s 0 n j 2982 HDCEAtexi_reverse_ecdf5 j 2983 d 0 1000 n j 2983 HEMEOS j 2984 s 0 n j 2984 HEMEOS_reverse_b63ba j 2985 d 0 1000 n j 2985 HEPK1 j 2986 s 0 n j 2986 HEPK1_reverse_7641a j 2987 d 0 1000 n j 2987 HEPK2 j 2988 s 0 n j 2988 HEPK2_reverse_0f9b1 j 2989 d 0 1000 n j 2989 HEPT1 j 2990 s 0 n j 2990 HEPT1_reverse_da8bc j 2991 d 0 1000 n j 2991 HEPT2 j 2992 s 0 n j 2992 HEPT2_reverse_6039c j 2993 d 0 1000 n j 2993 HEPT3 j 2994 s 0 n j 2994 HEPT3_reverse_23852 j 2995 d 0 1000 n j 2995 HEPT4 j 2996 s 0 n j 2996 HEPT4_reverse_1a6e9 j 2997 d 0 1000 n j 2997 HETZK j 2998 s 0 n j 2998 HETZK_reverse_34f91 j 2999 d 0 1000 n j 2999 HEX1 j 3000 s 0 n j 3000 HEX1_reverse_25efa j 3001 d 0 1000 n j 3001 HEX4 j 3002 s 0 n j 3002 HEX4_reverse_5b8fc j 3003 d 0 1000 n j 3003 HEX7 j 3004 s 0 n j 3004 HEX7_reverse_f7d4e j 3005 d 0 1000 n j 3005 HEXt2rpp j 3006 d 0 1000 n j 3006 HEXt2rpp_reverse_5cf40 j 3007 d 0 1000 n j 3007 HG2abcpp j 3008 s 0 n j 3008 HG2abcpp_reverse_7efc6 j 3009 d 0 1000 n j 3009 HG2t3pp j 3010 s 0 n j 3010 HG2t3pp_reverse_037c0 j 3011 d 0 1000 n j 3011 HG2tex j 3012 d 0 1000 n j 3012 HG2tex_reverse_75fcc j 3013 d 0 1000 n j 3013 HISTD j 3014 s 0 n j 3014 HISTD_reverse_2a63b j 3015 d 0 1000 n j 3015 HISTP j 3016 s 0 n j 3016 HISTP_reverse_5e409 j 3017 d 0 1000 n j 3017 HISTRS j 3018 s 0 n j 3018 HISTRS_reverse_a6df2 j 3019 d 0 1000 n j 3019 HISabcpp j 3020 s 0 n j 3020 HISabcpp_reverse_4e9d8 j 3021 d 0 1000 n j 3021 HISt2rpp j 3022 d 0 1000 n j 3022 HISt2rpp_reverse_f6739 j 3023 d 0 1000 n j 3023 HIStex j 3024 d 0 1000 n j 3024 HIStex_reverse_4d05f j 3025 d 0 1000 n j 3025 HKNDDH j 3026 s 0 n j 3026 HKNDDH_reverse_92167 j 3027 d 0 1000 n j 3027 HKNTDH j 3028 s 0 n j 3028 HKNTDH_reverse_6a5e1 j 3029 d 0 1000 n j 3029 HMBS j 3030 s 0 n j 3030 HMBS_reverse_23a06 j 3031 d 0 1000 n j 3031 HMPK1 j 3032 s 0 n j 3032 HMPK1_reverse_8f692 j 3033 d 0 1000 n j 3033 HOMt2pp j 3034 s 0 n j 3034 HOMt2pp_reverse_6b82d j 3035 d 0 1000 n j 3035 HOMtex j 3036 d 0 1000 n j 3036 HOMtex_reverse_58325 j 3037 d 0 1000 n j 3037 HOPNTAL j 3038 s 0 n j 3038 HOPNTAL_reverse_43031 j 3039 d 0 1000 n j 3039 HPPK2 j 3040 s 0 n j 3040 HPPK2_reverse_9a03f j 3041 d 0 1000 n j 3041 HPPPNDO j 3042 s 0 n j 3042 HPPPNDO_reverse_efb27 j 3043 d 0 1000 n j 3043 HPPPNt2rpp j 3044 d 0 1000 n j 3044 HPPPNt2rpp_reverse_faf7f j 3045 d 0 1000 n j 3045 HPPPNtex j 3046 d 0 1000 n j 3046 HPPPNtex_reverse_5cefc j 3047 d 0 1000 n j 3047 HPYRI j 3048 d 0 1000 n j 3048 HPYRI_reverse_5f20f j 3049 d 0 1000 n j 3049 HPYRRx j 3050 s 0 n j 3050 HPYRRx_reverse_8678f j 3051 d 0 1000 n j 3051 HPYRRy j 3052 s 0 n j 3052 HPYRRy_reverse_197c5 j 3053 d 0 1000 n j 3053 HSDy j 3054 d 0 1000 n j 3054 HSDy_reverse_77ce7 j 3055 d 0 1000 n j 3055 HSK j 3056 s 0 n j 3056 HSK_reverse_e4218 j 3057 d 0 1000 n j 3057 HSST j 3058 s 0 n j 3058 HSST_reverse_a009a j 3059 d 0 1000 n j 3059 HSTPT j 3060 s 0 n j 3060 HSTPT_reverse_b3657 j 3061 d 0 1000 n j 3061 HXAND j 3062 s 0 n j 3062 HXAND_reverse_36555 j 3063 d 0 1000 n j 3063 HXAtex j 3064 d 0 1000 n j 3064 HXAtex_reverse_ba86b j 3065 d 0 1000 n j 3065 HXCT j 3066 s 0 n j 3066 HXCT_reverse_38b4c j 3067 d 0 1000 n j 3067 HXPRT j 3068 s 0 n j 3068 HXPRT_reverse_c7021 j 3069 d 0 1000 n j 3069 HYD1pp j 3070 s 0 n j 3070 HYD1pp_reverse_2792d j 3071 d 0 1000 n j 3071 HYD2pp j 3072 s 0 n j 3072 HYD2pp_reverse_c5002 j 3073 d 0 1000 n j 3073 HYD3pp j 3074 s 0 n j 3074 HYD3pp_reverse_0fbba j 3075 d 0 1000 n j 3075 HYPOE j 3076 s 0 n j 3076 HYPOE_reverse_6571b j 3077 d 0 1000 n j 3077 HYXNtex j 3078 d 0 1000 n j 3078 HYXNtex_reverse_af8af j 3079 d 0 1000 n j 3079 HYXNtpp j 3080 d 0 1000 n j 3080 HYXNtpp_reverse_52a92 j 3081 d 0 1000 n j 3081 Htex j 3082 d 0 1000 n j 3082 Htex_reverse_6f9a4 j 3083 d 0 1000 n j 3083 I2FE2SR j 3084 s 0 n j 3084 I2FE2SR_reverse_25e47 j 3085 d 0 1000 n j 3085 I2FE2SS j 3086 s 0 n j 3086 I2FE2SS_reverse_8ced0 j 3087 d 0 1000 n j 3087 I2FE2SS2 j 3088 s 0 n j 3088 I2FE2SS2_reverse_e0613 j 3089 d 0 1000 n j 3089 I2FE2ST j 3090 s 0 n j 3090 I2FE2ST_reverse_7fd6b j 3091 d 0 1000 n j 3091 I4FE4SR j 3092 s 0 n j 3092 I4FE4SR_reverse_eee61 j 3093 d 0 1000 n j 3093 I4FE4ST j 3094 s 0 n j 3094 I4FE4ST_reverse_a78a4 j 3095 d 0 1000 n j 3095 ICDHyr j 3096 d 0 1000 n j 3096 ICDHyr_reverse_7f84b j 3097 d 0 1000 n j 3097 ICHORS_copy1 j 3098 d 0 1000 n j 3098 ICHORS_copy1_reverse_31425 j 3099 d 0 1000 n j 3099 ICHORS_copy2 j 3100 s 0 n j 3100 ICHORS_copy2_reverse_fd9ab j 3101 d 0 1000 n j 3101 ICHORT j 3102 s 0 n j 3102 ICHORT_reverse_743ed j 3103 d 0 1000 n j 3103 ICL j 3104 s 0 n j 3104 ICL_reverse_2f27e j 3105 d 0 1000 n j 3105 ICYSDS j 3106 s 0 n j 3106 ICYSDS_reverse_1e758 j 3107 d 0 1000 n j 3107 IDOND j 3108 d 0 1000 n j 3108 IDOND_reverse_b2be5 j 3109 d 0 1000 n j 3109 IDOND2 j 3110 s 0 n j 3110 IDOND2_reverse_a93f5 j 3111 d 0 1000 n j 3111 IDONt2rpp j 3112 d 0 1000 n j 3112 IDONt2rpp_reverse_b5eee j 3113 d 0 1000 n j 3113 IDONtex j 3114 d 0 1000 n j 3114 IDONtex_reverse_efc44 j 3115 d 0 1000 n j 3115 IG3PS j 3116 s 0 n j 3116 IG3PS_reverse_12008 j 3117 d 0 1000 n j 3117 IGPDH j 3118 s 0 n j 3118 IGPDH_reverse_b1a3c j 3119 d 0 1000 n j 3119 IGPS j 3120 s 0 n j 3120 IGPS_reverse_feb80 j 3121 d 0 1000 n j 3121 ILETA j 3122 d 0 1000 n j 3122 ILETA_reverse_aec70 j 3123 d 0 1000 n j 3123 ILETRS j 3124 s 0 n j 3124 ILETRS_reverse_02878 j 3125 d 0 1000 n j 3125 ILEabcpp j 3126 s 0 n j 3126 ILEabcpp_reverse_a3857 j 3127 d 0 1000 n j 3127 ILEt2rpp j 3128 d 0 1000 n j 3128 ILEt2rpp_reverse_b0484 j 3129 d 0 1000 n j 3129 ILEtex j 3130 d 0 1000 n j 3130 ILEtex_reverse_d95d1 j 3131 d 0 1000 n j 3131 IMPC j 3132 d 0 1000 n j 3132 IMPC_reverse_efa41 j 3133 d 0 1000 n j 3133 IMPD j 3134 s 0 n j 3134 IMPD_reverse_6e625 j 3135 d 0 1000 n j 3135 IMPtex j 3136 d 0 1000 n j 3136 IMPtex_reverse_014c6 j 3137 d 0 1000 n j 3137 INDOLEt2pp j 3138 s 0 n j 3138 INDOLEt2pp_reverse_6a69a j 3139 d 0 1000 n j 3139 INDOLEt2rpp j 3140 d 0 1000 n j 3140 INDOLEt2rpp_reverse_67d3f j 3141 d 0 1000 n j 3141 INDOLEtex j 3142 d 0 1000 n j 3142 INDOLEtex_reverse_8bf71 j 3143 d 0 1000 n j 3143 INOSTt4pp j 3144 s 0 n j 3144 INOSTt4pp_reverse_0b7d9 j 3145 d 0 1000 n j 3145 INSH j 3146 s 0 n j 3146 INSH_reverse_e84bd j 3147 d 0 1000 n j 3147 INSK j 3148 s 0 n j 3148 INSK_reverse_93565 j 3149 d 0 1000 n j 3149 INSTtex j 3150 d 0 1000 n j 3150 INSTtex_reverse_583d8 j 3151 d 0 1000 n j 3151 INSt2pp_copy1 j 3152 s 0 n j 3152 INSt2pp_copy1_reverse_909b3 j 3153 d 0 1000 n j 3153 INSt2pp_copy2 j 3154 d 0 1000 n j 3154 INSt2pp_copy2_reverse_ff628 j 3155 d 0 1000 n j 3155 INStex j 3156 d 0 1000 n j 3156 INStex_reverse_06a7c j 3157 d 0 1000 n j 3157 IPDDI j 3158 d 0 1000 n j 3158 IPDDI_reverse_6c5f9 j 3159 d 0 1000 n j 3159 IPDPS j 3160 s 0 n j 3160 IPDPS_reverse_baaf9 j 3161 d 0 1000 n j 3161 IPMD j 3162 s 0 n j 3162 IPMD_reverse_d7a5e j 3163 d 0 1000 n j 3163 IPPMIa j 3164 d 0 1000 n j 3164 IPPMIa_reverse_0594d j 3165 d 0 1000 n j 3165 IPPMIb j 3166 d 0 1000 n j 3166 IPPMIb_reverse_e37a1 j 3167 d 0 1000 n j 3167 IPPS j 3168 s 0 n j 3168 IPPS_reverse_d94c0 j 3169 d 0 1000 n j 3169 ISETACabcpp j 3170 s 0 n j 3170 ISETACabcpp_reverse_cbd54 j 3171 d 0 1000 n j 3171 ISETACtex j 3172 d 0 1000 n j 3172 ISETACtex_reverse_3abad j 3173 d 0 1000 n j 3173 K2L4Aabcpp j 3174 s 0 n j 3174 K2L4Aabcpp_reverse_ff31a j 3175 d 0 1000 n j 3175 K2L4Aabctex j 3176 s 0 n j 3176 K2L4Aabctex_reverse_27549 j 3177 d 0 1000 n j 3177 KARA1 j 3178 d 0 1000 n j 3178 KARA1_reverse_2b971 j 3179 d 0 1000 n j 3179 KARA2 j 3180 d 0 1000 n j 3180 KARA2_reverse_65e99 j 3181 d 0 1000 n j 3181 KAS14 j 3182 s 0 n j 3182 KAS14_reverse_25582 j 3183 d 0 1000 n j 3183 KAS15 j 3184 s 0 n j 3184 KAS15_reverse_6f7fb j 3185 d 0 1000 n j 3185 KDOCT2 j 3186 s 0 n j 3186 KDOCT2_reverse_b2fcd j 3187 d 0 1000 n j 3187 KDOPP j 3188 s 0 n j 3188 KDOPP_reverse_c38fd j 3189 d 0 1000 n j 3189 KDOPS j 3190 s 0 n j 3190 KDOPS_reverse_d4842 j 3191 d 0 1000 n j 3191 KG6PDC j 3192 s 0 n j 3192 KG6PDC_reverse_9de4b j 3193 d 0 1000 n j 3193 Kabcpp j 3194 s 0 n j 3194 Kabcpp_reverse_35f86 j 3195 d 0 1000 n j 3195 Kt2pp j 3196 s 0 n j 3196 Kt2pp_reverse_5687c j 3197 d 0 1000 n j 3197 Kt3pp j 3198 s 0 n j 3198 Kt3pp_reverse_63b11 j 3199 d 0 1000 n j 3199 Ktex j 3200 d 0 1000 n j 3200 Ktex_reverse_03b32 j 3201 d 0 1000 n j 3201 L_LACD2 j 3202 s 0 n j 3202 L_LACD2_reverse_31758 j 3203 d 0 1000 n j 3203 L_LACD3 j 3204 s 0 n j 3204 L_LACD3_reverse_d3a1b j 3205 d 0 1000 n j 3205 L_LACt2rpp j 3206 d 0 1000 n j 3206 L_LACt2rpp_reverse_9d5df j 3207 d 0 1000 n j 3207 L_LACtex j 3208 d 0 1000 n j 3208 L_LACtex_reverse_7f0b4 j 3209 d 0 1000 n j 3209 LA4NTpp j 3210 s 0 n j 3210 LA4NTpp_reverse_2ba26 j 3211 d 0 1000 n j 3211 LACZ j 3212 s 0 n j 3212 LACZ_reverse_f28e7 j 3213 d 0 1000 n j 3213 LACZpp j 3214 s 0 n j 3214 LACZpp_reverse_2b3b0 j 3215 d 0 1000 n j 3215 LADGMDH j 3216 s 0 n j 3216 LADGMDH_reverse_f7b48 j 3217 d 0 1000 n j 3217 LALADGLUtex j 3218 d 0 1000 n j 3218 LALADGLUtex_reverse_34575 j 3219 d 0 1000 n j 3219 LALADGLUtpp j 3220 s 0 n j 3220 LALADGLUtpp_reverse_3421b j 3221 d 0 1000 n j 3221 LALALGLUtex j 3222 d 0 1000 n j 3222 LALALGLUtex_reverse_0d516 j 3223 d 0 1000 n j 3223 LALALGLUtpp j 3224 s 0 n j 3224 LALALGLUtpp_reverse_fc27f j 3225 d 0 1000 n j 3225 LALDO2x j 3226 s 0 n j 3226 LALDO2x_reverse_97d24 j 3227 d 0 1000 n j 3227 LALDO3 j 3228 s 0 n j 3228 LALDO3_reverse_60a42 j 3229 d 0 1000 n j 3229 LALGP j 3230 s 0 n j 3230 LALGP_reverse_4d1aa j 3231 d 0 1000 n j 3231 LCADi j 3232 s 0 n j 3232 LCADi_reverse_58cdc j 3233 d 0 1000 n j 3233 LCARR j 3234 d 0 1000 n j 3234 LCARR_reverse_9213d j 3235 d 0 1000 n j 3235 LCARS j 3236 d 0 1000 n j 3236 LCARS_reverse_66c3d j 3237 d 0 1000 n j 3237 LCTSt3ipp j 3238 s 0 n j 3238 LCTSt3ipp_reverse_06c0c j 3239 d 0 1000 n j 3239 LCTStex j 3240 d 0 1000 n j 3240 LCTStex_reverse_b72e4 j 3241 d 0 1000 n j 3241 LCTStpp j 3242 d 0 1000 n j 3242 LCTStpp_reverse_9f21b j 3243 d 0 1000 n j 3243 LDH_D j 3244 d 0 1000 n j 3244 LDH_D_reverse_f8507 j 3245 d 0 1000 n j 3245 LDH_D2 j 3246 s 0 n j 3246 LDH_D2_reverse_92e29 j 3247 d 0 1000 n j 3247 LEUTAi j 3248 s 0 n j 3248 LEUTAi_reverse_0ec8d j 3249 d 0 1000 n j 3249 LEUTRS j 3250 s 0 n j 3250 LEUTRS_reverse_06175 j 3251 d 0 1000 n j 3251 LEUabcpp j 3252 s 0 n j 3252 LEUabcpp_reverse_ab30a j 3253 d 0 1000 n j 3253 LEUt2rpp j 3254 d 0 1000 n j 3254 LEUt2rpp_reverse_96e34 j 3255 d 0 1000 n j 3255 LEUtex j 3256 d 0 1000 n j 3256 LEUtex_reverse_a9685 j 3257 d 0 1000 n j 3257 LGTHL j 3258 s 0 n j 3258 LGTHL_reverse_c8eb0 j 3259 d 0 1000 n j 3259 LIPACabcpp j 3260 s 0 n j 3260 LIPACabcpp_reverse_9aced j 3261 d 0 1000 n j 3261 LIPAHT2ex j 3262 s 0 n j 3262 LIPAHT2ex_reverse_d08dd j 3263 d 0 1000 n j 3263 LIPAHTex j 3264 s 0 n j 3264 LIPAHTex_reverse_306bf j 3265 d 0 1000 n j 3265 LIPAMPL j 3266 s 0 n j 3266 LIPAMPL_reverse_71584 j 3267 d 0 1000 n j 3267 LIPATPT j 3268 s 0 n j 3268 LIPATPT_reverse_e209a j 3269 d 0 1000 n j 3269 LIPAabcpp j 3270 s 0 n j 3270 LIPAabcpp_reverse_26807 j 3271 d 0 1000 n j 3271 LIPAabctex j 3272 s 0 n j 3272 LIPAabctex_reverse_d1e02 j 3273 d 0 1000 n j 3273 LIPOCT j 3274 s 0 n j 3274 LIPOCT_reverse_0078e j 3275 d 0 1000 n j 3275 LIPOS j 3276 s 0 n j 3276 LIPOS_reverse_cefb0 j 3277 d 0 1000 n j 3277 LIPOt2pp j 3278 s 0 n j 3278 LIPOt2pp_reverse_d1447 j 3279 d 0 1000 n j 3279 LIPOtex j 3280 d 0 1000 n j 3280 LIPOtex_reverse_2ec74 j 3281 d 0 1000 n j 3281 LPADSS j 3282 s 0 n j 3282 LPADSS_reverse_a2b94 j 3283 d 0 1000 n j 3283 LPLIPAL1A120pp j 3284 s 0 n j 3284 LPLIPAL1A120pp_reverse_c4d72 j 3285 d 0 1000 n j 3285 LPLIPAL1A140pp j 3286 s 0 n j 3286 LPLIPAL1A140pp_reverse_675c8 j 3287 d 0 1000 n j 3287 LPLIPAL1A141pp j 3288 s 0 n j 3288 LPLIPAL1A141pp_reverse_d3730 j 3289 d 0 1000 n j 3289 LPLIPAL1A160pp j 3290 s 0 n j 3290 LPLIPAL1A160pp_reverse_e3b7b j 3291 d 0 1000 n j 3291 LPLIPAL1A161pp j 3292 s 0 n j 3292 LPLIPAL1A161pp_reverse_d6287 j 3293 d 0 1000 n j 3293 LPLIPAL1A180pp j 3294 s 0 n j 3294 LPLIPAL1A180pp_reverse_e29e8 j 3295 d 0 1000 n j 3295 LPLIPAL1A181pp j 3296 s 0 n j 3296 LPLIPAL1A181pp_reverse_94417 j 3297 d 0 1000 n j 3297 LPLIPAL1E120pp j 3298 s 0 n j 3298 LPLIPAL1E120pp_reverse_ba0a2 j 3299 d 0 1000 n j 3299 LPLIPAL1E140pp j 3300 s 0 n j 3300 LPLIPAL1E140pp_reverse_fa8c9 j 3301 d 0 1000 n j 3301 LPLIPAL1E141pp j 3302 s 0 n j 3302 LPLIPAL1E141pp_reverse_afa00 j 3303 d 0 1000 n j 3303 LPLIPAL1E160pp j 3304 s 0 n j 3304 LPLIPAL1E160pp_reverse_b1637 j 3305 d 0 1000 n j 3305 LPLIPAL1E161pp j 3306 s 0 n j 3306 LPLIPAL1E161pp_reverse_51c71 j 3307 d 0 1000 n j 3307 LPLIPAL1E180pp j 3308 s 0 n j 3308 LPLIPAL1E180pp_reverse_841f1 j 3309 d 0 1000 n j 3309 LPLIPAL1E181pp j 3310 s 0 n j 3310 LPLIPAL1E181pp_reverse_add33 j 3311 d 0 1000 n j 3311 LPLIPAL1G120pp j 3312 s 0 n j 3312 LPLIPAL1G120pp_reverse_6056c j 3313 d 0 1000 n j 3313 LPLIPAL1G140pp j 3314 s 0 n j 3314 LPLIPAL1G140pp_reverse_9d9e7 j 3315 d 0 1000 n j 3315 LPLIPAL1G141pp j 3316 s 0 n j 3316 LPLIPAL1G141pp_reverse_30b2b j 3317 d 0 1000 n j 3317 LPLIPAL1G160pp j 3318 s 0 n j 3318 LPLIPAL1G160pp_reverse_6ab0f j 3319 d 0 1000 n j 3319 LPLIPAL1G161pp j 3320 s 0 n j 3320 LPLIPAL1G161pp_reverse_aecac j 3321 d 0 1000 n j 3321 LPLIPAL1G180pp j 3322 s 0 n j 3322 LPLIPAL1G180pp_reverse_9b51e j 3323 d 0 1000 n j 3323 LPLIPAL1G181pp j 3324 s 0 n j 3324 LPLIPAL1G181pp_reverse_c46f5 j 3325 d 0 1000 n j 3325 LPLIPAL2A120 j 3326 s 0 n j 3326 LPLIPAL2A120_reverse_844c0 j 3327 d 0 1000 n j 3327 LPLIPAL2A140 j 3328 s 0 n j 3328 LPLIPAL2A140_reverse_6e1ff j 3329 d 0 1000 n j 3329 LPLIPAL2A141 j 3330 s 0 n j 3330 LPLIPAL2A141_reverse_12ad1 j 3331 d 0 1000 n j 3331 LPLIPAL2A160 j 3332 s 0 n j 3332 LPLIPAL2A160_reverse_b2af0 j 3333 d 0 1000 n j 3333 LPLIPAL2A161 j 3334 s 0 n j 3334 LPLIPAL2A161_reverse_37df8 j 3335 d 0 1000 n j 3335 LPLIPAL2A180 j 3336 s 0 n j 3336 LPLIPAL2A180_reverse_dba15 j 3337 d 0 1000 n j 3337 LPLIPAL2A181 j 3338 s 0 n j 3338 LPLIPAL2A181_reverse_8b966 j 3339 d 0 1000 n j 3339 LPLIPAL2ATE120 j 3340 s 0 n j 3340 LPLIPAL2ATE120_reverse_deb80 j 3341 d 0 1000 n j 3341 LPLIPAL2ATE140 j 3342 s 0 n j 3342 LPLIPAL2ATE140_reverse_0c69d j 3343 d 0 1000 n j 3343 LPLIPAL2ATE141 j 3344 s 0 n j 3344 LPLIPAL2ATE141_reverse_eae4b j 3345 d 0 1000 n j 3345 LPLIPAL2ATE160 j 3346 s 0 n j 3346 LPLIPAL2ATE160_reverse_bcf82 j 3347 d 0 1000 n j 3347 LPLIPAL2ATE161 j 3348 s 0 n j 3348 LPLIPAL2ATE161_reverse_a516f j 3349 d 0 1000 n j 3349 LPLIPAL2ATE180 j 3350 s 0 n j 3350 LPLIPAL2ATE180_reverse_cce82 j 3351 d 0 1000 n j 3351 LPLIPAL2ATE181 j 3352 s 0 n j 3352 LPLIPAL2ATE181_reverse_e8332 j 3353 d 0 1000 n j 3353 LPLIPAL2ATG120 j 3354 s 0 n j 3354 LPLIPAL2ATG120_reverse_9d5c1 j 3355 d 0 1000 n j 3355 LPLIPAL2ATG140 j 3356 s 0 n j 3356 LPLIPAL2ATG140_reverse_e2a65 j 3357 d 0 1000 n j 3357 LPLIPAL2ATG141 j 3358 s 0 n j 3358 LPLIPAL2ATG141_reverse_7ddf2 j 3359 d 0 1000 n j 3359 LPLIPAL2ATG160 j 3360 s 0 n j 3360 LPLIPAL2ATG160_reverse_8358e j 3361 d 0 1000 n j 3361 LPLIPAL2ATG161 j 3362 s 0 n j 3362 LPLIPAL2ATG161_reverse_1cac0 j 3363 d 0 1000 n j 3363 LPLIPAL2ATG180 j 3364 s 0 n j 3364 LPLIPAL2ATG180_reverse_d5e49 j 3365 d 0 1000 n j 3365 LPLIPAL2ATG181 j 3366 s 0 n j 3366 LPLIPAL2ATG181_reverse_0d22a j 3367 d 0 1000 n j 3367 LPLIPAL2E120 j 3368 s 0 n j 3368 LPLIPAL2E120_reverse_f1aae j 3369 d 0 1000 n j 3369 LPLIPAL2E140 j 3370 s 0 n j 3370 LPLIPAL2E140_reverse_075ab j 3371 d 0 1000 n j 3371 LPLIPAL2E141 j 3372 s 0 n j 3372 LPLIPAL2E141_reverse_3ee47 j 3373 d 0 1000 n j 3373 LPLIPAL2E160 j 3374 s 0 n j 3374 LPLIPAL2E160_reverse_ad528 j 3375 d 0 1000 n j 3375 LPLIPAL2E161 j 3376 s 0 n j 3376 LPLIPAL2E161_reverse_e9be3 j 3377 d 0 1000 n j 3377 LPLIPAL2E180 j 3378 s 0 n j 3378 LPLIPAL2E180_reverse_022a8 j 3379 d 0 1000 n j 3379 LPLIPAL2E181 j 3380 s 0 n j 3380 LPLIPAL2E181_reverse_1c193 j 3381 d 0 1000 n j 3381 LPLIPAL2G120 j 3382 s 0 n j 3382 LPLIPAL2G120_reverse_68d19 j 3383 d 0 1000 n j 3383 LPLIPAL2G140 j 3384 s 0 n j 3384 LPLIPAL2G140_reverse_780ce j 3385 d 0 1000 n j 3385 LPLIPAL2G141 j 3386 s 0 n j 3386 LPLIPAL2G141_reverse_2459e j 3387 d 0 1000 n j 3387 LPLIPAL2G160 j 3388 s 0 n j 3388 LPLIPAL2G160_reverse_54863 j 3389 d 0 1000 n j 3389 LPLIPAL2G161 j 3390 s 0 n j 3390 LPLIPAL2G161_reverse_9a980 j 3391 d 0 1000 n j 3391 LPLIPAL2G180 j 3392 s 0 n j 3392 LPLIPAL2G180_reverse_116f7 j 3393 d 0 1000 n j 3393 LPLIPAL2G181 j 3394 s 0 n j 3394 LPLIPAL2G181_reverse_3cf24 j 3395 d 0 1000 n j 3395 LSERDHr j 3396 d 0 1000 n j 3396 LSERDHr_reverse_7bbae j 3397 d 0 1000 n j 3397 LYSDC j 3398 s 0 n j 3398 LYSDC_reverse_d9eb6 j 3399 d 0 1000 n j 3399 LYSTRS j 3400 s 0 n j 3400 LYSTRS_reverse_d3497 j 3401 d 0 1000 n j 3401 LYSabcpp j 3402 s 0 n j 3402 LYSabcpp_reverse_b8184 j 3403 d 0 1000 n j 3403 LYSt2pp j 3404 s 0 n j 3404 LYSt2pp_reverse_3bd5c j 3405 d 0 1000 n j 3405 LYSt3pp j 3406 s 0 n j 3406 LYSt3pp_reverse_e2d78 j 3407 d 0 1000 n j 3407 LYStex j 3408 d 0 1000 n j 3408 LYStex_reverse_a5886 j 3409 d 0 1000 n j 3409 LYXI j 3410 s 0 n j 3410 LYXI_reverse_16c69 j 3411 d 0 1000 n j 3411 LYXt2pp j 3412 s 0 n j 3412 LYXt2pp_reverse_946d1 j 3413 d 0 1000 n j 3413 LYXtex j 3414 d 0 1000 n j 3414 LYXtex_reverse_8deea j 3415 d 0 1000 n j 3415 M1PD j 3416 d 0 1000 n j 3416 M1PD_reverse_914a8 j 3417 d 0 1000 n j 3417 MACPD j 3418 s 0 n j 3418 MACPD_reverse_57f90 j 3419 d 0 1000 n j 3419 MALCOAMT j 3420 s 0 n j 3420 MALCOAMT_reverse_1031e j 3421 d 0 1000 n j 3421 MALDDH j 3422 s 0 n j 3422 MALDDH_reverse_c5287 j 3423 d 0 1000 n j 3423 MALDt2_2pp j 3424 s 0 n j 3424 MALDt2_2pp_reverse_bdc53 j 3425 d 0 1000 n j 3425 MALDtex j 3426 d 0 1000 n j 3426 MALDtex_reverse_10a51 j 3427 d 0 1000 n j 3427 MALS j 3428 s 0 n j 3428 MALS_reverse_d7382 j 3429 d 0 1000 n j 3429 MALTATr j 3430 d 0 1000 n j 3430 MALTATr_reverse_7153a j 3431 d 0 1000 n j 3431 MALTHXabcpp j 3432 s 0 n j 3432 MALTHXabcpp_reverse_db4fe j 3433 d 0 1000 n j 3433 MALTHXtexi j 3434 s 0 n j 3434 MALTHXtexi_reverse_f16e3 j 3435 d 0 1000 n j 3435 MALTPTabcpp j 3436 s 0 n j 3436 MALTPTabcpp_reverse_2d651 j 3437 d 0 1000 n j 3437 MALTPTtexi j 3438 s 0 n j 3438 MALTPTtexi_reverse_926cf j 3439 d 0 1000 n j 3439 MALTTRabcpp j 3440 s 0 n j 3440 MALTTRabcpp_reverse_82fd8 j 3441 d 0 1000 n j 3441 MALTTRtexi j 3442 s 0 n j 3442 MALTTRtexi_reverse_c76b5 j 3443 d 0 1000 n j 3443 MALTTTRabcpp j 3444 s 0 n j 3444 MALTTTRabcpp_reverse_2e7d0 j 3445 d 0 1000 n j 3445 MALTTTRtexi j 3446 s 0 n j 3446 MALTTTRtexi_reverse_03fc4 j 3447 d 0 1000 n j 3447 MALTabcpp j 3448 s 0 n j 3448 MALTabcpp_reverse_6c8be j 3449 d 0 1000 n j 3449 MALTptspp j 3450 s 0 n j 3450 MALTptspp_reverse_1cf27 j 3451 d 0 1000 n j 3451 MALTtexi j 3452 s 0 n j 3452 MALTtexi_reverse_b9839 j 3453 d 0 1000 n j 3453 MALt2_2pp j 3454 s 0 n j 3454 MALt2_2pp_reverse_b55c3 j 3455 d 0 1000 n j 3455 MALt2_3pp j 3456 s 0 n j 3456 MALt2_3pp_reverse_38522 j 3457 d 0 1000 n j 3457 MALt3pp j 3458 s 0 n j 3458 MALt3pp_reverse_90fc9 j 3459 d 0 1000 n j 3459 MALtex j 3460 d 0 1000 n j 3460 MALtex_reverse_5ca30 j 3461 d 0 1000 n j 3461 MAN1PT2 j 3462 s 0 n j 3462 MAN1PT2_reverse_861e0 j 3463 d 0 1000 n j 3463 MAN6PI j 3464 d 0 1000 n j 3464 MAN6PI_reverse_d96f0 j 3465 d 0 1000 n j 3465 MAN6Pt6_2pp j 3466 s 0 n j 3466 MAN6Pt6_2pp_reverse_b70c0 j 3467 d 0 1000 n j 3467 MAN6Ptex j 3468 d 0 1000 n j 3468 MAN6Ptex_reverse_783f8 j 3469 d 0 1000 n j 3469 MANAO j 3470 d 0 1000 n j 3470 MANAO_reverse_5cec0 j 3471 d 0 1000 n j 3471 MANGLYCptspp j 3472 s 0 n j 3472 MANGLYCptspp_reverse_6186a j 3473 d 0 1000 n j 3473 MANGLYCtex j 3474 d 0 1000 n j 3474 MANGLYCtex_reverse_ae4c5 j 3475 d 0 1000 n j 3475 MANPGH j 3476 s 0 n j 3476 MANPGH_reverse_e9d3e j 3477 d 0 1000 n j 3477 MANptspp j 3478 s 0 n j 3478 MANptspp_reverse_31b36 j 3479 d 0 1000 n j 3479 MANtex j 3480 d 0 1000 n j 3480 MANtex_reverse_87c2f j 3481 d 0 1000 n j 3481 MCITD j 3482 s 0 n j 3482 MCITD_reverse_431b1 j 3483 d 0 1000 n j 3483 MCITL2 j 3484 d 0 1000 n j 3484 MCITL2_reverse_5d403 j 3485 d 0 1000 n j 3485 MCITS j 3486 s 0 n j 3486 MCITS_reverse_21c9c j 3487 d 0 1000 n j 3487 MCOATA j 3488 d 0 1000 n j 3488 MCOATA_reverse_d10f2 j 3489 d 0 1000 n j 3489 MCPST j 3490 s 0 n j 3490 MCPST_reverse_c1773 j 3491 d 0 1000 n j 3491 MCTP1App j 3492 s 0 n j 3492 MCTP1App_reverse_33fae j 3493 d 0 1000 n j 3493 MCTP1Bpp j 3494 s 0 n j 3494 MCTP1Bpp_reverse_801d1 j 3495 d 0 1000 n j 3495 MCTP2App j 3496 s 0 n j 3496 MCTP2App_reverse_ab790 j 3497 d 0 1000 n j 3497 MDDCP1pp j 3498 s 0 n j 3498 MDDCP1pp_reverse_77f34 j 3499 d 0 1000 n j 3499 MDDCP2pp j 3500 s 0 n j 3500 MDDCP2pp_reverse_16437 j 3501 d 0 1000 n j 3501 MDDCP3pp j 3502 s 0 n j 3502 MDDCP3pp_reverse_def42 j 3503 d 0 1000 n j 3503 MDDCP4pp j 3504 s 0 n j 3504 MDDCP4pp_reverse_76a1f j 3505 d 0 1000 n j 3505 MDDCP5pp j 3506 s 0 n j 3506 MDDCP5pp_reverse_fd1dc j 3507 d 0 1000 n j 3507 MDDEP1pp j 3508 s 0 n j 3508 MDDEP1pp_reverse_6e8fc j 3509 d 0 1000 n j 3509 MDDEP2pp j 3510 s 0 n j 3510 MDDEP2pp_reverse_952c4 j 3511 d 0 1000 n j 3511 MDDEP3pp j 3512 s 0 n j 3512 MDDEP3pp_reverse_99f89 j 3513 d 0 1000 n j 3513 MDDEP4pp j 3514 s 0 n j 3514 MDDEP4pp_reverse_6d84a j 3515 d 0 1000 n j 3515 MDH j 3516 d 0 1000 n j 3516 MDH_reverse_ee52c j 3517 d 0 1000 n j 3517 MDH2 j 3518 s 0 n j 3518 MDH2_reverse_d8905 j 3519 d 0 1000 n j 3519 MDH3 j 3520 s 0 n j 3520 MDH3_reverse_15f5e j 3521 d 0 1000 n j 3521 ME1 j 3522 s 0 n j 3522 ME1_reverse_9736c j 3523 d 0 1000 n j 3523 ME2 j 3524 s 0 n j 3524 ME2_reverse_2b0a2 j 3525 d 0 1000 n j 3525 MECDPDH5 j 3526 s 0 n j 3526 MECDPDH5_reverse_f6cae j 3527 d 0 1000 n j 3527 MECDPS j 3528 s 0 n j 3528 MECDPS_reverse_8baa5 j 3529 d 0 1000 n j 3529 MELIBt2pp j 3530 s 0 n j 3530 MELIBt2pp_reverse_41d5d j 3531 d 0 1000 n j 3531 MELIBt3ipp j 3532 s 0 n j 3532 MELIBt3ipp_reverse_83e68 j 3533 d 0 1000 n j 3533 MELIBtex j 3534 d 0 1000 n j 3534 MELIBtex_reverse_35489 j 3535 d 0 1000 n j 3535 MEOHtex j 3536 d 0 1000 n j 3536 MEOHtex_reverse_fb32d j 3537 d 0 1000 n j 3537 MEOHtrpp j 3538 d 0 1000 n j 3538 MEOHtrpp_reverse_3d00f j 3539 d 0 1000 n j 3539 MEPCT j 3540 s 0 n j 3540 MEPCT_reverse_de97a j 3541 d 0 1000 n j 3541 METAT j 3542 s 0 n j 3542 METAT_reverse_793ef j 3543 d 0 1000 n j 3543 METDabcpp j 3544 s 0 n j 3544 METDabcpp_reverse_5e6d9 j 3545 d 0 1000 n j 3545 METDtex j 3546 d 0 1000 n j 3546 METDtex_reverse_a7b32 j 3547 d 0 1000 n j 3547 METOX1s j 3548 s 0 n j 3548 METOX1s_reverse_d3bca j 3549 d 0 1000 n j 3549 METOX2s j 3550 s 0 n j 3550 METOX2s_reverse_21cff j 3551 d 0 1000 n j 3551 METS j 3552 s 0 n j 3552 METS_reverse_af81e j 3553 d 0 1000 n j 3553 METSOX1abcpp j 3554 s 0 n j 3554 METSOX1abcpp_reverse_86d41 j 3555 d 0 1000 n j 3555 METSOX1tex j 3556 d 0 1000 n j 3556 METSOX1tex_reverse_61d34 j 3557 d 0 1000 n j 3557 METSOX2abcpp j 3558 s 0 n j 3558 METSOX2abcpp_reverse_17619 j 3559 d 0 1000 n j 3559 METSOX2tex j 3560 d 0 1000 n j 3560 METSOX2tex_reverse_27fe0 j 3561 d 0 1000 n j 3561 METSOXR1 j 3562 s 0 n j 3562 METSOXR1_reverse_1f950 j 3563 d 0 1000 n j 3563 METSOXR2 j 3564 s 0 n j 3564 METSOXR2_reverse_18064 j 3565 d 0 1000 n j 3565 METTRS j 3566 s 0 n j 3566 METTRS_reverse_d6cd0 j 3567 d 0 1000 n j 3567 METabcpp j 3568 s 0 n j 3568 METabcpp_reverse_3d065 j 3569 d 0 1000 n j 3569 METtex j 3570 d 0 1000 n j 3570 METtex_reverse_3ac81 j 3571 d 0 1000 n j 3571 MG2t3_2pp j 3572 d 0 1000 n j 3572 MG2t3_2pp_reverse_f9aa3 j 3573 d 0 1000 n j 3573 MG2tex j 3574 d 0 1000 n j 3574 MG2tex_reverse_a1983 j 3575 d 0 1000 n j 3575 MG2tpp j 3576 s 0 n j 3576 MG2tpp_reverse_85d82 j 3577 d 0 1000 n j 3577 MG2uabcpp j 3578 s 0 n j 3578 MG2uabcpp_reverse_adeed j 3579 d 0 1000 n j 3579 MGSA j 3580 s 0 n j 3580 MGSA_reverse_ca5f7 j 3581 d 0 1000 n j 3581 MI1PP j 3582 s 0 n j 3582 MI1PP_reverse_76aa8 j 3583 d 0 1000 n j 3583 MICITDr j 3584 d 0 1000 n j 3584 MICITDr_reverse_9d582 j 3585 d 0 1000 n j 3585 MINCYCtex j 3586 d 0 1000 n j 3586 MINCYCtex_reverse_639c8 j 3587 d 0 1000 n j 3587 MINCYCtpp j 3588 s 0 n j 3588 MINCYCtpp_reverse_bd414 j 3589 d 0 1000 n j 3589 MINOHPtexi j 3590 s 0 n j 3590 MINOHPtexi_reverse_afc75 j 3591 d 0 1000 n j 3591 MLDCP1App j 3592 s 0 n j 3592 MLDCP1App_reverse_96701 j 3593 d 0 1000 n j 3593 MLDCP1Bpp j 3594 s 0 n j 3594 MLDCP1Bpp_reverse_5a028 j 3595 d 0 1000 n j 3595 MLDCP2App j 3596 s 0 n j 3596 MLDCP2App_reverse_ab200 j 3597 d 0 1000 n j 3597 MLDCP2Bpp j 3598 s 0 n j 3598 MLDCP2Bpp_reverse_14d0a j 3599 d 0 1000 n j 3599 MLDCP3App j 3600 s 0 n j 3600 MLDCP3App_reverse_cb2dc j 3601 d 0 1000 n j 3601 MLDEP1pp j 3602 s 0 n j 3602 MLDEP1pp_reverse_3a4b7 j 3603 d 0 1000 n j 3603 MLDEP2pp j 3604 s 0 n j 3604 MLDEP2pp_reverse_d46ea j 3605 d 0 1000 n j 3605 MLTG1 j 3606 s 0 n j 3606 MLTG1_reverse_807e4 j 3607 d 0 1000 n j 3607 MLTG2 j 3608 s 0 n j 3608 MLTG2_reverse_8ae5d j 3609 d 0 1000 n j 3609 MLTG3 j 3610 s 0 n j 3610 MLTG3_reverse_4bea7 j 3611 d 0 1000 n j 3611 MLTG4 j 3612 s 0 n j 3612 MLTG4_reverse_c7c2e j 3613 d 0 1000 n j 3613 MLTG5 j 3614 s 0 n j 3614 MLTG5_reverse_6f2d4 j 3615 d 0 1000 n j 3615 MLTGY1pp j 3616 s 0 n j 3616 MLTGY1pp_reverse_83a82 j 3617 d 0 1000 n j 3617 MLTGY2pp j 3618 s 0 n j 3618 MLTGY2pp_reverse_21ef1 j 3619 d 0 1000 n j 3619 MLTGY3pp j 3620 s 0 n j 3620 MLTGY3pp_reverse_b3def j 3621 d 0 1000 n j 3621 MLTGY4pp j 3622 s 0 n j 3622 MLTGY4pp_reverse_73320 j 3623 d 0 1000 n j 3623 MLTP1 j 3624 d 0 1000 n j 3624 MLTP1_reverse_0e00b j 3625 d 0 1000 n j 3625 MLTP2 j 3626 d 0 1000 n j 3626 MLTP2_reverse_2ca92 j 3627 d 0 1000 n j 3627 MLTP3 j 3628 d 0 1000 n j 3628 MLTP3_reverse_b3ce1 j 3629 d 0 1000 n j 3629 MMCD j 3630 s 0 n j 3630 MMCD_reverse_64681 j 3631 d 0 1000 n j 3631 MMETt2pp j 3632 s 0 n j 3632 MMETt2pp_reverse_79a30 j 3633 d 0 1000 n j 3633 MMETtex j 3634 d 0 1000 n j 3634 MMETtex_reverse_65f54 j 3635 d 0 1000 n j 3635 MMM j 3636 s 0 n j 3636 MMM_reverse_37538 j 3637 d 0 1000 n j 3637 MN2t3pp j 3638 s 0 n j 3638 MN2t3pp_reverse_f2687 j 3639 d 0 1000 n j 3639 MN2tpp j 3640 s 0 n j 3640 MN2tpp_reverse_45302 j 3641 d 0 1000 n j 3641 MN6PP j 3642 s 0 n j 3642 MN6PP_reverse_27a9e j 3643 d 0 1000 n j 3643 MNLptspp j 3644 s 0 n j 3644 MNLptspp_reverse_ef012 j 3645 d 0 1000 n j 3645 MNLtex j 3646 d 0 1000 n j 3646 MNLtex_reverse_b3c92 j 3647 d 0 1000 n j 3647 MNNH j 3648 s 0 n j 3648 MNNH_reverse_93660 j 3649 d 0 1000 n j 3649 MNt2pp j 3650 s 0 n j 3650 MNt2pp_reverse_c690c j 3651 d 0 1000 n j 3651 MNtex j 3652 d 0 1000 n j 3652 MNtex_reverse_711cc j 3653 d 0 1000 n j 3653 MOADSUx j 3654 s 0 n j 3654 MOADSUx_reverse_ba039 j 3655 d 0 1000 n j 3655 MOAT j 3656 s 0 n j 3656 MOAT_reverse_0fdf8 j 3657 d 0 1000 n j 3657 MOAT2 j 3658 s 0 n j 3658 MOAT2_reverse_6e42e j 3659 d 0 1000 n j 3659 MOAT3C j 3660 s 0 n j 3660 MOAT3C_reverse_7302c j 3661 d 0 1000 n j 3661 MOBDabcpp j 3662 s 0 n j 3662 MOBDabcpp_reverse_4be38 j 3663 d 0 1000 n j 3663 MOBDtex j 3664 d 0 1000 n j 3664 MOBDtex_reverse_4fdc7 j 3665 d 0 1000 n j 3665 MOCDS j 3666 s 0 n j 3666 MOCDS_reverse_6b5e6 j 3667 d 0 1000 n j 3667 MOCOS j 3668 s 0 n j 3668 MOCOS_reverse_39ff4 j 3669 d 0 1000 n j 3669 MOGDS j 3670 s 0 n j 3670 MOGDS_reverse_eab6b j 3671 d 0 1000 n j 3671 MOHMT j 3672 s 0 n j 3672 MOHMT_reverse_83ce0 j 3673 d 0 1000 n j 3673 MOX j 3674 d 0 1000 n j 3674 MOX_reverse_b32ec j 3675 d 0 1000 n j 3675 MPTAT j 3676 s 0 n j 3676 MPTAT_reverse_75105 j 3677 d 0 1000 n j 3677 MPTG j 3678 s 0 n j 3678 MPTG_reverse_610dd j 3679 d 0 1000 n j 3679 MPTG2 j 3680 s 0 n j 3680 MPTG2_reverse_fd602 j 3681 d 0 1000 n j 3681 MPTS j 3682 s 0 n j 3682 MPTS_reverse_45339 j 3683 d 0 1000 n j 3683 MPTSS j 3684 s 0 n j 3684 MPTSS_reverse_d864d j 3685 d 0 1000 n j 3685 MSAR j 3686 s 0 n j 3686 MSAR_reverse_9ab38 j 3687 d 0 1000 n j 3687 MSO3abcpp j 3688 s 0 n j 3688 MSO3abcpp_reverse_61429 j 3689 d 0 1000 n j 3689 MSO3tex j 3690 d 0 1000 n j 3690 MSO3tex_reverse_0222e j 3691 d 0 1000 n j 3691 MTAN j 3692 s 0 n j 3692 MTAN_reverse_57476 j 3693 d 0 1000 n j 3693 MTHFC j 3694 d 0 1000 n j 3694 MTHFC_reverse_f6fcc j 3695 d 0 1000 n j 3695 MTHFD j 3696 d 0 1000 n j 3696 MTHFD_reverse_c10fd j 3697 d 0 1000 n j 3697 MTHFR2 j 3698 s 0 n j 3698 MTHFR2_reverse_40f34 j 3699 d 0 1000 n j 3699 MTHTHFSs j 3700 s 0 n j 3700 MTHTHFSs_reverse_faff6 j 3701 d 0 1000 n j 3701 MTRPOX j 3702 s 0 n j 3702 MTRPOX_reverse_db92e j 3703 d 0 1000 n j 3703 N2Otex j 3704 d 0 1000 n j 3704 N2Otex_reverse_8cda9 j 3705 d 0 1000 n j 3705 N2Otpp j 3706 d 0 1000 n j 3706 N2Otpp_reverse_e5ea3 j 3707 d 0 1000 n j 3707 NACODA j 3708 s 0 n j 3708 NACODA_reverse_e2aa5 j 3709 d 0 1000 n j 3709 NACtex j 3710 d 0 1000 n j 3710 NACtex_reverse_435b9 j 3711 d 0 1000 n j 3711 NACtpp j 3712 s 0 n j 3712 NACtpp_reverse_4b9c9 j 3713 d 0 1000 n j 3713 NADDP j 3714 s 0 n j 3714 NADDP_reverse_7a11e j 3715 d 0 1000 n j 3715 NADH10 j 3716 s 0 n j 3716 NADH10_reverse_e415a j 3717 d 0 1000 n j 3717 NADH16pp j 3718 s 0 n j 3718 NADH16pp_reverse_a8e37 j 3719 d 0 1000 n j 3719 NADH17pp j 3720 s 0 n j 3720 NADH17pp_reverse_f64c7 j 3721 d 0 1000 n j 3721 NADH18pp j 3722 s 0 n j 3722 NADH18pp_reverse_8cf33 j 3723 d 0 1000 n j 3723 NADH5 j 3724 s 0 n j 3724 NADH5_reverse_d695e j 3725 d 0 1000 n j 3725 NADH9 j 3726 s 0 n j 3726 NADH9_reverse_91511 j 3727 d 0 1000 n j 3727 NADK j 3728 s 0 n j 3728 NADK_reverse_bba52 j 3729 d 0 1000 n j 3729 NADN j 3730 s 0 n j 3730 NADN_reverse_8a930 j 3731 d 0 1000 n j 3731 NADPHQR2 j 3732 s 0 n j 3732 NADPHQR2_reverse_481c5 j 3733 d 0 1000 n j 3733 NADPHQR3 j 3734 s 0 n j 3734 NADPHQR3_reverse_6a295 j 3735 d 0 1000 n j 3735 NADPHQR4 j 3736 s 0 n j 3736 NADPHQR4_reverse_ebd03 j 3737 d 0 1000 n j 3737 NADPPPS j 3738 s 0 n j 3738 NADPPPS_reverse_c9c40 j 3739 d 0 1000 n j 3739 NADS1 j 3740 s 0 n j 3740 NADS1_reverse_0b93a j 3741 d 0 1000 n j 3741 NADTRHD j 3742 s 0 n j 3742 NADTRHD_reverse_49725 j 3743 d 0 1000 n j 3743 NAMNPP j 3744 s 0 n j 3744 NAMNPP_reverse_ebb31 j 3745 d 0 1000 n j 3745 NAt3_1p5pp j 3746 s 0 n j 3746 NAt3_1p5pp_reverse_b9b29 j 3747 d 0 1000 n j 3747 NAt3_2pp j 3748 s 0 n j 3748 NAt3_2pp_reverse_4ca5c j 3749 d 0 1000 n j 3749 NAt3pp j 3750 s 0 n j 3750 NAt3pp_reverse_421a2 j 3751 d 0 1000 n j 3751 NAtex j 3752 d 0 1000 n j 3752 NAtex_reverse_0181e j 3753 d 0 1000 n j 3753 NDPK1 j 3754 d 0 1000 n j 3754 NDPK1_reverse_9216a j 3755 d 0 1000 n j 3755 NDPK2 j 3756 d 0 1000 n j 3756 NDPK2_reverse_10df6 j 3757 d 0 1000 n j 3757 NDPK3 j 3758 d 0 1000 n j 3758 NDPK3_reverse_37ea6 j 3759 d 0 1000 n j 3759 NDPK4 j 3760 d 0 1000 n j 3760 NDPK4_reverse_9a1c8 j 3761 d 0 1000 n j 3761 NDPK5 j 3762 d 0 1000 n j 3762 NDPK5_reverse_6973f j 3763 d 0 1000 n j 3763 NDPK6 j 3764 d 0 1000 n j 3764 NDPK6_reverse_d41ea j 3765 d 0 1000 n j 3765 NDPK7 j 3766 d 0 1000 n j 3766 NDPK7_reverse_9dc79 j 3767 d 0 1000 n j 3767 NDPK8 j 3768 d 0 1000 n j 3768 NDPK8_reverse_13dd1 j 3769 d 0 1000 n j 3769 NH4tex j 3770 d 0 1000 n j 3770 NH4tex_reverse_ce04b j 3771 d 0 1000 n j 3771 NH4tpp j 3772 d 0 1000 n j 3772 NH4tpp_reverse_eca16 j 3773 d 0 1000 n j 3773 NHFRBO j 3774 s 0 n j 3774 NHFRBO_reverse_08cf3 j 3775 d 0 1000 n j 3775 NI2abcpp j 3776 s 0 n j 3776 NI2abcpp_reverse_77f95 j 3777 d 0 1000 n j 3777 NI2t3pp j 3778 s 0 n j 3778 NI2t3pp_reverse_0da92 j 3779 d 0 1000 n j 3779 NI2tex j 3780 d 0 1000 n j 3780 NI2tex_reverse_a2971 j 3781 d 0 1000 n j 3781 NI2tpp j 3782 s 0 n j 3782 NI2tpp_reverse_e3b18 j 3783 d 0 1000 n j 3783 NI2uabcpp j 3784 s 0 n j 3784 NI2uabcpp_reverse_db325 j 3785 d 0 1000 n j 3785 NMNAT j 3786 s 0 n j 3786 NMNAT_reverse_6a3d7 j 3787 d 0 1000 n j 3787 NMNDA j 3788 s 0 n j 3788 NMNDA_reverse_0dc65 j 3789 d 0 1000 n j 3789 NMNN j 3790 s 0 n j 3790 NMNN_reverse_3ff83 j 3791 d 0 1000 n j 3791 NMNPtpp j 3792 s 0 n j 3792 NMNPtpp_reverse_fc375 j 3793 d 0 1000 n j 3793 NMNt7pp j 3794 s 0 n j 3794 NMNt7pp_reverse_aded5 j 3795 d 0 1000 n j 3795 NMNtex j 3796 d 0 1000 n j 3796 NMNtex_reverse_f9178 j 3797 d 0 1000 n j 3797 NNAM j 3798 s 0 n j 3798 NNAM_reverse_68e1d j 3799 d 0 1000 n j 3799 NNATr j 3800 d 0 1000 n j 3800 NNATr_reverse_8ab73 j 3801 d 0 1000 n j 3801 NNDMBRT j 3802 s 0 n j 3802 NNDMBRT_reverse_13f8c j 3803 d 0 1000 n j 3803 NNDPR j 3804 s 0 n j 3804 NNDPR_reverse_445ff j 3805 d 0 1000 n j 3805 NO2t2rpp j 3806 d 0 1000 n j 3806 NO2t2rpp_reverse_a35c8 j 3807 d 0 1000 n j 3807 NO2tex j 3808 d 0 1000 n j 3808 NO2tex_reverse_fed02 j 3809 d 0 1000 n j 3809 NO3R1bpp j 3810 s 0 n j 3810 NO3R1bpp_reverse_f3ffe j 3811 d 0 1000 n j 3811 NO3R1pp j 3812 s 0 n j 3812 NO3R1pp_reverse_9141e j 3813 d 0 1000 n j 3813 NO3R2bpp j 3814 s 0 n j 3814 NO3R2bpp_reverse_ba091 j 3815 d 0 1000 n j 3815 NO3R2pp j 3816 s 0 n j 3816 NO3R2pp_reverse_f05e9 j 3817 d 0 1000 n j 3817 NO3t7pp j 3818 s 0 n j 3818 NO3t7pp_reverse_bcb41 j 3819 d 0 1000 n j 3819 NO3tex j 3820 d 0 1000 n j 3820 NO3tex_reverse_b1290 j 3821 d 0 1000 n j 3821 NODOx j 3822 s 0 n j 3822 NODOx_reverse_aa53a j 3823 d 0 1000 n j 3823 NODOy j 3824 s 0 n j 3824 NODOy_reverse_0f72e j 3825 d 0 1000 n j 3825 NOVBCNtex j 3826 d 0 1000 n j 3826 NOVBCNtex_reverse_5ad93 j 3827 d 0 1000 n j 3827 NOVBCNtpp j 3828 s 0 n j 3828 NOVBCNtpp_reverse_0bf15 j 3829 d 0 1000 n j 3829 NOtex j 3830 d 0 1000 n j 3830 NOtex_reverse_4cf96 j 3831 d 0 1000 n j 3831 NOtpp j 3832 d 0 1000 n j 3832 NOtpp_reverse_27880 j 3833 d 0 1000 n j 3833 NTD1 j 3834 s 0 n j 3834 NTD1_reverse_d7db9 j 3835 d 0 1000 n j 3835 NTD10 j 3836 s 0 n j 3836 NTD10_reverse_8b9f0 j 3837 d 0 1000 n j 3837 NTD10pp j 3838 s 0 n j 3838 NTD10pp_reverse_7730d j 3839 d 0 1000 n j 3839 NTD11 j 3840 s 0 n j 3840 NTD11_reverse_39abf j 3841 d 0 1000 n j 3841 NTD11pp j 3842 s 0 n j 3842 NTD11pp_reverse_ffc05 j 3843 d 0 1000 n j 3843 NTD12 j 3844 s 0 n j 3844 NTD12_reverse_293d1 j 3845 d 0 1000 n j 3845 NTD12pp j 3846 s 0 n j 3846 NTD12pp_reverse_9d30b j 3847 d 0 1000 n j 3847 NTD1pp j 3848 s 0 n j 3848 NTD1pp_reverse_fa4c2 j 3849 d 0 1000 n j 3849 NTD2 j 3850 s 0 n j 3850 NTD2_reverse_a3382 j 3851 d 0 1000 n j 3851 NTD2pp j 3852 s 0 n j 3852 NTD2pp_reverse_78372 j 3853 d 0 1000 n j 3853 NTD3 j 3854 s 0 n j 3854 NTD3_reverse_6e80d j 3855 d 0 1000 n j 3855 NTD3pp j 3856 s 0 n j 3856 NTD3pp_reverse_c3d97 j 3857 d 0 1000 n j 3857 NTD4 j 3858 s 0 n j 3858 NTD4_reverse_0e18e j 3859 d 0 1000 n j 3859 NTD4pp j 3860 s 0 n j 3860 NTD4pp_reverse_51810 j 3861 d 0 1000 n j 3861 NTD5 j 3862 s 0 n j 3862 NTD5_reverse_28a76 j 3863 d 0 1000 n j 3863 NTD5pp j 3864 s 0 n j 3864 NTD5pp_reverse_b7c36 j 3865 d 0 1000 n j 3865 NTD6 j 3866 s 0 n j 3866 NTD6_reverse_c5bce j 3867 d 0 1000 n j 3867 NTD6pp j 3868 s 0 n j 3868 NTD6pp_reverse_fefa9 j 3869 d 0 1000 n j 3869 NTD7 j 3870 s 0 n j 3870 NTD7_reverse_20dab j 3871 d 0 1000 n j 3871 NTD7pp j 3872 s 0 n j 3872 NTD7pp_reverse_96e48 j 3873 d 0 1000 n j 3873 NTD8 j 3874 s 0 n j 3874 NTD8_reverse_9dc69 j 3875 d 0 1000 n j 3875 NTD8pp j 3876 s 0 n j 3876 NTD8pp_reverse_2d04b j 3877 d 0 1000 n j 3877 NTD9 j 3878 s 0 n j 3878 NTD9_reverse_d6a60 j 3879 d 0 1000 n j 3879 NTD9pp j 3880 s 0 n j 3880 NTD9pp_reverse_56df5 j 3881 d 0 1000 n j 3881 NTP1 j 3882 s 0 n j 3882 NTP1_reverse_46daa j 3883 d 0 1000 n j 3883 NTP10 j 3884 s 0 n j 3884 NTP10_reverse_1c22d j 3885 d 0 1000 n j 3885 NTP11 j 3886 s 0 n j 3886 NTP11_reverse_a170e j 3887 d 0 1000 n j 3887 NTP12 j 3888 s 0 n j 3888 NTP12_reverse_c9a38 j 3889 d 0 1000 n j 3889 NTP3 j 3890 s 0 n j 3890 NTP3_reverse_eac23 j 3891 d 0 1000 n j 3891 NTP3pp j 3892 s 0 n j 3892 NTP3pp_reverse_4be59 j 3893 d 0 1000 n j 3893 NTP5 j 3894 s 0 n j 3894 NTP5_reverse_252e0 j 3895 d 0 1000 n j 3895 NTPP1 j 3896 s 0 n j 3896 NTPP1_reverse_947f5 j 3897 d 0 1000 n j 3897 NTPP10 j 3898 s 0 n j 3898 NTPP10_reverse_bcc00 j 3899 d 0 1000 n j 3899 NTPP11 j 3900 s 0 n j 3900 NTPP11_reverse_a0c27 j 3901 d 0 1000 n j 3901 NTPP2 j 3902 s 0 n j 3902 NTPP2_reverse_bff4f j 3903 d 0 1000 n j 3903 NTPP3 j 3904 s 0 n j 3904 NTPP3_reverse_32c2e j 3905 d 0 1000 n j 3905 NTPP4 j 3906 s 0 n j 3906 NTPP4_reverse_232cc j 3907 d 0 1000 n j 3907 NTPP5 j 3908 s 0 n j 3908 NTPP5_reverse_f08d0 j 3909 d 0 1000 n j 3909 NTPP6 j 3910 s 0 n j 3910 NTPP6_reverse_4f33c j 3911 d 0 1000 n j 3911 NTPP7 j 3912 s 0 n j 3912 NTPP7_reverse_47c62 j 3913 d 0 1000 n j 3913 NTPP8 j 3914 s 0 n j 3914 NTPP8_reverse_ce3f8 j 3915 d 0 1000 n j 3915 NTPP9 j 3916 s 0 n j 3916 NTPP9_reverse_70642 j 3917 d 0 1000 n j 3917 NTPTP1 j 3918 s 0 n j 3918 NTPTP1_reverse_9002b j 3919 d 0 1000 n j 3919 NTPTP2 j 3920 s 0 n j 3920 NTPTP2_reverse_fb322 j 3921 d 0 1000 n j 3921 NTRIR2x j 3922 s 0 n j 3922 NTRIR2x_reverse_2ba0c j 3923 d 0 1000 n j 3923 NTRIR3pp j 3924 s 0 n j 3924 NTRIR3pp_reverse_9be00 j 3925 d 0 1000 n j 3925 NTRIR4pp j 3926 s 0 n j 3926 NTRIR4pp_reverse_e0888 j 3927 d 0 1000 n j 3927 O16A4COLIPAabctex j 3928 s 0 n j 3928 O16A4COLIPAabctex_reverse_235f8 j 3929 d 0 1000 n j 3929 O16A4Lpp j 3930 s 0 n j 3930 O16A4Lpp_reverse_1c015 j 3931 d 0 1000 n j 3931 O16AP1pp j 3932 s 0 n j 3932 O16AP1pp_reverse_7fc4f j 3933 d 0 1000 n j 3933 O16AP2pp j 3934 s 0 n j 3934 O16AP2pp_reverse_72a5e j 3935 d 0 1000 n j 3935 O16AP3pp j 3936 s 0 n j 3936 O16AP3pp_reverse_4d00e j 3937 d 0 1000 n j 3937 O16AT j 3938 s 0 n j 3938 O16AT_reverse_6b6d9 j 3939 d 0 1000 n j 3939 O16AUNDtpp j 3940 s 0 n j 3940 O16AUNDtpp_reverse_3cf3d j 3941 d 0 1000 n j 3941 O16GALFT j 3942 s 0 n j 3942 O16GALFT_reverse_a9d34 j 3943 d 0 1000 n j 3943 O16GLCT1 j 3944 s 0 n j 3944 O16GLCT1_reverse_677cd j 3945 d 0 1000 n j 3945 O16GLCT2 j 3946 s 0 n j 3946 O16GLCT2_reverse_cee71 j 3947 d 0 1000 n j 3947 O2Stex j 3948 d 0 1000 n j 3948 O2Stex_reverse_77f73 j 3949 d 0 1000 n j 3949 O2tex j 3950 d 0 1000 n j 3950 O2tex_reverse_a3b28 j 3951 d 0 1000 n j 3951 O2tpp j 3952 d 0 1000 n j 3952 O2tpp_reverse_28c7e j 3953 d 0 1000 n j 3953 OAADC j 3954 s 0 n j 3954 OAADC_reverse_f85ae j 3955 d 0 1000 n j 3955 OBTFL j 3956 s 0 n j 3956 OBTFL_reverse_ab4a2 j 3957 d 0 1000 n j 3957 OCBT j 3958 d 0 1000 n j 3958 OCBT_reverse_f5568 j 3959 d 0 1000 n j 3959 OCDCAtexi j 3960 s 0 n j 3960 OCDCAtexi_reverse_b8f37 j 3961 d 0 1000 n j 3961 OCDCEAtexi j 3962 s 0 n j 3962 OCDCEAtexi_reverse_4cd10 j 3963 d 0 1000 n j 3963 OCTAtex j 3964 d 0 1000 n j 3964 OCTAtex_reverse_4220a j 3965 d 0 1000 n j 3965 OCTDPS j 3966 s 0 n j 3966 OCTDPS_reverse_358d9 j 3967 d 0 1000 n j 3967 OCTNLL j 3968 s 0 n j 3968 OCTNLL_reverse_c89f6 j 3969 d 0 1000 n j 3969 OGMEACPD j 3970 s 0 n j 3970 OGMEACPD_reverse_fa697 j 3971 d 0 1000 n j 3971 OGMEACPR j 3972 s 0 n j 3972 OGMEACPR_reverse_53919 j 3973 d 0 1000 n j 3973 OGMEACPS j 3974 s 0 n j 3974 OGMEACPS_reverse_13b17 j 3975 d 0 1000 n j 3975 OHPBAT j 3976 d 0 1000 n j 3976 OHPBAT_reverse_7e72e j 3977 d 0 1000 n j 3977 OHPHM j 3978 s 0 n j 3978 OHPHM_reverse_b08b4 j 3979 d 0 1000 n j 3979 OMBZLM j 3980 s 0 n j 3980 OMBZLM_reverse_a3f14 j 3981 d 0 1000 n j 3981 OMCDC j 3982 s 0 n j 3982 OMCDC_reverse_74477 j 3983 d 0 1000 n j 3983 OMMBLHX j 3984 s 0 n j 3984 OMMBLHX_reverse_94dfd j 3985 d 0 1000 n j 3985 OMMBLHX3 j 3986 s 0 n j 3986 OMMBLHX3_reverse_afc77 j 3987 d 0 1000 n j 3987 OMPDC j 3988 s 0 n j 3988 OMPDC_reverse_45ba1 j 3989 d 0 1000 n j 3989 OMPHHX j 3990 s 0 n j 3990 OMPHHX_reverse_c6683 j 3991 d 0 1000 n j 3991 OMPHHX3 j 3992 s 0 n j 3992 OMPHHX3_reverse_24123 j 3993 d 0 1000 n j 3993 OP4ENH j 3994 s 0 n j 3994 OP4ENH_reverse_ef8b0 j 3995 d 0 1000 n j 3995 OPHBDC j 3996 s 0 n j 3996 OPHBDC_reverse_da435 j 3997 d 0 1000 n j 3997 OPHHX j 3998 s 0 n j 3998 OPHHX_reverse_2aeb1 j 3999 d 0 1000 n j 3999 OPHHX3 j 4000 s 0 n j 4000 OPHHX3_reverse_63f10 j 4001 d 0 1000 n j 4001 OPMEACPD j 4002 s 0 n j 4002 OPMEACPD_reverse_d1190 j 4003 d 0 1000 n j 4003 OPMEACPR j 4004 s 0 n j 4004 OPMEACPR_reverse_7cc6e j 4005 d 0 1000 n j 4005 OPMEACPS j 4006 s 0 n j 4006 OPMEACPS_reverse_e3f2d j 4007 d 0 1000 n j 4007 ORNDC j 4008 s 0 n j 4008 ORNDC_reverse_63596 j 4009 d 0 1000 n j 4009 ORNabcpp j 4010 s 0 n j 4010 ORNabcpp_reverse_d4b6e j 4011 d 0 1000 n j 4011 ORNtex j 4012 d 0 1000 n j 4012 ORNtex_reverse_d3e6a j 4013 d 0 1000 n j 4013 OROTt2_2pp j 4014 s 0 n j 4014 OROTt2_2pp_reverse_6e78b j 4015 d 0 1000 n j 4015 OROTtex j 4016 d 0 1000 n j 4016 OROTtex_reverse_42ab2 j 4017 d 0 1000 n j 4017 ORPT j 4018 d 0 1000 n j 4018 ORPT_reverse_19432 j 4019 d 0 1000 n j 4019 OXAMTC j 4020 s 0 n j 4020 OXAMTC_reverse_51fcf j 4021 d 0 1000 n j 4021 OXCDC j 4022 s 0 n j 4022 OXCDC_reverse_ee03e j 4023 d 0 1000 n j 4023 OXCOAHDH j 4024 s 0 n j 4024 OXCOAHDH_reverse_82fbe j 4025 d 0 1000 n j 4025 OXDHCOAT j 4026 s 0 n j 4026 OXDHCOAT_reverse_4ad9c j 4027 d 0 1000 n j 4027 P5CD j 4028 s 0 n j 4028 P5CD_reverse_c7374 j 4029 d 0 1000 n j 4029 P5CR j 4030 s 0 n j 4030 P5CR_reverse_55c58 j 4031 d 0 1000 n j 4031 PA120abcpp j 4032 s 0 n j 4032 PA120abcpp_reverse_b98c7 j 4033 d 0 1000 n j 4033 PA140abcpp j 4034 s 0 n j 4034 PA140abcpp_reverse_01d15 j 4035 d 0 1000 n j 4035 PA141abcpp j 4036 s 0 n j 4036 PA141abcpp_reverse_685e5 j 4037 d 0 1000 n j 4037 PA160abcpp j 4038 s 0 n j 4038 PA160abcpp_reverse_5cabb j 4039 d 0 1000 n j 4039 PA161abcpp j 4040 s 0 n j 4040 PA161abcpp_reverse_5530a j 4041 d 0 1000 n j 4041 PA180abcpp j 4042 s 0 n j 4042 PA180abcpp_reverse_58c6b j 4043 d 0 1000 n j 4043 PA181abcpp j 4044 s 0 n j 4044 PA181abcpp_reverse_a7059 j 4045 d 0 1000 n j 4045 PACALDt2rpp j 4046 d 0 1000 n j 4046 PACALDt2rpp_reverse_110b1 j 4047 d 0 1000 n j 4047 PACALDtex j 4048 d 0 1000 n j 4048 PACALDtex_reverse_1dc09 j 4049 d 0 1000 n j 4049 PACCOAE j 4050 s 0 n j 4050 PACCOAE_reverse_20591 j 4051 d 0 1000 n j 4051 PACCOAL j 4052 s 0 n j 4052 PACCOAL_reverse_e1401 j 4053 d 0 1000 n j 4053 PANTS j 4054 s 0 n j 4054 PANTS_reverse_11dcb j 4055 d 0 1000 n j 4055 PAPA120 j 4056 s 0 n j 4056 PAPA120_reverse_75d70 j 4057 d 0 1000 n j 4057 PAPA120pp j 4058 s 0 n j 4058 PAPA120pp_reverse_54fbc j 4059 d 0 1000 n j 4059 PAPA140 j 4060 s 0 n j 4060 PAPA140_reverse_255f5 j 4061 d 0 1000 n j 4061 PAPA140pp j 4062 s 0 n j 4062 PAPA140pp_reverse_0a02c j 4063 d 0 1000 n j 4063 PAPA141 j 4064 s 0 n j 4064 PAPA141_reverse_945ab j 4065 d 0 1000 n j 4065 PAPA141pp j 4066 s 0 n j 4066 PAPA141pp_reverse_5d5a0 j 4067 d 0 1000 n j 4067 PAPA160 j 4068 s 0 n j 4068 PAPA160_reverse_c64df j 4069 d 0 1000 n j 4069 PAPA160pp j 4070 s 0 n j 4070 PAPA160pp_reverse_1ea28 j 4071 d 0 1000 n j 4071 PAPA161 j 4072 s 0 n j 4072 PAPA161_reverse_1bc33 j 4073 d 0 1000 n j 4073 PAPA161pp j 4074 s 0 n j 4074 PAPA161pp_reverse_65baf j 4075 d 0 1000 n j 4075 PAPA180 j 4076 s 0 n j 4076 PAPA180_reverse_9f7f6 j 4077 d 0 1000 n j 4077 PAPA180pp j 4078 s 0 n j 4078 PAPA180pp_reverse_f1d0a j 4079 d 0 1000 n j 4079 PAPA181 j 4080 s 0 n j 4080 PAPA181_reverse_fb09e j 4081 d 0 1000 n j 4081 PAPA181pp j 4082 s 0 n j 4082 PAPA181pp_reverse_f7ae0 j 4083 d 0 1000 n j 4083 PAPPT3 j 4084 s 0 n j 4084 PAPPT3_reverse_0a787 j 4085 d 0 1000 n j 4085 PAPSR j 4086 s 0 n j 4086 PAPSR_reverse_75961 j 4087 d 0 1000 n j 4087 PAPSR2 j 4088 s 0 n j 4088 PAPSR2_reverse_3fe9e j 4089 d 0 1000 n j 4089 PDE1 j 4090 s 0 n j 4090 PDE1_reverse_9a118 j 4091 d 0 1000 n j 4091 PDE4 j 4092 s 0 n j 4092 PDE4_reverse_5c3ba j 4093 d 0 1000 n j 4093 PDH j 4094 s 0 n j 4094 PDH_reverse_ca160 j 4095 d 0 1000 n j 4095 PDX5PO2 j 4096 s 0 n j 4096 PDX5PO2_reverse_069b2 j 4097 d 0 1000 n j 4097 PDX5POi j 4098 s 0 n j 4098 PDX5POi_reverse_797dd j 4099 d 0 1000 n j 4099 PDX5PS j 4100 s 0 n j 4100 PDX5PS_reverse_2e3a2 j 4101 d 0 1000 n j 4101 PDXPP j 4102 s 0 n j 4102 PDXPP_reverse_e5f62 j 4103 d 0 1000 n j 4103 PE120abcpp j 4104 s 0 n j 4104 PE120abcpp_reverse_5ce28 j 4105 d 0 1000 n j 4105 PE140abcpp j 4106 s 0 n j 4106 PE140abcpp_reverse_6fa3a j 4107 d 0 1000 n j 4107 PE141abcpp j 4108 s 0 n j 4108 PE141abcpp_reverse_c1abc j 4109 d 0 1000 n j 4109 PE160abcpp j 4110 s 0 n j 4110 PE160abcpp_reverse_a5047 j 4111 d 0 1000 n j 4111 PE161abcpp j 4112 s 0 n j 4112 PE161abcpp_reverse_bbf6e j 4113 d 0 1000 n j 4113 PE180abcpp j 4114 s 0 n j 4114 PE180abcpp_reverse_7cc0f j 4115 d 0 1000 n j 4115 PE181abcpp j 4116 s 0 n j 4116 PE181abcpp_reverse_7648b j 4117 d 0 1000 n j 4117 PEAMNOpp j 4118 s 0 n j 4118 PEAMNOpp_reverse_82140 j 4119 d 0 1000 n j 4119 PEAMNtex j 4120 d 0 1000 n j 4120 PEAMNtex_reverse_3139a j 4121 d 0 1000 n j 4121 PERD j 4122 d 0 1000 n j 4122 PERD_reverse_c9aa4 j 4123 d 0 1000 n j 4123 PETNT161pp j 4124 s 0 n j 4124 PETNT161pp_reverse_946d0 j 4125 d 0 1000 n j 4125 PETNT181pp j 4126 s 0 n j 4126 PETNT181pp_reverse_3d813 j 4127 d 0 1000 n j 4127 PFK j 4128 s 0 n j 4128 PFK_reverse_d24a6 j 4129 d 0 1000 n j 4129 PFK_2 j 4130 s 0 n j 4130 PFK_2_reverse_ff38b j 4131 d 0 1000 n j 4131 PFK_3 j 4132 s 0 n j 4132 PFK_3_reverse_037e0 j 4133 d 0 1000 n j 4133 PFL j 4134 s 0 n j 4134 PFL_reverse_af9ec j 4135 d 0 1000 n j 4135 PG120abcpp j 4136 s 0 n j 4136 PG120abcpp_reverse_1e715 j 4137 d 0 1000 n j 4137 PG140abcpp j 4138 s 0 n j 4138 PG140abcpp_reverse_ac85f j 4139 d 0 1000 n j 4139 PG141abcpp j 4140 s 0 n j 4140 PG141abcpp_reverse_d1db9 j 4141 d 0 1000 n j 4141 PG160abcpp j 4142 s 0 n j 4142 PG160abcpp_reverse_5e019 j 4143 d 0 1000 n j 4143 PG161abcpp j 4144 s 0 n j 4144 PG161abcpp_reverse_c6d7f j 4145 d 0 1000 n j 4145 PG180abcpp j 4146 s 0 n j 4146 PG180abcpp_reverse_c791a j 4147 d 0 1000 n j 4147 PG181abcpp j 4148 s 0 n j 4148 PG181abcpp_reverse_7fd9e j 4149 d 0 1000 n j 4149 PGAMT j 4150 d 0 1000 n j 4150 PGAMT_reverse_52c23 j 4151 d 0 1000 n j 4151 PGCD j 4152 s 0 n j 4152 PGCD_reverse_1bc76 j 4153 d 0 1000 n j 4153 PGI j 4154 d 0 1000 n j 4154 PGI_reverse_27efc j 4155 d 0 1000 n j 4155 PGK j 4156 d 0 1000 n j 4156 PGK_reverse_02696 j 4157 d 0 1000 n j 4157 PGL j 4158 s 0 n j 4158 PGL_reverse_2bb6b j 4159 d 0 1000 n j 4159 PGLYCP j 4160 s 0 n j 4160 PGLYCP_reverse_9063f j 4161 d 0 1000 n j 4161 PGM j 4162 d 0 1000 n j 4162 PGM_reverse_fc9af j 4163 d 0 1000 n j 4163 PGMT j 4164 d 0 1000 n j 4164 PGMT_reverse_5bcdd j 4165 d 0 1000 n j 4165 PGP120abcpp j 4166 s 0 n j 4166 PGP120abcpp_reverse_2af50 j 4167 d 0 1000 n j 4167 PGP140abcpp j 4168 s 0 n j 4168 PGP140abcpp_reverse_8af6d j 4169 d 0 1000 n j 4169 PGP141abcpp j 4170 s 0 n j 4170 PGP141abcpp_reverse_cfe51 j 4171 d 0 1000 n j 4171 PGP160abcpp j 4172 s 0 n j 4172 PGP160abcpp_reverse_cc220 j 4173 d 0 1000 n j 4173 PGP161abcpp j 4174 s 0 n j 4174 PGP161abcpp_reverse_6df76 j 4175 d 0 1000 n j 4175 PGP180abcpp j 4176 s 0 n j 4176 PGP180abcpp_reverse_14a2e j 4177 d 0 1000 n j 4177 PGP181abcpp j 4178 s 0 n j 4178 PGP181abcpp_reverse_5bd7a j 4179 d 0 1000 n j 4179 PGPP120 j 4180 s 0 n j 4180 PGPP120_reverse_4087a j 4181 d 0 1000 n j 4181 PGPP120pp j 4182 s 0 n j 4182 PGPP120pp_reverse_12e68 j 4183 d 0 1000 n j 4183 PGPP140 j 4184 s 0 n j 4184 PGPP140_reverse_88ebb j 4185 d 0 1000 n j 4185 PGPP140pp j 4186 s 0 n j 4186 PGPP140pp_reverse_59079 j 4187 d 0 1000 n j 4187 PGPP141 j 4188 s 0 n j 4188 PGPP141_reverse_7b56a j 4189 d 0 1000 n j 4189 PGPP141pp j 4190 s 0 n j 4190 PGPP141pp_reverse_31791 j 4191 d 0 1000 n j 4191 PGPP160 j 4192 s 0 n j 4192 PGPP160_reverse_742ef j 4193 d 0 1000 n j 4193 PGPP160pp j 4194 s 0 n j 4194 PGPP160pp_reverse_7bac9 j 4195 d 0 1000 n j 4195 PGPP161 j 4196 s 0 n j 4196 PGPP161_reverse_5b17b j 4197 d 0 1000 n j 4197 PGPP161pp j 4198 s 0 n j 4198 PGPP161pp_reverse_6d946 j 4199 d 0 1000 n j 4199 PGPP180 j 4200 s 0 n j 4200 PGPP180_reverse_0851b j 4201 d 0 1000 n j 4201 PGPP180pp j 4202 s 0 n j 4202 PGPP180pp_reverse_c3bb9 j 4203 d 0 1000 n j 4203 PGPP181 j 4204 s 0 n j 4204 PGPP181_reverse_5742a j 4205 d 0 1000 n j 4205 PGPP181pp j 4206 s 0 n j 4206 PGPP181pp_reverse_58cfc j 4207 d 0 1000 n j 4207 PGSA120 j 4208 s 0 n j 4208 PGSA120_reverse_7ef84 j 4209 d 0 1000 n j 4209 PGSA140 j 4210 s 0 n j 4210 PGSA140_reverse_69338 j 4211 d 0 1000 n j 4211 PGSA141 j 4212 s 0 n j 4212 PGSA141_reverse_c2823 j 4213 d 0 1000 n j 4213 PGSA160 j 4214 s 0 n j 4214 PGSA160_reverse_d0d63 j 4215 d 0 1000 n j 4215 PGSA161 j 4216 s 0 n j 4216 PGSA161_reverse_9b5db j 4217 d 0 1000 n j 4217 PGSA180 j 4218 s 0 n j 4218 PGSA180_reverse_7fb49 j 4219 d 0 1000 n j 4219 PGSA181 j 4220 s 0 n j 4220 PGSA181_reverse_1a9c8 j 4221 d 0 1000 n j 4221 PHEMEabcpp j 4222 s 0 n j 4222 PHEMEabcpp_reverse_008c2 j 4223 d 0 1000 n j 4223 PHEMEtiex j 4224 s 0 n j 4224 PHEMEtiex_reverse_f3742 j 4225 d 0 1000 n j 4225 PHETA1 j 4226 d 0 1000 n j 4226 PHETA1_reverse_9d47a j 4227 d 0 1000 n j 4227 PHETRS j 4228 s 0 n j 4228 PHETRS_reverse_a31de j 4229 d 0 1000 n j 4229 PHEt2rpp j 4230 d 0 1000 n j 4230 PHEt2rpp_reverse_0e589 j 4231 d 0 1000 n j 4231 PHEtex j 4232 d 0 1000 n j 4232 PHEtex_reverse_dcc10 j 4233 d 0 1000 n j 4233 PHYTSpp j 4234 s 0 n j 4234 PHYTSpp_reverse_adac3 j 4235 d 0 1000 n j 4235 PIt2rpp j 4236 d 0 1000 n j 4236 PIt2rpp_reverse_52e06 j 4237 d 0 1000 n j 4237 PItex j 4238 d 0 1000 n j 4238 PItex_reverse_05721 j 4239 d 0 1000 n j 4239 PIuabcpp j 4240 s 0 n j 4240 PIuabcpp_reverse_c4f9b j 4241 d 0 1000 n j 4241 PLIPA1A120pp j 4242 s 0 n j 4242 PLIPA1A120pp_reverse_29048 j 4243 d 0 1000 n j 4243 PLIPA1A140pp j 4244 s 0 n j 4244 PLIPA1A140pp_reverse_6d29c j 4245 d 0 1000 n j 4245 PLIPA1A141pp j 4246 s 0 n j 4246 PLIPA1A141pp_reverse_8a166 j 4247 d 0 1000 n j 4247 PLIPA1A160pp j 4248 s 0 n j 4248 PLIPA1A160pp_reverse_1bbc9 j 4249 d 0 1000 n j 4249 PLIPA1A161pp j 4250 s 0 n j 4250 PLIPA1A161pp_reverse_31db8 j 4251 d 0 1000 n j 4251 PLIPA1A180pp j 4252 s 0 n j 4252 PLIPA1A180pp_reverse_35000 j 4253 d 0 1000 n j 4253 PLIPA1A181pp j 4254 s 0 n j 4254 PLIPA1A181pp_reverse_2eb9a j 4255 d 0 1000 n j 4255 PLIPA1E120pp j 4256 s 0 n j 4256 PLIPA1E120pp_reverse_a5c47 j 4257 d 0 1000 n j 4257 PLIPA1E140pp j 4258 s 0 n j 4258 PLIPA1E140pp_reverse_fa52d j 4259 d 0 1000 n j 4259 PLIPA1E141pp j 4260 s 0 n j 4260 PLIPA1E141pp_reverse_e1eb9 j 4261 d 0 1000 n j 4261 PLIPA1E160pp j 4262 s 0 n j 4262 PLIPA1E160pp_reverse_36143 j 4263 d 0 1000 n j 4263 PLIPA1E161pp j 4264 s 0 n j 4264 PLIPA1E161pp_reverse_91db5 j 4265 d 0 1000 n j 4265 PLIPA1E180pp j 4266 s 0 n j 4266 PLIPA1E180pp_reverse_daf92 j 4267 d 0 1000 n j 4267 PLIPA1E181pp j 4268 s 0 n j 4268 PLIPA1E181pp_reverse_80567 j 4269 d 0 1000 n j 4269 PLIPA1G120pp j 4270 s 0 n j 4270 PLIPA1G120pp_reverse_3bd2e j 4271 d 0 1000 n j 4271 PLIPA1G140pp j 4272 s 0 n j 4272 PLIPA1G140pp_reverse_1aa54 j 4273 d 0 1000 n j 4273 PLIPA1G141pp j 4274 s 0 n j 4274 PLIPA1G141pp_reverse_e6c68 j 4275 d 0 1000 n j 4275 PLIPA1G160pp j 4276 s 0 n j 4276 PLIPA1G160pp_reverse_3970d j 4277 d 0 1000 n j 4277 PLIPA1G161pp j 4278 s 0 n j 4278 PLIPA1G161pp_reverse_4473f j 4279 d 0 1000 n j 4279 PLIPA1G180pp j 4280 s 0 n j 4280 PLIPA1G180pp_reverse_f2449 j 4281 d 0 1000 n j 4281 PLIPA1G181pp j 4282 s 0 n j 4282 PLIPA1G181pp_reverse_548b3 j 4283 d 0 1000 n j 4283 PLIPA2A120pp j 4284 s 0 n j 4284 PLIPA2A120pp_reverse_7fb4b j 4285 d 0 1000 n j 4285 PLIPA2A140pp j 4286 s 0 n j 4286 PLIPA2A140pp_reverse_9fca8 j 4287 d 0 1000 n j 4287 PLIPA2A141pp j 4288 s 0 n j 4288 PLIPA2A141pp_reverse_c48d1 j 4289 d 0 1000 n j 4289 PLIPA2A160pp j 4290 s 0 n j 4290 PLIPA2A160pp_reverse_06e6b j 4291 d 0 1000 n j 4291 PLIPA2A161pp j 4292 s 0 n j 4292 PLIPA2A161pp_reverse_6424b j 4293 d 0 1000 n j 4293 PLIPA2A180pp j 4294 s 0 n j 4294 PLIPA2A180pp_reverse_8a7eb j 4295 d 0 1000 n j 4295 PLIPA2A181pp j 4296 s 0 n j 4296 PLIPA2A181pp_reverse_a384e j 4297 d 0 1000 n j 4297 PLIPA2E120pp j 4298 s 0 n j 4298 PLIPA2E120pp_reverse_01507 j 4299 d 0 1000 n j 4299 PLIPA2E140pp j 4300 s 0 n j 4300 PLIPA2E140pp_reverse_3c082 j 4301 d 0 1000 n j 4301 PLIPA2E141pp j 4302 s 0 n j 4302 PLIPA2E141pp_reverse_cb320 j 4303 d 0 1000 n j 4303 PLIPA2E160pp j 4304 s 0 n j 4304 PLIPA2E160pp_reverse_5dad9 j 4305 d 0 1000 n j 4305 PLIPA2E161pp j 4306 s 0 n j 4306 PLIPA2E161pp_reverse_47c6f j 4307 d 0 1000 n j 4307 PLIPA2E180pp j 4308 s 0 n j 4308 PLIPA2E180pp_reverse_b0d54 j 4309 d 0 1000 n j 4309 PLIPA2E181pp j 4310 s 0 n j 4310 PLIPA2E181pp_reverse_b2969 j 4311 d 0 1000 n j 4311 PLIPA2G120pp j 4312 s 0 n j 4312 PLIPA2G120pp_reverse_27cd5 j 4313 d 0 1000 n j 4313 PLIPA2G140pp j 4314 s 0 n j 4314 PLIPA2G140pp_reverse_c09b5 j 4315 d 0 1000 n j 4315 PLIPA2G141pp j 4316 s 0 n j 4316 PLIPA2G141pp_reverse_d1bc8 j 4317 d 0 1000 n j 4317 PLIPA2G160pp j 4318 s 0 n j 4318 PLIPA2G160pp_reverse_787b3 j 4319 d 0 1000 n j 4319 PLIPA2G161pp j 4320 s 0 n j 4320 PLIPA2G161pp_reverse_66ee8 j 4321 d 0 1000 n j 4321 PLIPA2G180pp j 4322 s 0 n j 4322 PLIPA2G180pp_reverse_a9dc2 j 4323 d 0 1000 n j 4323 PLIPA2G181pp j 4324 s 0 n j 4324 PLIPA2G181pp_reverse_c6379 j 4325 d 0 1000 n j 4325 PMANM j 4326 d 0 1000 n j 4326 PMANM_reverse_53eb0 j 4327 d 0 1000 n j 4327 PMDPHT j 4328 s 0 n j 4328 PMDPHT_reverse_8a0fd j 4329 d 0 1000 n j 4329 PMEACPE j 4330 s 0 n j 4330 PMEACPE_reverse_002c3 j 4331 d 0 1000 n j 4331 PMPK j 4332 s 0 n j 4332 PMPK_reverse_48b12 j 4333 d 0 1000 n j 4333 PNTK j 4334 s 0 n j 4334 PNTK_reverse_236b6 j 4335 d 0 1000 n j 4335 PNTOt4pp j 4336 s 0 n j 4336 PNTOt4pp_reverse_a34a9 j 4337 d 0 1000 n j 4337 PNTOtex j 4338 d 0 1000 n j 4338 PNTOtex_reverse_e5e15 j 4339 d 0 1000 n j 4339 POAACR j 4340 s 0 n j 4340 POAACR_reverse_7d724 j 4341 d 0 1000 n j 4341 POR5 j 4342 d 0 1000 n j 4342 POR5_reverse_fe67d j 4343 d 0 1000 n j 4343 POX j 4344 s 0 n j 4344 POX_reverse_35cf5 j 4345 d 0 1000 n j 4345 PPA j 4346 s 0 n j 4346 PPA_reverse_c5293 j 4347 d 0 1000 n j 4347 PPA2 j 4348 s 0 n j 4348 PPA2_reverse_cb6ee j 4349 d 0 1000 n j 4349 PPAKr j 4350 d 0 1000 n j 4350 PPAKr_reverse_aefb2 j 4351 d 0 1000 n j 4351 PPALtex j 4352 d 0 1000 n j 4352 PPALtex_reverse_cdcdf j 4353 d 0 1000 n j 4353 PPALtpp j 4354 d 0 1000 n j 4354 PPALtpp_reverse_3bfa7 j 4355 d 0 1000 n j 4355 PPAt4pp j 4356 s 0 n j 4356 PPAt4pp_reverse_ace84 j 4357 d 0 1000 n j 4357 PPAtex j 4358 d 0 1000 n j 4358 PPAtex_reverse_aa50f j 4359 d 0 1000 n j 4359 PPBNGS j 4360 s 0 n j 4360 PPBNGS_reverse_dc5a2 j 4361 d 0 1000 n j 4361 PPC j 4362 s 0 n j 4362 PPC_reverse_e854a j 4363 d 0 1000 n j 4363 PPCDC j 4364 s 0 n j 4364 PPCDC_reverse_39306 j 4365 d 0 1000 n j 4365 PPCK j 4366 s 0 n j 4366 PPCK_reverse_2557d j 4367 d 0 1000 n j 4367 PPCSCT j 4368 s 0 n j 4368 PPCSCT_reverse_8447c j 4369 d 0 1000 n j 4369 PPGPPDP j 4370 s 0 n j 4370 PPGPPDP_reverse_82153 j 4371 d 0 1000 n j 4371 PPK2 j 4372 d 0 1000 n j 4372 PPK2_reverse_3275d j 4373 d 0 1000 n j 4373 PPK j 4374 d 0 1000 n j 4374 PPK_reverse_69cd8 j 4375 d 0 1000 n j 4375 PPM j 4376 d 0 1000 n j 4376 PPM_reverse_4bb1e j 4377 d 0 1000 n j 4377 PPM2 j 4378 d 0 1000 n j 4378 PPM2_reverse_a5e20 j 4379 d 0 1000 n j 4379 PPNCL2 j 4380 s 0 n j 4380 PPNCL2_reverse_a65ee j 4381 d 0 1000 n j 4381 PPND j 4382 s 0 n j 4382 PPND_reverse_5463c j 4383 d 0 1000 n j 4383 PPNDH j 4384 s 0 n j 4384 PPNDH_reverse_58300 j 4385 d 0 1000 n j 4385 PPPGO j 4386 s 0 n j 4386 PPPGO_reverse_3a681 j 4387 d 0 1000 n j 4387 PPPGO3 j 4388 s 0 n j 4388 PPPGO3_reverse_ccd8c j 4389 d 0 1000 n j 4389 PPPNDO j 4390 s 0 n j 4390 PPPNDO_reverse_01e00 j 4391 d 0 1000 n j 4391 PPPNt2rpp j 4392 d 0 1000 n j 4392 PPPNt2rpp_reverse_b60aa j 4393 d 0 1000 n j 4393 PPPNtex j 4394 d 0 1000 n j 4394 PPPNtex_reverse_0c5bc j 4395 d 0 1000 n j 4395 PPS j 4396 s 0 n j 4396 PPS_reverse_1c319 j 4397 d 0 1000 n j 4397 PPTHpp j 4398 s 0 n j 4398 PPTHpp_reverse_ece28 j 4399 d 0 1000 n j 4399 PPTtex j 4400 d 0 1000 n j 4400 PPTtex_reverse_e0a66 j 4401 d 0 1000 n j 4401 PRAGSr j 4402 d 0 1000 n j 4402 PRAGSr_reverse_fd2d8 j 4403 d 0 1000 n j 4403 PRAIS j 4404 s 0 n j 4404 PRAIS_reverse_8e616 j 4405 d 0 1000 n j 4405 PRAIi j 4406 s 0 n j 4406 PRAIi_reverse_e568f j 4407 d 0 1000 n j 4407 PRAMPC j 4408 s 0 n j 4408 PRAMPC_reverse_54696 j 4409 d 0 1000 n j 4409 PRASCSi j 4410 s 0 n j 4410 PRASCSi_reverse_11704 j 4411 d 0 1000 n j 4411 PRATPP j 4412 s 0 n j 4412 PRATPP_reverse_99bf0 j 4413 d 0 1000 n j 4413 PRFGS j 4414 s 0 n j 4414 PRFGS_reverse_db4e5 j 4415 d 0 1000 n j 4415 PRMICI j 4416 d 0 1000 n j 4416 PRMICI_reverse_af0a9 j 4417 d 0 1000 n j 4417 PROD2 j 4418 s 0 n j 4418 PROD2_reverse_972fe j 4419 d 0 1000 n j 4419 PROGLYabcpp j 4420 s 0 n j 4420 PROGLYabcpp_reverse_dbb93 j 4421 d 0 1000 n j 4421 PROGLYtex j 4422 d 0 1000 n j 4422 PROGLYtex_reverse_098fe j 4423 d 0 1000 n j 4423 PROTRS j 4424 s 0 n j 4424 PROTRS_reverse_9d634 j 4425 d 0 1000 n j 4425 PROabcpp j 4426 s 0 n j 4426 PROabcpp_reverse_f67d8 j 4427 d 0 1000 n j 4427 PROt2rpp j 4428 d 0 1000 n j 4428 PROt2rpp_reverse_b5589 j 4429 d 0 1000 n j 4429 PROt4pp j 4430 s 0 n j 4430 PROt4pp_reverse_a83c2 j 4431 d 0 1000 n j 4431 PROtex j 4432 d 0 1000 n j 4432 PROtex_reverse_71ca0 j 4433 d 0 1000 n j 4433 PRPPS j 4434 d 0 1000 n j 4434 PRPPS_reverse_dd7f2 j 4435 d 0 1000 n j 4435 PSCLYSt2pp j 4436 s 0 n j 4436 PSCLYSt2pp_reverse_a94ad j 4437 d 0 1000 n j 4437 PSCLYStex j 4438 d 0 1000 n j 4438 PSCLYStex_reverse_c9eff j 4439 d 0 1000 n j 4439 PSCVT j 4440 d 0 1000 n j 4440 PSCVT_reverse_1a852 j 4441 d 0 1000 n j 4441 PSD120 j 4442 s 0 n j 4442 PSD120_reverse_167dc j 4443 d 0 1000 n j 4443 PSD140 j 4444 s 0 n j 4444 PSD140_reverse_a8f72 j 4445 d 0 1000 n j 4445 PSD141 j 4446 s 0 n j 4446 PSD141_reverse_1bafe j 4447 d 0 1000 n j 4447 PSD160 j 4448 s 0 n j 4448 PSD160_reverse_f80ad j 4449 d 0 1000 n j 4449 PSD161 j 4450 s 0 n j 4450 PSD161_reverse_daeb9 j 4451 d 0 1000 n j 4451 PSD180 j 4452 s 0 n j 4452 PSD180_reverse_a7b08 j 4453 d 0 1000 n j 4453 PSD181 j 4454 s 0 n j 4454 PSD181_reverse_8b615 j 4455 d 0 1000 n j 4455 PSERT j 4456 s 0 n j 4456 PSERT_reverse_cbee4 j 4457 d 0 1000 n j 4457 PSERtex j 4458 d 0 1000 n j 4458 PSERtex_reverse_36126 j 4459 d 0 1000 n j 4459 PSP_L j 4460 s 0 n j 4460 PSP_L_reverse_cfa3c j 4461 d 0 1000 n j 4461 PSP_Lpp j 4462 s 0 n j 4462 PSP_Lpp_reverse_9456f j 4463 d 0 1000 n j 4463 PSSA120 j 4464 s 0 n j 4464 PSSA120_reverse_146fc j 4465 d 0 1000 n j 4465 PSSA140 j 4466 s 0 n j 4466 PSSA140_reverse_8cef4 j 4467 d 0 1000 n j 4467 PSSA141 j 4468 s 0 n j 4468 PSSA141_reverse_7ab04 j 4469 d 0 1000 n j 4469 PSSA160 j 4470 s 0 n j 4470 PSSA160_reverse_f5fc1 j 4471 d 0 1000 n j 4471 PSSA161 j 4472 s 0 n j 4472 PSSA161_reverse_cbe33 j 4473 d 0 1000 n j 4473 PSSA180 j 4474 s 0 n j 4474 PSSA180_reverse_e607c j 4475 d 0 1000 n j 4475 PSSA181 j 4476 s 0 n j 4476 PSSA181_reverse_83a3e j 4477 d 0 1000 n j 4477 PTA2 j 4478 s 0 n j 4478 PTA2_reverse_720d5 j 4479 d 0 1000 n j 4479 PTAr j 4480 d 0 1000 n j 4480 PTAr_reverse_fce15 j 4481 d 0 1000 n j 4481 PTHRpp j 4482 s 0 n j 4482 PTHRpp_reverse_80890 j 4483 d 0 1000 n j 4483 PTPATi j 4484 s 0 n j 4484 PTPATi_reverse_381d9 j 4485 d 0 1000 n j 4485 PTRCORNt7pp j 4486 d 0 1000 n j 4486 PTRCORNt7pp_reverse_b20e8 j 4487 d 0 1000 n j 4487 PTRCTA j 4488 s 0 n j 4488 PTRCTA_reverse_1e90c j 4489 d 0 1000 n j 4489 PTRCabcpp j 4490 s 0 n j 4490 PTRCabcpp_reverse_96b27 j 4491 d 0 1000 n j 4491 PTRCt2pp j 4492 s 0 n j 4492 PTRCt2pp_reverse_0b90f j 4493 d 0 1000 n j 4493 PTRCtex j 4494 d 0 1000 n j 4494 PTRCtex_reverse_a3bd6 j 4495 d 0 1000 n j 4495 PUNP1 j 4496 d 0 1000 n j 4496 PUNP1_reverse_e404e j 4497 d 0 1000 n j 4497 PUNP2 j 4498 d 0 1000 n j 4498 PUNP2_reverse_af3de j 4499 d 0 1000 n j 4499 PUNP3 j 4500 d 0 1000 n j 4500 PUNP3_reverse_1db03 j 4501 d 0 1000 n j 4501 PUNP4 j 4502 d 0 1000 n j 4502 PUNP4_reverse_e2a37 j 4503 d 0 1000 n j 4503 PUNP5 j 4504 d 0 1000 n j 4504 PUNP5_reverse_e49fe j 4505 d 0 1000 n j 4505 PUNP6 j 4506 d 0 1000 n j 4506 PUNP6_reverse_00238 j 4507 d 0 1000 n j 4507 PUNP7 j 4508 d 0 1000 n j 4508 PUNP7_reverse_05926 j 4509 d 0 1000 n j 4509 PYAM5PO j 4510 s 0 n j 4510 PYAM5PO_reverse_d008c j 4511 d 0 1000 n j 4511 PYDAMK j 4512 s 0 n j 4512 PYDAMK_reverse_a0071 j 4513 d 0 1000 n j 4513 PYDAMtex j 4514 d 0 1000 n j 4514 PYDAMtex_reverse_03aa6 j 4515 d 0 1000 n j 4515 PYDAMtpp j 4516 s 0 n j 4516 PYDAMtpp_reverse_3412a j 4517 d 0 1000 n j 4517 PYDXK j 4518 s 0 n j 4518 PYDXK_reverse_6e9be j 4519 d 0 1000 n j 4519 PYDXNK j 4520 s 0 n j 4520 PYDXNK_reverse_b5b8e j 4521 d 0 1000 n j 4521 PYDXNtex j 4522 d 0 1000 n j 4522 PYDXNtex_reverse_c98ca j 4523 d 0 1000 n j 4523 PYDXNtpp j 4524 s 0 n j 4524 PYDXNtpp_reverse_6fb4f j 4525 d 0 1000 n j 4525 PYDXPP j 4526 s 0 n j 4526 PYDXPP_reverse_26730 j 4527 d 0 1000 n j 4527 PYDXtex j 4528 d 0 1000 n j 4528 PYDXtex_reverse_1b0e1 j 4529 d 0 1000 n j 4529 PYDXtpp j 4530 s 0 n j 4530 PYDXtpp_reverse_58147 j 4531 d 0 1000 n j 4531 PYK j 4532 s 0 n j 4532 PYK_reverse_bc8ff j 4533 d 0 1000 n j 4533 PYNP2r j 4534 d 0 1000 n j 4534 PYNP2r_reverse_d20f8 j 4535 d 0 1000 n j 4535 PYROX j 4536 s 0 n j 4536 PYROX_reverse_df090 j 4537 d 0 1000 n j 4537 PYRt2rpp j 4538 d 0 1000 n j 4538 PYRt2rpp_reverse_3baab j 4539 d 0 1000 n j 4539 PYRtex j 4540 d 0 1000 n j 4540 PYRtex_reverse_59eef j 4541 d 0 1000 n j 4541 QMO2 j 4542 s 0 n j 4542 QMO2_reverse_62073 j 4543 d 0 1000 n j 4543 QMO3 j 4544 s 0 n j 4544 QMO3_reverse_81f19 j 4545 d 0 1000 n j 4545 QUIN2tex j 4546 d 0 1000 n j 4546 QUIN2tex_reverse_c1717 j 4547 d 0 1000 n j 4547 QUIN2tpp j 4548 d 0 1000 n j 4548 QUIN2tpp_reverse_a8d27 j 4549 d 0 1000 n j 4549 QUINDH j 4550 d 0 1000 n j 4550 QUINDH_reverse_3ca4c j 4551 d 0 1000 n j 4551 QULNS j 4552 s 0 n j 4552 QULNS_reverse_66da1 j 4553 d 0 1000 n j 4553 R15BPK j 4554 s 0 n j 4554 R15BPK_reverse_37801 j 4555 d 0 1000 n j 4555 R1PK j 4556 s 0 n j 4556 R1PK_reverse_1c909 j 4557 d 0 1000 n j 4557 R5PP j 4558 s 0 n j 4558 R5PP_reverse_475d3 j 4559 d 0 1000 n j 4559 R5PPpp j 4560 s 0 n j 4560 R5PPpp_reverse_13c4d j 4561 d 0 1000 n j 4561 R5Ptex j 4562 d 0 1000 n j 4562 R5Ptex_reverse_cbfca j 4563 d 0 1000 n j 4563 RBFK j 4564 s 0 n j 4564 RBFK_reverse_8faa7 j 4565 d 0 1000 n j 4565 RBFSa j 4566 s 0 n j 4566 RBFSa_reverse_61d96 j 4567 d 0 1000 n j 4567 RBFSb j 4568 s 0 n j 4568 RBFSb_reverse_32299 j 4569 d 0 1000 n j 4569 RBK j 4570 s 0 n j 4570 RBK_reverse_ee934 j 4571 d 0 1000 n j 4571 RBK_L1 j 4572 s 0 n j 4572 RBK_L1_reverse_7ee06 j 4573 d 0 1000 n j 4573 RBP4E j 4574 d 0 1000 n j 4574 RBP4E_reverse_12591 j 4575 d 0 1000 n j 4575 REPHACCOAI j 4576 d 0 1000 n j 4576 REPHACCOAI_reverse_4d4b5 j 4577 d 0 1000 n j 4577 RFAMPtex j 4578 d 0 1000 n j 4578 RFAMPtex_reverse_202c2 j 4579 d 0 1000 n j 4579 RFAMPtpp j 4580 s 0 n j 4580 RFAMPtpp_reverse_64e26 j 4581 d 0 1000 n j 4581 RHAT1 j 4582 s 0 n j 4582 RHAT1_reverse_0bb45 j 4583 d 0 1000 n j 4583 RHCCE j 4584 s 0 n j 4584 RHCCE_reverse_e91ee j 4585 d 0 1000 n j 4585 RIBabcpp j 4586 s 0 n j 4586 RIBabcpp_reverse_e1bf5 j 4587 d 0 1000 n j 4587 RIBtex j 4588 d 0 1000 n j 4588 RIBtex_reverse_42338 j 4589 d 0 1000 n j 4589 RMI j 4590 d 0 1000 n j 4590 RMI_reverse_ab7c1 j 4591 d 0 1000 n j 4591 RMK j 4592 s 0 n j 4592 RMK_reverse_f9a9f j 4593 d 0 1000 n j 4593 RMNtex j 4594 d 0 1000 n j 4594 RMNtex_reverse_90350 j 4595 d 0 1000 n j 4595 RMNtpp j 4596 s 0 n j 4596 RMNtpp_reverse_40417 j 4597 d 0 1000 n j 4597 RMPA j 4598 d 0 1000 n j 4598 RMPA_reverse_a5284 j 4599 d 0 1000 n j 4599 RNDR1 j 4600 s 0 n j 4600 RNDR1_reverse_f4be1 j 4601 d 0 1000 n j 4601 RNDR1b j 4602 s 0 n j 4602 RNDR1b_reverse_59a84 j 4603 d 0 1000 n j 4603 RNDR2 j 4604 s 0 n j 4604 RNDR2_reverse_7df82 j 4605 d 0 1000 n j 4605 RNDR2b j 4606 s 0 n j 4606 RNDR2b_reverse_73295 j 4607 d 0 1000 n j 4607 RNDR3 j 4608 s 0 n j 4608 RNDR3_reverse_bc84a j 4609 d 0 1000 n j 4609 RNDR3b j 4610 s 0 n j 4610 RNDR3b_reverse_036ef j 4611 d 0 1000 n j 4611 RNDR4 j 4612 s 0 n j 4612 RNDR4_reverse_aff84 j 4613 d 0 1000 n j 4613 RNDR4b j 4614 s 0 n j 4614 RNDR4b_reverse_9c1a0 j 4615 d 0 1000 n j 4615 RNTR1c2 j 4616 s 0 n j 4616 RNTR1c2_reverse_b4b14 j 4617 d 0 1000 n j 4617 RNTR2c2 j 4618 s 0 n j 4618 RNTR2c2_reverse_b6d45 j 4619 d 0 1000 n j 4619 RNTR3c2 j 4620 s 0 n j 4620 RNTR3c2_reverse_8ada2 j 4621 d 0 1000 n j 4621 RNTR4c2 j 4622 s 0 n j 4622 RNTR4c2_reverse_0f7f0 j 4623 d 0 1000 n j 4623 RPE j 4624 d 0 1000 n j 4624 RPE_reverse_a1b04 j 4625 d 0 1000 n j 4625 RPI j 4626 d 0 1000 n j 4626 RPI_reverse_853a1 j 4627 d 0 1000 n j 4627 RZ5PP j 4628 s 0 n j 4628 RZ5PP_reverse_b2942 j 4629 d 0 1000 n j 4629 S2FE2SR j 4630 s 0 n j 4630 S2FE2SR_reverse_7a140 j 4631 d 0 1000 n j 4631 S2FE2SS j 4632 s 0 n j 4632 S2FE2SS_reverse_dbd1b j 4633 d 0 1000 n j 4633 S2FE2SS2 j 4634 s 0 n j 4634 S2FE2SS2_reverse_db43c j 4635 d 0 1000 n j 4635 S2FE2ST j 4636 s 0 n j 4636 S2FE2ST_reverse_557c4 j 4637 d 0 1000 n j 4637 S4FE4SR j 4638 s 0 n j 4638 S4FE4SR_reverse_9a130 j 4639 d 0 1000 n j 4639 S4FE4ST j 4640 s 0 n j 4640 S4FE4ST_reverse_caa0d j 4641 d 0 1000 n j 4641 S7PI j 4642 s 0 n j 4642 S7PI_reverse_6ace9 j 4643 d 0 1000 n j 4643 SADH j 4644 s 0 n j 4644 SADH_reverse_a0164 j 4645 d 0 1000 n j 4645 SADT2 j 4646 s 0 n j 4646 SADT2_reverse_2632d j 4647 d 0 1000 n j 4647 SARCOX j 4648 s 0 n j 4648 SARCOX_reverse_eebad j 4649 d 0 1000 n j 4649 SBTPD j 4650 d 0 1000 n j 4650 SBTPD_reverse_9a7da j 4651 d 0 1000 n j 4651 SBTptspp j 4652 s 0 n j 4652 SBTptspp_reverse_05c76 j 4653 d 0 1000 n j 4653 SBTtex j 4654 d 0 1000 n j 4654 SBTtex_reverse_6aeda j 4655 d 0 1000 n j 4655 SCYSDS j 4656 s 0 n j 4656 SCYSDS_reverse_3bb75 j 4657 d 0 1000 n j 4657 SDPDS j 4658 s 0 n j 4658 SDPDS_reverse_43d25 j 4659 d 0 1000 n j 4659 SDPTA j 4660 d 0 1000 n j 4660 SDPTA_reverse_76834 j 4661 d 0 1000 n j 4661 SELCYSS j 4662 s 0 n j 4662 SELCYSS_reverse_2f401 j 4663 d 0 1000 n j 4663 SELGTHR j 4664 s 0 n j 4664 SELGTHR_reverse_87696 j 4665 d 0 1000 n j 4665 SELGTHR2 j 4666 s 0 n j 4666 SELGTHR2_reverse_076d8 j 4667 d 0 1000 n j 4667 SELGTHR3 j 4668 s 0 n j 4668 SELGTHR3_reverse_02c0a j 4669 d 0 1000 n j 4669 SELNPS j 4670 s 0 n j 4670 SELNPS_reverse_498bb j 4671 d 0 1000 n j 4671 SELR j 4672 s 0 n j 4672 SELR_reverse_c1bf1 j 4673 d 0 1000 n j 4673 SELtex j 4674 d 0 1000 n j 4674 SELtex_reverse_316ff j 4675 d 0 1000 n j 4675 SELtpp j 4676 s 0 n j 4676 SELtpp_reverse_466c6 j 4677 d 0 1000 n j 4677 SEPHCHCS j 4678 s 0 n j 4678 SEPHCHCS_reverse_cb185 j 4679 d 0 1000 n j 4679 SERASr j 4680 d 0 1000 n j 4680 SERASr_reverse_eb9c5 j 4681 d 0 1000 n j 4681 SERAT j 4682 d 0 1000 n j 4682 SERAT_reverse_0de5e j 4683 d 0 1000 n j 4683 SERD_D j 4684 s 0 n j 4684 SERD_D_reverse_0aea1 j 4685 d 0 1000 n j 4685 SERD_L j 4686 s 0 n j 4686 SERD_L_reverse_0f0ab j 4687 d 0 1000 n j 4687 SERTRS j 4688 s 0 n j 4688 SERTRS_reverse_b65b1 j 4689 d 0 1000 n j 4689 SERTRS2 j 4690 s 0 n j 4690 SERTRS2_reverse_0c19b j 4691 d 0 1000 n j 4691 SERt2rpp j 4692 d 0 1000 n j 4692 SERt2rpp_reverse_94979 j 4693 d 0 1000 n j 4693 SERt4pp j 4694 s 0 n j 4694 SERt4pp_reverse_0b585 j 4695 d 0 1000 n j 4695 SERtex j 4696 d 0 1000 n j 4696 SERtex_reverse_85c6a j 4697 d 0 1000 n j 4697 SFGTHi j 4698 s 0 n j 4698 SFGTHi_reverse_71e0b j 4699 d 0 1000 n j 4699 SGDS j 4700 s 0 n j 4700 SGDS_reverse_25ac4 j 4701 d 0 1000 n j 4701 SGSAD j 4702 s 0 n j 4702 SGSAD_reverse_57781 j 4703 d 0 1000 n j 4703 SHCHCS3 j 4704 s 0 n j 4704 SHCHCS3_reverse_fb361 j 4705 d 0 1000 n j 4705 SHCHD2 j 4706 s 0 n j 4706 SHCHD2_reverse_d3585 j 4707 d 0 1000 n j 4707 SHCHF j 4708 s 0 n j 4708 SHCHF_reverse_fbf31 j 4709 d 0 1000 n j 4709 SHK3Dr j 4710 d 0 1000 n j 4710 SHK3Dr_reverse_d5c8f j 4711 d 0 1000 n j 4711 SHKK j 4712 s 0 n j 4712 SHKK_reverse_163fd j 4713 d 0 1000 n j 4713 SHSL1 j 4714 s 0 n j 4714 SHSL1_reverse_22e26 j 4715 d 0 1000 n j 4715 SKMt2pp j 4716 s 0 n j 4716 SKMt2pp_reverse_b0b41 j 4717 d 0 1000 n j 4717 SKMtex j 4718 d 0 1000 n j 4718 SKMtex_reverse_af580 j 4719 d 0 1000 n j 4719 SLNTtex j 4720 d 0 1000 n j 4720 SLNTtex_reverse_17a8f j 4721 d 0 1000 n j 4721 SLNTtpp j 4722 s 0 n j 4722 SLNTtpp_reverse_55a3f j 4723 d 0 1000 n j 4723 SO2tex j 4724 d 0 1000 n j 4724 SO2tex_reverse_0dea8 j 4725 d 0 1000 n j 4725 SO2tpp j 4726 d 0 1000 n j 4726 SO2tpp_reverse_860e1 j 4727 d 0 1000 n j 4727 SO3tex j 4728 d 0 1000 n j 4728 SO3tex_reverse_94e61 j 4729 d 0 1000 n j 4729 SO4t2pp j 4730 s 0 n j 4730 SO4t2pp_reverse_02fd3 j 4731 d 0 1000 n j 4731 SO4tex j 4732 d 0 1000 n j 4732 SO4tex_reverse_1908f j 4733 d 0 1000 n j 4733 SOTA j 4734 s 0 n j 4734 SOTA_reverse_98c5d j 4735 d 0 1000 n j 4735 SPMDAT1 j 4736 s 0 n j 4736 SPMDAT1_reverse_5edba j 4737 d 0 1000 n j 4737 SPMDAT2 j 4738 s 0 n j 4738 SPMDAT2_reverse_1a39e j 4739 d 0 1000 n j 4739 SPMDabcpp j 4740 s 0 n j 4740 SPMDabcpp_reverse_7abfe j 4741 d 0 1000 n j 4741 SPMDt3pp j 4742 s 0 n j 4742 SPMDt3pp_reverse_9cb9f j 4743 d 0 1000 n j 4743 SPMDtex j 4744 d 0 1000 n j 4744 SPMDtex_reverse_5a2d5 j 4745 d 0 1000 n j 4745 SPMS j 4746 s 0 n j 4746 SPMS_reverse_92c51 j 4747 s 0 n j 4747 SPODM j 4748 s 0 n j 4748 SPODM_reverse_2648f j 4749 s 0 n j 4749 SPODMpp j 4750 s 0 n j 4750 SPODMpp_reverse_a61b9 j 4751 d 0 1000 n j 4751 SSALx j 4752 s 0 n j 4752 SSALx_reverse_25de3 j 4753 d 0 1000 n j 4753 SSALy j 4754 s 0 n j 4754 SSALy_reverse_c02ab j 4755 s 0 n j 4755 SUCASPtpp j 4756 s 0 n j 4756 SUCASPtpp_reverse_bb0a7 j 4757 d 0 1000 n j 4757 SUCBZL j 4758 s 0 n j 4758 SUCBZL_reverse_536e6 j 4759 d 0 1000 n j 4759 SUCBZS j 4760 s 0 n j 4760 SUCBZS_reverse_cdbc9 j 4761 d 0 1000 n j 4761 SUCCt2_2pp j 4762 s 0 n j 4762 SUCCt2_2pp_reverse_bb10d j 4763 d 0 1000 n j 4763 SUCCt2_3pp j 4764 s 0 n j 4764 SUCCt2_3pp_reverse_1fb63 j 4765 d 0 1000 n j 4765 SUCCt3pp j 4766 s 0 n j 4766 SUCCt3pp_reverse_5b004 j 4767 d 0 1000 n j 4767 SUCCtex j 4768 d 0 1000 n j 4768 SUCCtex_reverse_9b687 j 4769 d 0 1000 n j 4769 SUCDi j 4770 s 0 n j 4770 SUCDi_reverse_480f4 j 4771 s 0 n j 4771 SUCFUMtpp j 4772 s 0 n j 4772 SUCFUMtpp_reverse_ace02 j 4773 s 0 n j 4773 SUCMALtpp j 4774 s 0 n j 4774 SUCMALtpp_reverse_e457b j 4775 d 0 1000 n j 4775 SUCOAS j 4776 d 0 1000 n j 4776 SUCOAS_reverse_22958 j 4777 d 0 1000 n j 4777 SUCRtex j 4778 d 0 1000 n j 4778 SUCRtex_reverse_77415 j 4779 s 0 n j 4779 SUCTARTtpp j 4780 s 0 n j 4780 SUCTARTtpp_reverse_d1f18 j 4781 d 0 1000 n j 4781 SUCptspp j 4782 s 0 n j 4782 SUCptspp_reverse_66a2f j 4783 d 0 1000 n j 4783 SULFACabcpp j 4784 s 0 n j 4784 SULFACabcpp_reverse_c4992 j 4785 d 0 1000 n j 4785 SULFACtex j 4786 d 0 1000 n j 4786 SULFACtex_reverse_7636a j 4787 d 0 1000 n j 4787 SULR j 4788 s 0 n j 4788 SULR_reverse_12727 j 4789 d 0 1000 n j 4789 SULabcpp j 4790 s 0 n j 4790 SULabcpp_reverse_40679 j 4791 d 0 1000 n j 4791 T2DECAI j 4792 d 0 1000 n j 4792 T2DECAI_reverse_565c3 j 4793 d 0 1000 n j 4793 TAGURr j 4794 d 0 1000 n j 4794 TAGURr_reverse_82d85 j 4795 d 0 1000 n j 4795 TALA j 4796 d 0 1000 n j 4796 TALA_reverse_adfda j 4797 d 0 1000 n j 4797 TARTD j 4798 s 0 n j 4798 TARTD_reverse_66ff2 j 4799 d 0 1000 n j 4799 TARTRDtex j 4800 d 0 1000 n j 4800 TARTRDtex_reverse_2e0b9 j 4801 d 0 1000 n j 4801 TARTRt7pp j 4802 d 0 1000 n j 4802 TARTRt7pp_reverse_ece0a j 4803 d 0 1000 n j 4803 TARTRtex j 4804 d 0 1000 n j 4804 TARTRtex_reverse_41e85 j 4805 d 0 1000 n j 4805 TARTt2_3pp j 4806 s 0 n j 4806 TARTt2_3pp_reverse_d5a3c j 4807 d 0 1000 n j 4807 TAUDO j 4808 s 0 n j 4808 TAUDO_reverse_465a8 j 4809 d 0 1000 n j 4809 TAURabcpp j 4810 s 0 n j 4810 TAURabcpp_reverse_84498 j 4811 d 0 1000 n j 4811 TAURtex j 4812 d 0 1000 n j 4812 TAURtex_reverse_a14b6 j 4813 d 0 1000 n j 4813 TCYNTtex j 4814 d 0 1000 n j 4814 TCYNTtex_reverse_903a3 j 4815 d 0 1000 n j 4815 TDP j 4816 s 0 n j 4816 TDP_reverse_5ec7c j 4817 d 0 1000 n j 4817 TDPADGAT j 4818 s 0 n j 4818 TDPADGAT_reverse_b1c03 j 4819 d 0 1000 n j 4819 TDPAGTA j 4820 s 0 n j 4820 TDPAGTA_reverse_0f964 j 4821 d 0 1000 n j 4821 TDPDRE j 4822 s 0 n j 4822 TDPDRE_reverse_26405 j 4823 d 0 1000 n j 4823 TDPDRR j 4824 s 0 n j 4824 TDPDRR_reverse_e7bd2 j 4825 d 0 1000 n j 4825 TDPGDH j 4826 s 0 n j 4826 TDPGDH_reverse_f570d j 4827 d 0 1000 n j 4827 TDSK j 4828 s 0 n j 4828 TDSK_reverse_4bbc5 j 4829 d 0 1000 n j 4829 TDSR1 j 4830 s 0 n j 4830 TDSR1_reverse_270e9 j 4831 d 0 1000 n j 4831 TDSR2 j 4832 s 0 n j 4832 TDSR2_reverse_b23c2 j 4833 d 0 1000 n j 4833 TGBPA j 4834 d 0 1000 n j 4834 TGBPA_reverse_3cfab j 4835 d 0 1000 n j 4835 THD2pp j 4836 s 0 n j 4836 THD2pp_reverse_e68d7 j 4837 d 0 1000 n j 4837 THDPS j 4838 s 0 n j 4838 THDPS_reverse_41a90 j 4839 d 0 1000 n j 4839 THFAT j 4840 s 0 n j 4840 THFAT_reverse_463de j 4841 d 0 1000 n j 4841 THIORDXi j 4842 s 0 n j 4842 THIORDXi_reverse_27f13 j 4843 d 0 1000 n j 4843 THMDt2pp_copy1 j 4844 s 0 n j 4844 THMDt2pp_copy1_reverse_42ba2 j 4845 d 0 1000 n j 4845 THMDt2pp_copy2 j 4846 d 0 1000 n j 4846 THMDt2pp_copy2_reverse_d1651 j 4847 d 0 1000 n j 4847 THMDtex j 4848 d 0 1000 n j 4848 THMDtex_reverse_e82c0 j 4849 d 0 1000 n j 4849 THMabcpp j 4850 s 0 n j 4850 THMabcpp_reverse_f17bf j 4851 d 0 1000 n j 4851 THMtex j 4852 d 0 1000 n j 4852 THMtex_reverse_1e778 j 4853 d 0 1000 n j 4853 THRA2 j 4854 s 0 n j 4854 THRA2_reverse_bb206 j 4855 d 0 1000 n j 4855 THRA j 4856 s 0 n j 4856 THRA_reverse_549e7 j 4857 d 0 1000 n j 4857 THRD j 4858 s 0 n j 4858 THRD_reverse_83253 j 4859 d 0 1000 n j 4859 THRD_L j 4860 s 0 n j 4860 THRD_L_reverse_4c55d j 4861 d 0 1000 n j 4861 THRPtex j 4862 d 0 1000 n j 4862 THRPtex_reverse_2f25b j 4863 d 0 1000 n j 4863 THRS j 4864 s 0 n j 4864 THRS_reverse_a994c j 4865 d 0 1000 n j 4865 THRTRS j 4866 s 0 n j 4866 THRTRS_reverse_12237 j 4867 d 0 1000 n j 4867 THRabcpp j 4868 s 0 n j 4868 THRabcpp_reverse_41c99 j 4869 d 0 1000 n j 4869 THRt2pp j 4870 s 0 n j 4870 THRt2pp_reverse_7cdd2 j 4871 d 0 1000 n j 4871 THRt2rpp j 4872 d 0 1000 n j 4872 THRt2rpp_reverse_b03e3 j 4873 d 0 1000 n j 4873 THRt4pp j 4874 s 0 n j 4874 THRt4pp_reverse_394e5 j 4875 d 0 1000 n j 4875 THRtex j 4876 d 0 1000 n j 4876 THRtex_reverse_2a874 j 4877 d 0 1000 n j 4877 THYMt3pp j 4878 s 0 n j 4878 THYMt3pp_reverse_be8cd j 4879 d 0 1000 n j 4879 THYMtex j 4880 d 0 1000 n j 4880 THYMtex_reverse_1b098 j 4881 d 0 1000 n j 4881 THZPSN3 j 4882 s 0 n j 4882 THZPSN3_reverse_90214 j 4883 d 0 1000 n j 4883 TKT1 j 4884 d 0 1000 n j 4884 TKT1_reverse_a1021 j 4885 d 0 1000 n j 4885 TKT2 j 4886 d 0 1000 n j 4886 TKT2_reverse_7ebc7 j 4887 d 0 1000 n j 4887 TMAOR1 j 4888 s 0 n j 4888 TMAOR1_reverse_ba0cf j 4889 d 0 1000 n j 4889 TMAOR1pp j 4890 s 0 n j 4890 TMAOR1pp_reverse_dafd5 j 4891 d 0 1000 n j 4891 TMAOR2 j 4892 s 0 n j 4892 TMAOR2_reverse_ad798 j 4893 d 0 1000 n j 4893 TMAOR2pp j 4894 s 0 n j 4894 TMAOR2pp_reverse_d7195 j 4895 d 0 1000 n j 4895 TMAOtex j 4896 d 0 1000 n j 4896 TMAOtex_reverse_4519a j 4897 d 0 1000 n j 4897 TMAtex j 4898 d 0 1000 n j 4898 TMAtex_reverse_4c54e j 4899 d 0 1000 n j 4899 TMDK1 j 4900 s 0 n j 4900 TMDK1_reverse_ecb2e j 4901 d 0 1000 n j 4901 TMDPP j 4902 d 0 1000 n j 4902 TMDPP_reverse_1aa90 j 4903 d 0 1000 n j 4903 TMDS j 4904 s 0 n j 4904 TMDS_reverse_0a1f4 j 4905 d 0 1000 n j 4905 TMK j 4906 s 0 n j 4906 TMK_reverse_99629 j 4907 d 0 1000 n j 4907 TMPK j 4908 s 0 n j 4908 TMPK_reverse_b7673 j 4909 d 0 1000 n j 4909 TMPPP j 4910 s 0 n j 4910 TMPPP_reverse_f275c j 4911 d 0 1000 n j 4911 TPI j 4912 d 0 1000 n j 4912 TPI_reverse_c2c3b j 4913 d 0 1000 n j 4913 TPRDCOAS j 4914 s 0 n j 4914 TPRDCOAS_reverse_56965 j 4915 d 0 1000 n j 4915 TRDR j 4916 s 0 n j 4916 TRDR_reverse_6372e j 4917 d 0 1000 n j 4917 TRE6PH j 4918 s 0 n j 4918 TRE6PH_reverse_ba9c2 j 4919 d 0 1000 n j 4919 TRE6PP j 4920 s 0 n j 4920 TRE6PP_reverse_4fe3e j 4921 d 0 1000 n j 4921 TRE6PS j 4922 s 0 n j 4922 TRE6PS_reverse_96346 j 4923 d 0 1000 n j 4923 TREH j 4924 s 0 n j 4924 TREH_reverse_3db52 j 4925 d 0 1000 n j 4925 TREHpp j 4926 s 0 n j 4926 TREHpp_reverse_a400f j 4927 d 0 1000 n j 4927 TREptspp j 4928 s 0 n j 4928 TREptspp_reverse_dc50f j 4929 d 0 1000 n j 4929 TREtex j 4930 d 0 1000 n j 4930 TREtex_reverse_1e6cc j 4931 d 0 1000 n j 4931 TRPAS2 j 4932 d 0 1000 n j 4932 TRPAS2_reverse_d1c71 j 4933 d 0 1000 n j 4933 TRPS1 j 4934 s 0 n j 4934 TRPS1_reverse_35c22 j 4935 d 0 1000 n j 4935 TRPS2 j 4936 s 0 n j 4936 TRPS2_reverse_cd73f j 4937 d 0 1000 n j 4937 TRPS3 j 4938 s 0 n j 4938 TRPS3_reverse_bdfbb j 4939 d 0 1000 n j 4939 TRPTRS j 4940 s 0 n j 4940 TRPTRS_reverse_f29b7 j 4941 d 0 1000 n j 4941 TRPt2rpp j 4942 d 0 1000 n j 4942 TRPt2rpp_reverse_ff3d1 j 4943 d 0 1000 n j 4943 TRPtex j 4944 d 0 1000 n j 4944 TRPtex_reverse_e7b4f j 4945 d 0 1000 n j 4945 TRSARr j 4946 d 0 1000 n j 4946 TRSARr_reverse_ac605 j 4947 d 0 1000 n j 4947 TSULabcpp j 4948 s 0 n j 4948 TSULabcpp_reverse_1aea7 j 4949 d 0 1000 n j 4949 TSULtex j 4950 d 0 1000 n j 4950 TSULtex_reverse_0b131 j 4951 d 0 1000 n j 4951 TTDCAtexi j 4952 s 0 n j 4952 TTDCAtexi_reverse_a3936 j 4953 d 0 1000 n j 4953 TTDCEAtexi j 4954 s 0 n j 4954 TTDCEAtexi_reverse_8b32c j 4955 d 0 1000 n j 4955 TTRCYCtex j 4956 d 0 1000 n j 4956 TTRCYCtex_reverse_64a12 j 4957 d 0 1000 n j 4957 TTRCYCtpp j 4958 s 0 n j 4958 TTRCYCtpp_reverse_16a56 j 4959 d 0 1000 n j 4959 TUNGSabcpp j 4960 s 0 n j 4960 TUNGSabcpp_reverse_a2be8 j 4961 d 0 1000 n j 4961 TUNGStex j 4962 d 0 1000 n j 4962 TUNGStex_reverse_30dbf j 4963 d 0 1000 n j 4963 TYMtex j 4964 d 0 1000 n j 4964 TYMtex_reverse_996e2 j 4965 d 0 1000 n j 4965 TYRL j 4966 s 0 n j 4966 TYRL_reverse_b74b0 j 4967 d 0 1000 n j 4967 TYROXDApp j 4968 s 0 n j 4968 TYROXDApp_reverse_1bd49 j 4969 d 0 1000 n j 4969 TYRPpp j 4970 s 0 n j 4970 TYRPpp_reverse_0b842 j 4971 d 0 1000 n j 4971 TYRPtex j 4972 d 0 1000 n j 4972 TYRPtex_reverse_208a8 j 4973 d 0 1000 n j 4973 TYRTA j 4974 d 0 1000 n j 4974 TYRTA_reverse_e9311 j 4975 d 0 1000 n j 4975 TYRTRS j 4976 s 0 n j 4976 TYRTRS_reverse_27d64 j 4977 d 0 1000 n j 4977 TYRt2rpp j 4978 d 0 1000 n j 4978 TYRt2rpp_reverse_cf011 j 4979 d 0 1000 n j 4979 TYRtex j 4980 d 0 1000 n j 4980 TYRtex_reverse_6da66 j 4981 d 0 1000 n j 4981 ThDPAT j 4982 s 0 n j 4982 ThDPAT_reverse_ae33f j 4983 d 0 1000 n j 4983 U23GAAT j 4984 s 0 n j 4984 U23GAAT_reverse_0353e j 4985 d 0 1000 n j 4985 UAAGDS j 4986 s 0 n j 4986 UAAGDS_reverse_313a9 j 4987 d 0 1000 n j 4987 UACGALPpp j 4988 s 0 n j 4988 UACGALPpp_reverse_840fa j 4989 d 0 1000 n j 4989 UACGAMPpp j 4990 s 0 n j 4990 UACGAMPpp_reverse_b239e j 4991 d 0 1000 n j 4991 UACGAMtex j 4992 d 0 1000 n j 4992 UACGAMtex_reverse_0aadf j 4993 d 0 1000 n j 4993 UACMAMO j 4994 s 0 n j 4994 UACMAMO_reverse_b0219 j 4995 d 0 1000 n j 4995 UAG2E j 4996 d 0 1000 n j 4996 UAG2E_reverse_83643 j 4997 d 0 1000 n j 4997 UAGAAT j 4998 d 0 1000 n j 4998 UAGAAT_reverse_24f8b j 4999 d 0 1000 n j 4999 UAGCVT j 5000 s 0 n j 5000 UAGCVT_reverse_ba1ab j 5001 d 0 1000 n j 5001 UAGDP j 5002 s 0 n j 5002 UAGDP_reverse_a5ec0 j 5003 d 0 1000 n j 5003 UAGPT3 j 5004 s 0 n j 5004 UAGPT3_reverse_7f3f7 j 5005 d 0 1000 n j 5005 UAMAGS j 5006 s 0 n j 5006 UAMAGS_reverse_a0d94 j 5007 d 0 1000 n j 5007 UAMAS j 5008 s 0 n j 5008 UAMAS_reverse_2b5e6 j 5009 d 0 1000 n j 5009 UAPGR j 5010 s 0 n j 5010 UAPGR_reverse_4f67b j 5011 d 0 1000 n j 5011 UDCPDP j 5012 s 0 n j 5012 UDCPDP_reverse_1813b j 5013 d 0 1000 n j 5013 UDCPDPS j 5014 s 0 n j 5014 UDCPDPS_reverse_04082 j 5015 d 0 1000 n j 5015 UDCPDPpp j 5016 s 0 n j 5016 UDCPDPpp_reverse_50c3e j 5017 d 0 1000 n j 5017 UDCPPtppi j 5018 s 0 n j 5018 UDCPPtppi_reverse_70e48 j 5019 d 0 1000 n j 5019 UDPACGALtex j 5020 d 0 1000 n j 5020 UDPACGALtex_reverse_db14e j 5021 d 0 1000 n j 5021 UDPG4E j 5022 d 0 1000 n j 5022 UDPG4E_reverse_08c7f j 5023 d 0 1000 n j 5023 UDPGALM j 5024 s 0 n j 5024 UDPGALM_reverse_dc620 j 5025 d 0 1000 n j 5025 UDPGALPpp j 5026 s 0 n j 5026 UDPGALPpp_reverse_e2436 j 5027 d 0 1000 n j 5027 UDPGALtex j 5028 d 0 1000 n j 5028 UDPGALtex_reverse_19783 j 5029 d 0 1000 n j 5029 UDPGD j 5030 s 0 n j 5030 UDPGD_reverse_de167 j 5031 d 0 1000 n j 5031 UDPGDC j 5032 s 0 n j 5032 UDPGDC_reverse_f654b j 5033 d 0 1000 n j 5033 UDPGLCURtex j 5034 d 0 1000 n j 5034 UDPGLCURtex_reverse_3d157 j 5035 d 0 1000 n j 5035 UDPGPpp j 5036 s 0 n j 5036 UDPGPpp_reverse_caf0c j 5037 d 0 1000 n j 5037 UDPGtex j 5038 d 0 1000 n j 5038 UDPGtex_reverse_024fe j 5039 d 0 1000 n j 5039 UDPKAAT j 5040 d 0 1000 n j 5040 UDPKAAT_reverse_39cbd j 5041 d 0 1000 n j 5041 UGLCURPpp j 5042 s 0 n j 5042 UGLCURPpp_reverse_02763 j 5043 d 0 1000 n j 5043 UGLT j 5044 d 0 1000 n j 5044 UGLT_reverse_5e7f8 j 5045 d 0 1000 n j 5045 UGLYCH j 5046 s 0 n j 5046 UGLYCH_reverse_38b1a j 5047 d 0 1000 n j 5047 UGMDDS j 5048 s 0 n j 5048 UGMDDS_reverse_2401f j 5049 d 0 1000 n j 5049 UHGADA j 5050 s 0 n j 5050 UHGADA_reverse_608c0 j 5051 d 0 1000 n j 5051 ULA4NFT j 5052 s 0 n j 5052 ULA4NFT_reverse_07217 j 5053 d 0 1000 n j 5053 ULA4Ntppi j 5054 s 0 n j 5054 ULA4Ntppi_reverse_52ebe j 5055 d 0 1000 n j 5055 UM3PL j 5056 s 0 n j 5056 UM3PL_reverse_32754 j 5057 d 0 1000 n j 5057 UM4PCP j 5058 s 0 n j 5058 UM4PCP_reverse_86d06 j 5059 d 0 1000 n j 5059 UM4PL j 5060 s 0 n j 5060 UM4PL_reverse_c309d j 5061 d 0 1000 n j 5061 UMPK j 5062 d 0 1000 n j 5062 UMPK_reverse_ae8e3 j 5063 d 0 1000 n j 5063 UMPtex j 5064 d 0 1000 n j 5064 UMPtex_reverse_15b55 j 5065 d 0 1000 n j 5065 UPLA4FNF j 5066 s 0 n j 5066 UPLA4FNF_reverse_b8c75 j 5067 d 0 1000 n j 5067 UPLA4FNT j 5068 s 0 n j 5068 UPLA4FNT_reverse_a4d3d j 5069 d 0 1000 n j 5069 UPP3MT j 5070 s 0 n j 5070 UPP3MT_reverse_2adf0 j 5071 d 0 1000 n j 5071 UPP3S j 5072 s 0 n j 5072 UPP3S_reverse_8bb53 j 5073 d 0 1000 n j 5073 UPPDC1 j 5074 s 0 n j 5074 UPPDC1_reverse_cb592 j 5075 d 0 1000 n j 5075 UPPRT j 5076 s 0 n j 5076 UPPRT_reverse_07052 j 5077 d 0 1000 n j 5077 URACPAH j 5078 s 0 n j 5078 URACPAH_reverse_b5224 j 5079 d 0 1000 n j 5079 URAt2pp_copy1 j 5080 s 0 n j 5080 URAt2pp_copy1_reverse_c543f j 5081 d 0 1000 n j 5081 URAt2pp_copy2 j 5082 d 0 1000 n j 5082 URAt2pp_copy2_reverse_e156f j 5083 d 0 1000 n j 5083 URAtex j 5084 d 0 1000 n j 5084 URAtex_reverse_1b290 j 5085 d 0 1000 n j 5085 URDGLYCD j 5086 s 0 n j 5086 URDGLYCD_reverse_cb5de j 5087 d 0 1000 n j 5087 UREAtex j 5088 d 0 1000 n j 5088 UREAtex_reverse_e1c1e j 5089 d 0 1000 n j 5089 UREAtpp j 5090 d 0 1000 n j 5090 UREAtpp_reverse_c8094 j 5091 d 0 1000 n j 5091 URIC j 5092 s 0 n j 5092 URIC_reverse_bb103 j 5093 d 0 1000 n j 5093 URIH j 5094 s 0 n j 5094 URIH_reverse_64395 j 5095 d 0 1000 n j 5095 URIK2 j 5096 s 0 n j 5096 URIK2_reverse_f3df8 j 5097 d 0 1000 n j 5097 URIt2pp_copy1 j 5098 s 0 n j 5098 URIt2pp_copy1_reverse_7e838 j 5099 d 0 1000 n j 5099 URIt2pp_copy2 j 5100 d 0 1000 n j 5100 URIt2pp_copy2_reverse_6917b j 5101 d 0 1000 n j 5101 URItex j 5102 d 0 1000 n j 5102 URItex_reverse_fac5c j 5103 d 0 1000 n j 5103 USHD j 5104 s 0 n j 5104 USHD_reverse_f9e3a j 5105 d 0 1000 n j 5105 VALTA j 5106 d 0 1000 n j 5106 VALTA_reverse_1d084 j 5107 d 0 1000 n j 5107 VALTRS j 5108 s 0 n j 5108 VALTRS_reverse_72083 j 5109 d 0 1000 n j 5109 VALabcpp j 5110 s 0 n j 5110 VALabcpp_reverse_f400d j 5111 d 0 1000 n j 5111 VALt2rpp j 5112 d 0 1000 n j 5112 VALt2rpp_reverse_0dc61 j 5113 d 0 1000 n j 5113 VALtex j 5114 d 0 1000 n j 5114 VALtex_reverse_126bd j 5115 d 0 1000 n j 5115 VPAMTr j 5116 d 0 1000 n j 5116 VPAMTr_reverse_872bd j 5117 d 0 1000 n j 5117 WCOS j 5118 s 0 n j 5118 WCOS_reverse_1505f j 5119 d 0 1000 n j 5119 X5PL3E j 5120 s 0 n j 5120 X5PL3E_reverse_a59f0 j 5121 d 0 1000 n j 5121 XAND j 5122 s 0 n j 5122 XAND_reverse_04307 j 5123 d 0 1000 n j 5123 XANt2pp j 5124 s 0 n j 5124 XANt2pp_reverse_d1ca9 j 5125 d 0 1000 n j 5125 XANtex j 5126 d 0 1000 n j 5126 XANtex_reverse_35e9c j 5127 d 0 1000 n j 5127 XANtpp j 5128 d 0 1000 n j 5128 XANtpp_reverse_97eaa j 5129 d 0 1000 n j 5129 XMPtex j 5130 d 0 1000 n j 5130 XMPtex_reverse_59397 j 5131 d 0 1000 n j 5131 XPPT j 5132 s 0 n j 5132 XPPT_reverse_acb2c j 5133 d 0 1000 n j 5133 XTSNH j 5134 s 0 n j 5134 XTSNH_reverse_62c83 j 5135 d 0 1000 n j 5135 XTSNt2rpp j 5136 d 0 1000 n j 5136 XTSNt2rpp_reverse_e1a1b j 5137 d 0 1000 n j 5137 XTSNtex j 5138 d 0 1000 n j 5138 XTSNtex_reverse_a7e55 j 5139 d 0 1000 n j 5139 XYLI1 j 5140 d 0 1000 n j 5140 XYLI1_reverse_ba684 j 5141 d 0 1000 n j 5141 XYLI2 j 5142 d 0 1000 n j 5142 XYLI2_reverse_40bca j 5143 d 0 1000 n j 5143 XYLK j 5144 s 0 n j 5144 XYLK_reverse_f9b1e j 5145 d 0 1000 n j 5145 XYLK2 j 5146 s 0 n j 5146 XYLK2_reverse_ce1fa j 5147 d 0 1000 n j 5147 XYLUt2pp j 5148 s 0 n j 5148 XYLUt2pp_reverse_a8188 j 5149 d 0 1000 n j 5149 XYLUtex j 5150 d 0 1000 n j 5150 XYLUtex_reverse_653ed j 5151 d 0 1000 n j 5151 XYLabcpp j 5152 s 0 n j 5152 XYLabcpp_reverse_35686 j 5153 d 0 1000 n j 5153 XYLt2pp j 5154 s 0 n j 5154 XYLt2pp_reverse_441ce j 5155 d 0 1000 n j 5155 XYLtex j 5156 d 0 1000 n j 5156 XYLtex_reverse_758dc j 5157 d 0 1000 n j 5157 ZN2abcpp j 5158 s 0 n j 5158 ZN2abcpp_reverse_93cd5 j 5159 d 0 1000 n j 5159 ZN2t3pp j 5160 s 0 n j 5160 ZN2t3pp_reverse_c5ed9 j 5161 d 0 1000 n j 5161 ZN2tpp j 5162 s 0 n j 5162 ZN2tpp_reverse_5a7f0 j 5163 d 0 1000 n j 5163 ZNabcpp j 5164 s 0 n j 5164 ZNabcpp_reverse_14d34 j 5165 d 0 1000 n j 5165 Zn2tex j 5166 d 0 1000 n j 5166 Zn2tex_reverse_6b2c9 a 0 15 1 a 0 16 -1 a 1 5052 1 a 1 5051 -1 a 1 3694 -1 a 1 3693 1 a 1 2632 1 a 1 2631 -1 a 1 2484 -1 a 1 2483 1 a 1 2482 1 a 1 2481 -1 a 1 2438 1 a 1 2437 -1 a 1 1210 1 a 1 1209 -1 a 1 16 0.000223 a 1 15 -0.000223 a 1 14 0.000223 a 1 13 -0.000223 a 2 4056 -1 a 2 4055 1 a 2 1864 1 a 2 1863 -1 a 2 666 -1 a 2 665 1 a 3 4060 -1 a 3 4059 1 a 3 1866 1 a 3 1865 -1 a 3 668 -1 a 3 667 1 a 4 4064 -1 a 4 4063 1 a 4 1868 1 a 4 1867 -1 a 4 670 -1 a 4 669 1 a 5 4068 -1 a 5 4067 1 a 5 1870 1 a 5 1869 -1 a 5 672 -1 a 5 671 1 a 6 4072 -1 a 6 4071 1 a 6 1872 1 a 6 1871 -1 a 6 674 -1 a 6 673 1 a 7 4076 -1 a 7 4075 1 a 7 1874 1 a 7 1873 -1 a 7 676 -1 a 7 675 1 a 8 4080 -1 a 8 4079 1 a 8 1876 1 a 8 1875 -1 a 8 678 -1 a 8 677 1 a 9 3234 -1 a 9 3233 1 a 9 1290 -1 a 9 1289 1 a 9 682 -1 a 9 681 1 a 10 3236 -1 a 10 3235 1 a 10 686 -1 a 10 685 1 a 11 4156 -1 a 11 4155 1 a 11 2630 -1 a 11 2629 1 a 12 1986 1 a 12 1985 -1 a 12 1984 -1 a 12 1983 1 a 13 950 1 a 13 949 -1 a 13 688 -1 a 13 687 1 a 14 3398 -1 a 14 3397 1 a 14 1548 1 a 14 1547 -1 a 14 1344 1 a 14 1343 -1 a 15 2516 -1 a 15 2515 1 a 15 1346 -1 a 15 1345 1 a 15 1190 1 a 15 1189 -1 a 16 2524 -1 a 16 2523 1 a 16 1354 -1 a 16 1353 1 a 16 1198 1 a 16 1197 -1 a 17 2522 -1 a 17 2521 1 a 17 1352 -1 a 17 1351 1 a 17 1196 1 a 17 1195 -1 a 18 2528 -1 a 18 2527 1 a 18 1358 -1 a 18 1357 1 a 18 1202 1 a 18 1201 -1 a 19 2526 -1 a 19 2525 1 a 19 1356 -1 a 19 1355 1 a 19 1200 1 a 19 1199 -1 a 20 4418 -1 a 20 4417 1 a 20 4030 1 a 20 4029 -1 a 20 4028 1 a 20 4027 -1 a 20 2560 -1 a 20 2559 1 a 21 2520 -1 a 21 2519 1 a 21 1350 -1 a 21 1349 1 a 21 1194 1 a 21 1193 -1 a 22 2518 -1 a 22 2517 1 a 22 1348 -1 a 22 1347 1 a 22 1192 1 a 22 1191 -1 a 23 1884 1 a 23 1883 -1 a 23 700 -1 a 23 699 1 a 24 3102 -1 a 24 3101 1 a 24 1962 1 a 24 1961 -1 a 25 4026 -1 a 25 4025 1 a 25 1950 1 a 25 1949 -1 a 26 1966 -1 a 26 1965 1 a 26 1964 1 a 26 1963 -1 a 26 1962 -1 a 26 1961 1 a 27 2206 3 a 27 2205 -3 a 27 1964 -1 a 27 1963 1 a 28 2344 -1 a 28 2343 1 a 28 2210 -3 a 28 2209 3 a 28 2208 -3 a 28 2207 3 a 28 1966 1 a 28 1965 -1 a 29 1974 -1 a 29 1973 1 a 29 1972 1 a 29 1971 -1 a 30 3178 1 a 30 3177 -1 a 30 1952 1 a 30 1951 -1 a 31 3180 -1 a 31 3179 1 a 31 1954 1 a 31 1953 -1 a 32 2054 1 a 32 2053 -1 a 33 4410 -1 a 33 4409 1 a 33 1154 1 a 33 1153 -1 a 34 2028 1 a 34 2027 -1 a 34 2026 1 a 34 2025 -1 a 34 2024 1 a 34 2023 -1 a 35 2906 -1 a 35 2905 1 a 35 2002 1 a 35 2001 -1 a 36 4658 -1 a 36 4657 1 a 36 1888 1 a 36 1887 -1 a 37 4986 1 a 37 4985 -1 a 37 3216 -1 a 37 3215 1 a 37 1890 -1 a 37 1889 1 a 37 1888 -1 a 37 1887 1 a 37 1886 1 a 37 1885 -1 a 38 3368 1 a 38 3367 -1 a 38 3340 1 a 38 3339 -1 a 38 742 1 a 38 741 -1 a 38 728 -1 a 38 727 1 a 39 3370 1 a 39 3369 -1 a 39 3342 1 a 39 3341 -1 a 39 744 1 a 39 743 -1 a 39 730 -1 a 39 729 1 a 40 3372 1 a 40 3371 -1 a 40 3344 1 a 40 3343 -1 a 40 746 1 a 40 745 -1 a 40 732 -1 a 40 731 1 a 41 3374 1 a 41 3373 -1 a 41 3346 1 a 41 3345 -1 a 41 748 1 a 41 747 -1 a 41 734 -1 a 41 733 1 a 42 3376 1 a 42 3375 -1 a 42 3348 1 a 42 3347 -1 a 42 750 1 a 42 749 -1 a 42 736 -1 a 42 735 1 a 43 3378 1 a 43 3377 -1 a 43 3350 1 a 43 3349 -1 a 43 752 1 a 43 751 -1 a 43 738 -1 a 43 737 1 a 44 3380 1 a 44 3379 -1 a 44 3352 1 a 44 3351 -1 a 44 754 1 a 44 753 -1 a 44 740 -1 a 44 739 1 a 45 3382 1 a 45 3381 -1 a 45 3354 1 a 45 3353 -1 a 45 770 1 a 45 769 -1 a 45 756 -1 a 45 755 1 a 46 3384 1 a 46 3383 -1 a 46 3356 1 a 46 3355 -1 a 46 772 1 a 46 771 -1 a 46 758 -1 a 46 757 1 a 47 3386 1 a 47 3385 -1 a 47 3358 1 a 47 3357 -1 a 47 774 1 a 47 773 -1 a 47 760 -1 a 47 759 1 a 48 3388 1 a 48 3387 -1 a 48 3360 1 a 48 3359 -1 a 48 776 1 a 48 775 -1 a 48 762 -1 a 48 761 1 a 49 3390 1 a 49 3389 -1 a 49 3362 1 a 49 3361 -1 a 49 778 1 a 49 777 -1 a 49 764 -1 a 49 763 1 a 50 3392 1 a 50 3391 -1 a 50 3364 1 a 50 3363 -1 a 50 780 1 a 50 779 -1 a 50 766 -1 a 50 765 1 a 51 3394 1 a 51 3393 -1 a 51 3366 1 a 51 3365 -1 a 51 782 1 a 51 781 -1 a 51 768 -1 a 51 767 1 a 52 3180 1 a 52 3179 -1 a 52 1022 -1 a 52 1021 1 a 53 3396 -1 a 53 3395 1 a 53 2078 -1 a 53 2077 1 a 54 4858 -1 a 54 4857 1 a 54 2766 -1 a 54 2765 1 a 54 1492 -1 a 54 1491 1 a 54 1334 1 a 54 1333 -1 a 55 4406 -1 a 55 4405 1 a 55 3120 1 a 55 3119 -1 a 56 2014 1 a 56 2013 -1 a 56 1938 -1 a 56 1937 1 a 57 3326 1 a 57 3325 -1 a 57 714 -1 a 57 713 1 a 58 2190 -1 a 58 2189 1 a 58 2188 1 a 58 2187 -1 a 58 1936 -1 a 58 1935 1 a 59 3648 -1 a 59 3647 1 a 59 1936 1 a 59 1935 -1 a 59 1932 -1 a 59 1931 1 a 59 1292 -1 a 59 1291 1 a 60 2584 -1 a 60 2583 1 a 60 1930 1 a 60 1929 -1 a 61 1940 1 a 61 1939 -1 a 61 1930 -1 a 61 1929 1 a 62 2024 -1 a 62 2023 1 a 62 790 1 a 62 789 -1 a 62 788 1 a 62 787 -1 a 62 786 1 a 62 785 -1 a 62 784 1 a 62 783 -1 a 63 3672 -1 a 63 3671 1 a 63 2064 1 a 63 2063 -1 a 64 4894 -1 a 64 4893 1 a 64 4892 -1 a 64 4891 1 a 64 3736 1 a 64 3735 -1 a 64 3726 1 a 64 3725 -1 a 64 3722 1 a 64 3721 -1 a 64 3074 1 a 64 3073 -1 a 64 2810 1 a 64 2809 -1 a 64 2540 1 a 64 2539 -1 a 64 2458 -1 a 64 2457 1 a 64 2042 -1 a 64 2041 1 a 64 2040 -1 a 64 2039 1 a 65 4894 1 a 65 4893 -1 a 65 4892 1 a 65 4891 -1 a 65 3736 -1 a 65 3735 1 a 65 3726 -1 a 65 3725 1 a 65 3722 -1 a 65 3721 1 a 65 3074 -1 a 65 3073 1 a 65 2810 -1 a 65 2809 1 a 65 2540 -1 a 65 2539 1 a 65 2458 1 a 65 2457 -1 a 65 2042 1 a 65 2041 -1 a 65 2040 1 a 65 2039 -1 a 65 1982 -1 a 65 1981 1 a 65 1314 1 a 65 1313 -1 a 65 14 0.000223 a 65 13 -0.000223 a 66 4902 -1 a 66 4901 1 a 66 4506 -1 a 66 4505 1 a 66 4502 -1 a 66 4501 1 a 66 4498 -1 a 66 4497 1 a 66 4378 1 a 66 4377 -1 a 66 2096 -1 a 66 2095 1 a 67 4378 -1 a 67 4377 1 a 67 2066 1 a 67 2065 -1 a 68 4630 1 a 68 4629 -1 a 68 3084 1 a 68 3083 -1 a 68 1526 -1 a 68 1525 1 a 69 4636 -1 a 69 4635 1 a 69 3276 -1 a 69 3275 1 a 69 3090 -1 a 69 3089 1 a 69 1526 1 a 69 1525 -1 a 69 16 2.6e-05 a 69 15 -2.6e-05 a 69 14 2.5e-05 a 69 13 -2.5e-05 a 70 4946 1 a 70 4945 -1 a 70 3048 -1 a 70 3047 1 a 70 2760 -1 a 70 2759 1 a 70 2682 -1 a 70 2681 1 a 71 3334 1 a 71 3333 -1 a 71 722 -1 a 71 721 1 a 72 3332 1 a 72 3331 -1 a 72 720 -1 a 72 719 1 a 73 3166 1 a 73 3165 -1 a 73 3164 -1 a 73 3163 1 a 74 4910 1 a 74 4909 -1 a 74 4332 -1 a 74 4331 1 a 74 792 1 a 74 791 -1 a 75 3584 1 a 75 3583 -1 a 75 3482 -1 a 75 3481 1 a 76 3486 -1 a 76 3485 1 a 76 3482 1 a 76 3481 -1 a 77 3540 1 a 77 3539 -1 a 77 2104 -1 a 77 2103 1 a 78 3528 -1 a 78 3527 1 a 78 3526 1 a 78 3525 -1 a 79 4860 -1 a 79 4859 1 a 79 3956 1 a 79 3955 -1 a 79 1022 1 a 79 1021 -1 a 80 3338 1 a 80 3337 -1 a 80 726 -1 a 80 725 1 a 81 3336 1 a 81 3335 -1 a 81 724 -1 a 81 723 1 a 82 4000 -1 a 82 3999 1 a 82 3998 -1 a 82 3997 1 a 82 3978 1 a 82 3977 -1 a 82 16 0.000223 a 82 15 -0.000223 a 83 3992 -1 a 83 3991 1 a 83 3990 -1 a 83 3989 1 a 83 3980 1 a 83 3979 -1 a 84 3986 -1 a 84 3985 1 a 84 3984 -1 a 84 3983 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a 155 2790 -1 a 155 2789 1 a 156 4456 1 a 156 4455 -1 a 156 4152 -1 a 156 4151 1 a 157 4440 -1 a 157 4439 1 a 157 1624 1 a 157 1623 -1 a 158 1814 1 a 158 1813 -1 a 159 2734 -1 a 159 2733 1 a 159 2730 1 a 159 2729 -1 a 159 2656 -1 a 159 2655 1 a 159 960 -1 a 159 959 1 a 159 958 -1 a 159 957 1 a 159 956 1 a 159 955 -1 a 160 4488 -1 a 160 4487 1 a 160 958 1 a 160 957 -1 a 161 2004 1 a 161 2003 -1 a 161 1108 -1 a 161 1107 1 a 162 1110 -1 a 162 1109 1 a 162 1108 1 a 162 1107 -1 a 163 3032 1 a 163 3031 -1 a 164 4332 1 a 164 4331 -1 a 164 3032 -1 a 164 3031 1 a 164 1316 -1 a 164 1315 1 a 164 792 -1 a 164 791 1 a 165 3540 -1 a 165 3539 1 a 165 1600 1 a 165 1599 -1 a 166 4966 -1 a 166 4965 1 a 166 2 1 a 166 1 -1 a 167 4640 -1 a 167 4639 1 a 167 3276 1 a 167 3275 -1 a 167 3094 -1 a 167 3093 1 a 167 2426 -1 a 167 2425 1 a 167 2424 2 a 167 2423 -2 a 167 2422 2 a 167 2421 -2 a 167 16 0.00026 a 167 15 -0.00026 a 167 14 0.000248 a 167 13 -0.000248 a 168 3994 -1 a 168 3993 1 a 168 3038 1 a 168 3037 -1 a 169 2968 1 a 169 2967 -1 a 169 1626 -1 a 169 1625 1 a 170 904 -1 a 170 903 1 a 171 2998 1 a 171 2997 -1 a 172 3982 -1 a 172 3981 1 a 172 3248 1 a 172 3247 -1 a 173 4910 1 a 173 4909 -1 a 173 4882 -1 a 173 4881 1 a 173 2998 -1 a 173 2997 1 a 174 1460 -1 a 174 1459 1 a 174 1428 -1 a 174 1427 1 a 175 4122 1 a 175 4121 -1 a 175 2110 -1 a 175 2109 1 a 176 4380 1 a 176 4379 -1 a 176 4334 -1 a 176 4333 1 a 177 4380 -1 a 177 4379 1 a 177 4364 1 a 177 4363 -1 a 178 4568 -1 a 178 4567 1 a 178 4566 1 a 178 4565 -1 a 178 4328 -1 a 178 4327 1 a 179 2092 1 a 179 2091 -1 a 180 4410 1 a 180 4409 -1 a 180 1214 1 a 180 1213 -1 a 181 4360 2 a 181 4359 -2 a 181 2510 -1 a 181 2509 1 a 182 4328 1 a 182 4327 -1 a 182 1376 -1 a 182 1375 1 a 183 2002 -1 a 183 2001 1 a 183 1376 1 a 183 1375 -1 a 184 1214 -1 a 184 1213 1 a 184 1212 -1 a 184 1211 1 a 185 3110 1 a 185 3109 -1 a 185 3108 1 a 185 3107 -1 a 185 2028 -1 a 185 2027 1 a 185 2026 -1 a 185 2025 1 a 185 916 -1 a 185 915 1 a 185 914 1 a 185 913 -1 a 186 2684 -1 a 186 2683 1 a 186 2682 1 a 186 2681 -1 a 186 2580 -1 a 186 2579 1 a 187 920 -1 a 187 919 1 a 187 4 1 a 187 3 -1 a 188 4840 -1 a 188 4839 1 a 188 2446 1 a 188 2445 -1 a 189 4746 -1 a 189 4745 1 a 189 3692 1 a 189 3691 -1 a 189 1344 -1 a 189 1343 1 a 190 3698 -1 a 190 3697 1 a 190 3552 1 a 190 3551 -1 a 190 14 0.000223 a 190 13 -0.000223 a 191 3692 -1 a 191 3691 1 a 191 924 1 a 191 923 -1 a 192 4628 1 a 192 4627 -1 a 192 3802 -1 a 192 3801 1 a 193 3040 1 a 193 3039 -1 a 193 1988 -1 a 193 1987 1 a 194 3040 -1 a 194 3039 1 a 194 2004 1 a 194 2003 -1 a 195 4158 -1 a 195 4157 1 a 195 2848 -1 a 195 2847 1 a 195 2846 1 a 195 2845 -1 a 195 2190 1 a 195 2189 -1 a 196 4158 1 a 196 4157 -1 a 196 2566 -1 a 196 2565 1 a 197 1336 -1 a 197 1335 1 a 197 1312 1 a 197 1311 -1 a 198 4998 -1 a 198 4997 1 a 198 4984 -1 a 198 4983 1 a 198 4006 -1 a 198 4005 1 a 198 3488 1 a 198 3487 -1 a 198 3274 -1 a 198 3273 1 a 198 3182 -1 a 198 3181 1 a 198 2528 -1 a 198 2527 1 a 198 2526 -1 a 198 2525 1 a 198 2524 -1 a 198 2523 1 a 198 2522 -1 a 198 2521 1 a 198 2520 -1 a 198 2519 1 a 198 2518 -1 a 198 2517 1 a 198 2516 -1 a 198 2515 1 a 198 2248 -1 a 198 2247 1 a 198 2246 -1 a 198 2245 1 a 198 2244 -1 a 198 2243 1 a 198 2242 -1 a 198 2241 1 a 198 2240 -1 a 198 2239 1 a 198 2238 -1 a 198 2237 1 a 198 2236 -1 a 198 2235 1 a 198 2198 -1 a 198 2197 1 a 198 2196 -1 a 198 2195 1 a 198 2194 -1 a 198 2193 1 a 198 2192 -1 a 198 2191 1 a 198 1336 -1 a 198 1335 1 a 198 1202 -1 a 198 1201 1 a 198 1200 -1 a 198 1199 1 a 198 1198 -1 a 198 1197 1 a 198 1196 -1 a 198 1195 1 a 198 1194 -1 a 198 1193 1 a 198 1192 -1 a 198 1191 1 a 198 1190 -1 a 198 1189 1 a 198 1092 -1 a 198 1091 1 a 198 1090 -1 a 198 1089 1 a 198 1088 -1 a 198 1087 1 a 198 1086 -1 a 198 1085 1 a 198 1084 -1 a 198 1083 1 a 198 1082 -1 a 198 1081 1 a 198 1080 -1 a 198 1079 1 a 198 1078 -1 a 198 1077 1 a 198 1062 1 a 198 1061 -1 a 198 944 1 a 198 943 -1 a 198 942 1 a 198 941 -1 a 198 940 1 a 198 939 -1 a 198 938 1 a 198 937 -1 a 198 936 1 a 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2950 1 a 207 2949 -1 a 207 966 -1 a 207 965 1 a 207 964 -1 a 207 963 1 a 208 1374 1 a 208 1373 -1 a 208 1334 -1 a 208 1333 1 a 208 946 1 a 208 945 -1 a 209 5050 -1 a 209 5049 1 a 209 4344 -1 a 209 4343 1 a 209 3708 -1 a 209 3707 1 a 209 3066 -1 a 209 3065 1 a 209 1812 -1 a 209 1811 1 a 209 1634 -1 a 209 1633 1 a 209 1528 -1 a 209 1527 1 a 209 1260 -1 a 209 1259 1 a 209 1258 -1 a 209 1257 1 a 209 1158 -1 a 209 1157 1 a 209 1102 -1 a 209 1101 1 a 209 1100 -1 a 209 1099 1 a 209 1094 1 a 209 1093 -1 a 209 1064 -1 a 209 1063 1 a 209 1024 1 a 209 1023 -1 a 209 964 -1 a 209 963 1 a 210 3418 -1 a 210 3417 1 a 210 3182 1 a 210 3181 -1 a 210 1062 -1 a 210 1061 1 a 211 982 -1 a 211 981 1 a 211 964 1 a 211 963 -1 a 212 4856 -1 a 212 4855 1 a 212 4854 -1 a 212 4853 1 a 212 3038 -1 a 212 3037 1 a 212 2324 -1 a 212 2323 1 a 212 2214 -1 a 212 2213 1 a 212 2066 -1 a 212 2065 1 a 212 1720 -1 a 212 1719 1 a 212 1260 1 a 212 1259 -1 a 212 1258 1 a 212 1257 -1 a 212 1254 -1 a 212 1253 1 a 212 990 -1 a 212 989 1 a 212 986 1 a 212 985 -1 a 213 992 -1 a 213 991 1 a 214 4818 1 a 214 4817 -1 a 214 4738 1 a 214 4737 -1 a 214 4736 1 a 214 4735 -1 a 214 4682 1 a 214 4681 -1 a 214 4480 1 a 214 4479 -1 a 214 4342 -1 a 214 4341 1 a 214 4134 -1 a 214 4133 1 a 214 4094 -1 a 214 4093 1 a 214 4026 -1 a 214 4025 1 a 214 3938 1 a 214 3937 -1 a 214 3430 1 a 214 3429 -1 a 214 3428 1 a 214 3427 -1 a 214 3184 1 a 214 3183 -1 a 214 3168 1 a 214 3167 -1 a 214 3066 1 a 214 3065 -1 a 214 2766 1 a 214 2765 -1 a 214 2670 1 a 214 2669 -1 a 214 2502 1 a 214 2501 -1 a 214 1756 1 a 214 1755 -1 a 214 1528 1 a 214 1527 -1 a 214 1094 -1 a 214 1093 1 a 214 1062 1 a 214 1061 -1 a 214 1020 1 a 214 1019 -1 a 214 996 1 a 214 995 -1 a 214 992 1 a 214 991 -1 a 214 986 -1 a 214 985 1 a 214 980 -1 a 214 979 1 a 214 978 1 a 214 977 -1 a 214 976 1 a 214 975 -1 a 214 974 1 a 214 973 -1 a 214 972 1 a 214 971 -1 a 214 970 1 a 214 969 -1 a 214 968 1 a 214 967 -1 a 214 966 2 a 214 965 -2 a 214 964 1 a 214 963 -1 a 214 892 -1 a 214 891 1 a 214 14 0.000279 a 214 13 -0.000279 a 215 1290 1 a 215 1289 -1 a 215 1288 -1 a 215 1287 1 a 216 1204 -1 a 216 1203 1 a 216 1018 -1 a 216 1017 1 a 217 3708 1 a 217 3707 -1 a 217 1204 1 a 217 1203 -1 a 217 1076 -1 a 217 1075 1 a 218 1914 -1 a 218 1913 1 a 218 1180 -1 a 218 1179 1 a 218 1176 -1 a 218 1175 1 a 218 1164 -1 a 218 1163 1 a 218 1010 1 a 218 1009 -1 a 219 5002 1 a 219 5001 -1 a 219 2502 -1 a 219 2501 1 a 220 1914 -1 a 220 1913 1 a 220 1308 -1 a 220 1307 1 a 220 1158 1 a 220 1157 -1 a 220 1028 -1 a 220 1027 1 a 220 1014 -1 a 220 1013 1 a 220 1010 -1 a 220 1009 1 a 221 2670 -1 a 221 2669 1 a 222 1020 -1 a 222 1019 1 a 222 1018 1 a 222 1017 -1 a 223 3430 -1 a 223 3429 1 a 224 1310 1 a 224 1309 -1 a 224 1044 -1 a 224 1043 1 a 225 1310 -1 a 225 1309 1 a 225 1308 1 a 225 1307 -1 a 225 1032 -1 a 225 1031 1 a 226 1328 -1 a 226 1327 1 a 226 1036 -1 a 226 1035 1 a 226 1028 1 a 226 1027 -1 a 227 1044 1 a 227 1043 -1 a 227 1040 -1 a 227 1039 1 a 228 1074 1 a 228 1073 -1 a 228 1072 -1 a 228 1071 1 a 228 1068 -1 a 228 1067 1 a 229 1070 1 a 229 1069 -1 a 229 1068 1 a 229 1067 -1 a 230 1070 -1 a 230 1069 1 a 231 1076 1 a 231 1075 -1 a 231 1064 1 a 231 1063 -1 a 232 4682 -1 a 232 4681 1 a 232 1812 1 a 232 1811 -1 a 232 1098 1 a 232 1097 -1 a 233 3184 -1 a 233 3183 1 a 233 3182 -1 a 233 3181 1 a 233 864 1 a 233 863 -1 a 234 4480 -1 a 234 4479 1 a 234 1024 -1 a 234 1023 1 a 235 4914 -1 a 235 4913 1 a 235 4498 -1 a 235 4497 1 a 235 4496 -1 a 235 4495 1 a 235 3692 -1 a 235 3691 1 a 235 1318 -1 a 235 1317 1 a 235 1208 -1 a 235 1207 1 a 235 1148 1 a 235 1147 -1 a 235 1130 -1 a 235 1129 1 a 235 1114 -1 a 235 1113 1 a 235 1112 1 a 235 1111 -1 a 235 920 -1 a 235 919 1 a 236 4496 1 a 236 4495 -1 a 236 3870 -1 a 236 3869 1 a 236 1134 -1 a 236 1133 1 a 236 1132 -1 a 236 1131 1 a 236 1130 1 a 236 1129 -1 a 236 1128 1 a 236 1127 -1 a 236 1106 1 a 236 1105 -1 a 237 1554 -1 a 237 1553 1 a 237 1138 1 a 237 1137 -1 a 238 1138 -1 a 238 1137 1 a 238 994 1 a 238 993 -1 a 239 1564 -1 a 239 1563 1 a 239 1142 -1 a 239 1141 1 a 239 1140 -1 a 239 1139 1 a 239 14 0.000223 a 239 13 -0.000223 a 240 5164 -1 a 240 5163 1 a 240 5158 -1 a 240 5157 1 a 240 5152 -1 a 240 5151 1 a 240 5146 -1 a 240 5145 1 a 240 5144 -1 a 240 5143 1 a 240 5110 -1 a 240 5109 1 a 240 5062 -1 a 240 5061 1 a 240 5060 -1 a 240 5059 1 a 240 5056 -1 a 240 5055 1 a 240 5048 -1 a 240 5047 1 a 240 5008 -1 a 240 5007 1 a 240 5006 -1 a 240 5005 1 a 240 4986 -1 a 240 4985 1 a 240 4982 1 a 240 4981 -1 a 240 4960 -1 a 240 4959 1 a 240 4948 -1 a 240 4947 1 a 240 4908 -1 a 240 4907 1 a 240 4906 -1 a 240 4905 1 a 240 4900 -1 a 240 4899 1 a 240 4868 -1 a 240 4867 1 a 240 4850 -1 a 240 4849 1 a 240 4828 -1 a 240 4827 1 a 240 4810 -1 a 240 4809 1 a 240 4790 -1 a 240 4789 1 a 240 4784 -1 a 240 4783 1 a 240 4776 -1 a 240 4775 1 a 240 4740 -1 a 240 4739 1 a 240 4712 -1 a 240 4711 1 a 240 4634 -1 a 240 4633 1 a 240 4632 -1 a 240 4631 1 a 240 4630 -1 a 240 4629 1 a 240 4602 1 a 240 4601 -1 a 240 4600 1 a 240 4599 -1 a 240 4592 -1 a 240 4591 1 a 240 4586 -1 a 240 4585 1 a 240 4572 -1 a 240 4571 1 a 240 4570 -1 a 240 4569 1 a 240 4564 -1 a 240 4563 1 a 240 4556 -1 a 240 4555 1 a 240 4554 -1 a 240 4553 1 a 240 4532 1 a 240 4531 -1 a 240 4520 -1 a 240 4519 1 a 240 4518 -1 a 240 4517 1 a 240 4512 -1 a 240 4511 1 a 240 4490 -1 a 240 4489 1 a 240 4426 -1 a 240 4425 1 a 240 4420 -1 a 240 4419 1 a 240 4414 -1 a 240 4413 1 a 240 4410 -1 a 240 4409 1 a 240 4404 -1 a 240 4403 1 a 240 4402 -1 a 240 4401 1 a 240 4374 -1 a 240 4373 1 a 240 4372 -1 a 240 4371 1 a 240 4366 -1 a 240 4365 1 a 240 4350 1 a 240 4349 -1 a 240 4334 -1 a 240 4333 1 a 240 4332 -1 a 240 4331 1 a 240 4240 -1 a 240 4239 1 a 240 4222 -1 a 240 4221 1 a 240 4178 -1 a 240 4177 1 a 240 4176 -1 a 240 4175 1 a 240 4174 -1 a 240 4173 1 a 240 4172 -1 a 240 4171 1 a 240 4170 -1 a 240 4169 1 a 240 4168 -1 a 240 4167 1 a 240 4166 -1 a 240 4165 1 a 240 4156 -1 a 240 4155 1 a 240 4148 -1 a 240 4147 1 a 240 4146 -1 a 240 4145 1 a 240 4144 -1 a 240 4143 1 a 240 4142 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399 4607 1 a 399 3766 1 a 399 3765 -1 a 399 1848 -1 a 399 1847 1 a 400 3904 -1 a 400 3903 1 a 400 3854 1 a 400 3853 -1 a 400 1848 1 a 400 1847 -1 a 401 4620 -1 a 401 4619 1 a 401 3904 1 a 401 3903 -1 a 401 3766 -1 a 401 3765 1 a 401 1920 1 a 401 1919 -1 a 401 16 0.027017 a 401 15 -0.027017 a 401 14 0.025612 a 401 13 -0.025612 a 402 3854 -1 a 402 3853 1 a 402 1924 -1 a 402 1923 1 a 402 1922 1 a 402 1921 -1 a 403 2180 -1 a 403 2179 1 a 403 1054 -1 a 403 1053 1 a 404 3382 -1 a 404 3381 1 a 404 3368 -1 a 404 3367 1 a 404 3326 -1 a 404 3325 1 a 404 2252 -1 a 404 2251 1 a 404 2238 -1 a 404 2237 1 a 404 1360 -1 a 404 1359 1 a 404 940 1 a 404 939 -1 a 404 770 1 a 404 769 -1 a 404 742 1 a 404 741 -1 a 405 2516 1 a 405 2515 -1 a 405 2238 1 a 405 2237 -1 a 405 2192 1 a 405 2191 -1 a 405 2118 -1 a 405 2117 1 a 405 2116 -1 a 405 2115 1 a 405 1190 1 a 405 1189 -1 a 405 1078 1 a 405 1077 -1 a 405 940 -1 a 405 939 1 a 405 876 1 a 405 875 -1 a 406 2272 -1 a 406 2271 1 a 406 2252 1 a 406 2251 -1 a 406 1054 1 a 406 1053 -1 a 406 976 1 a 406 975 -1 a 407 1360 1 a 407 1359 -1 a 407 1346 1 a 407 1345 -1 a 407 1078 -1 a 407 1077 1 a 408 4606 -1 a 408 4605 1 a 408 4604 -1 a 408 4603 1 a 408 3762 1 a 408 3761 -1 a 408 1942 -1 a 408 1941 1 a 409 3896 -1 a 409 3895 1 a 409 3874 1 a 409 3873 -1 a 409 1942 1 a 409 1941 -1 a 410 4666 1 a 410 4665 -1 a 410 4664 -1 a 410 4663 1 a 411 4502 1 a 411 4501 -1 a 411 3918 -1 a 411 3917 1 a 411 3874 -1 a 411 3873 1 a 411 1946 -1 a 411 1945 1 a 412 4618 -1 a 412 4617 1 a 412 3918 1 a 412 3917 -1 a 412 3896 1 a 412 3895 -1 a 412 3762 -1 a 412 3761 1 a 412 16 0.027017 a 412 15 -0.027017 a 412 14 0.025612 a 412 13 -0.025612 a 413 2788 -1 a 413 2787 1 a 413 2228 -1 a 413 2227 1 a 413 1960 -1 a 413 1959 1 a 413 1956 1 a 413 1955 -1 a 414 4912 1 a 414 4911 -1 a 414 4834 -1 a 414 4833 1 a 414 4598 -1 a 414 4597 1 a 414 4552 1 a 414 4551 -1 a 414 3580 1 a 414 3579 -1 a 414 2540 -1 a 414 2539 1 a 414 2538 -1 a 414 2537 1 a 414 2536 -1 a 414 2535 1 a 414 2534 -1 a 414 2533 1 a 414 2312 -1 a 414 2311 1 a 414 2304 -1 a 414 2303 1 a 414 2302 -1 a 414 2301 1 a 414 1956 -1 a 414 1955 1 a 415 1970 1 a 415 1969 -1 a 415 1968 -1 a 415 1967 1 a 415 828 -1 a 415 827 1 a 416 4904 -1 a 416 4903 1 a 416 1978 -1 a 416 1977 1 a 416 1976 1 a 416 1975 -1 a 417 4966 -1 a 417 4965 1 a 417 4882 1 a 417 4881 -1 a 418 1990 -1 a 418 1989 1 a 418 1980 1 a 418 1979 -1 a 419 2012 -1 a 419 2011 1 a 420 1986 -1 a 420 1985 1 a 420 1982 1 a 420 1981 -1 a 421 2050 -1 a 421 2049 1 a 421 1990 1 a 421 1989 -1 a 421 1988 1 a 421 1987 -1 a 422 1998 1 a 422 1997 -1 a 422 1996 1 a 422 1995 -1 a 422 1994 1 a 422 1993 -1 a 422 1992 1 a 422 1991 -1 a 423 2052 -1 a 423 2051 1 a 423 2050 1 a 423 2049 -1 a 424 3042 1 a 424 3041 -1 a 424 2000 -1 a 424 1999 1 a 424 830 -1 a 424 829 1 a 425 2004 -1 a 425 2003 1 a 425 1978 1 a 425 1977 -1 a 426 4584 -1 a 426 4583 1 a 426 2006 1 a 426 2005 -1 a 427 2010 1 a 427 2009 -1 a 427 2008 1 a 427 2007 -1 a 428 3886 -1 a 428 3885 1 a 429 3898 -1 a 429 3897 1 a 429 3844 1 a 429 3843 -1 a 430 4506 1 a 430 4505 -1 a 430 3844 -1 a 430 3843 1 a 430 2020 -1 a 430 2019 1 a 430 1856 -1 a 430 1855 1 a 431 3898 1 a 431 3897 -1 a 431 3886 1 a 431 3885 -1 a 431 1908 -1 a 431 1907 1 a 432 3802 1 a 432 3801 -1 a 433 4568 2 a 433 4567 -2 a 433 4566 -1 a 433 4565 1 a 434 3158 -1 a 434 3157 1 a 434 2032 -1 a 434 2031 1 a 434 2030 1 a 434 2029 -1 a 435 2040 -1 a 435 2039 1 a 435 2036 -1 a 435 2035 1 a 436 2046 -1 a 436 2045 1 a 436 2040 1 a 436 2039 -1 a 436 2036 1 a 436 2035 -1 a 437 3800 -1 a 437 3799 1 a 437 3740 1 a 437 3739 -1 a 438 4914 1 a 438 4913 -1 a 438 4484 -1 a 438 4483 1 a 438 2062 1 a 438 2061 -1 a 439 4832 -1 a 439 4831 1 a 439 4830 -1 a 439 4829 1 a 439 2074 1 a 439 2073 -1 a 440 4832 1 a 440 4831 -1 a 440 4830 1 a 440 4829 -1 a 440 2074 -1 a 440 2073 1 a 441 5070 -1 a 441 5069 1 a 441 4706 1 a 441 4705 -1 a 442 1912 -1 a 442 1911 1 a 442 1526 1 a 442 1525 -1 a 443 4582 -1 a 443 4581 1 a 443 3760 1 a 443 3759 -1 a 443 2086 -1 a 443 2085 1 a 443 948 -1 a 443 947 1 a 444 4818 -1 a 444 4817 1 a 444 948 1 a 444 947 -1 a 445 4820 -1 a 445 4819 1 a 445 4818 1 a 445 4817 -1 a 446 4826 -1 a 446 4825 1 a 446 4822 1 a 446 4821 -1 a 446 4820 1 a 446 4819 -1 a 447 4824 1 a 447 4823 -1 a 447 4822 -1 a 447 4821 1 a 448 4826 1 a 448 4825 -1 a 448 2506 -1 a 448 2505 1 a 449 4824 -1 a 449 4823 1 a 449 4582 1 a 449 4581 -1 a 450 4904 -1 a 450 4903 1 a 450 4900 -1 a 450 4899 1 a 450 3912 -1 a 450 3911 1 a 450 3862 1 a 450 3861 -1 a 450 2086 1 a 450 2085 -1 a 451 3912 1 a 451 3911 -1 a 451 3760 -1 a 451 3759 1 a 451 2506 1 a 451 2505 -1 a 451 16 0.026166 a 451 15 -0.026166 a 451 14 0.024805 a 451 13 -0.024805 a 452 4614 -1 a 452 4613 1 a 452 4612 -1 a 452 4611 1 a 452 3764 1 a 452 3763 -1 a 453 4904 1 a 453 4903 -1 a 453 3834 1 a 453 3833 -1 a 453 2102 -1 a 453 2101 1 a 453 2094 -1 a 453 2093 1 a 454 3834 -1 a 454 3833 1 a 454 2098 -1 a 454 2097 1 a 454 2096 1 a 454 2095 -1 a 454 2094 1 a 454 2093 -1 a 454 1922 -1 a 454 1921 1 a 455 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-1 a 468 1054 1 a 468 1053 -1 a 468 1052 1 a 468 1051 -1 a 468 1050 1 a 468 1049 -1 a 468 1048 1 a 468 1047 -1 a 468 1046 1 a 468 1045 -1 a 468 16 0.000223 a 468 15 -0.000223 a 468 14 0.000223 a 468 13 -0.000223 a 469 4638 1 a 469 4637 -1 a 469 4634 1 a 469 4633 -1 a 469 4632 1 a 469 4631 -1 a 469 4418 -1 a 469 4417 1 a 469 3092 1 a 469 3091 -1 a 469 3088 1 a 469 3087 -1 a 469 3086 1 a 469 3085 -1 a 469 2404 1 a 469 2403 -1 a 469 2388 1 a 469 2387 -1 a 469 2372 1 a 469 2371 -1 a 469 2366 1 a 469 2365 -1 a 469 2350 1 a 469 2349 -1 a 469 2292 -1 a 469 2291 1 a 469 2290 -1 a 469 2289 1 a 469 1854 -1 a 469 1853 1 a 469 1704 1 a 469 1703 -1 a 469 1380 1 a 469 1379 -1 a 469 1060 -1 a 469 1059 1 a 469 1058 -1 a 469 1057 1 a 469 1056 -1 a 469 1055 1 a 469 1054 -1 a 469 1053 1 a 469 1052 -1 a 469 1051 1 a 469 1050 -1 a 469 1049 1 a 469 1048 -1 a 469 1047 1 a 469 1046 -1 a 469 1045 1 a 470 4648 -1 a 470 4647 1 a 470 3702 -1 a 470 3701 1 a 470 2322 -1 a 470 2321 1 a 470 2300 1 a 470 2299 -1 a 470 2298 -1 a 470 2297 1 a 471 2312 1 a 471 2311 -1 a 471 2310 -1 a 471 2309 1 a 472 2310 1 a 472 2309 -1 a 472 2308 -1 a 472 2307 1 a 473 4128 -1 a 473 4127 1 a 473 2460 -1 a 473 2459 1 a 473 2306 1 a 473 2305 -1 a 473 2302 1 a 473 2301 -1 a 474 4708 1 a 474 4707 -1 a 474 4634 2 a 474 4633 -2 a 474 4632 2 a 474 4631 -2 a 474 3276 -2 a 474 3275 2 a 474 3088 2 a 474 3087 -2 a 474 3086 2 a 474 3085 -2 a 474 2426 1 a 474 2425 -1 a 474 2408 -2 a 474 2407 2 a 474 2406 -2 a 474 2405 2 a 474 2404 -2 a 474 2403 2 a 474 2392 -2 a 474 2391 2 a 474 2390 -2 a 474 2389 2 a 474 2388 -2 a 474 2387 2 a 474 2376 -2 a 474 2375 2 a 474 2374 -2 a 474 2373 2 a 474 2372 -2 a 474 2371 2 a 474 2366 -2 a 474 2365 2 a 474 2354 -2 a 474 2353 2 a 474 2352 -2 a 474 2351 2 a 474 2350 -2 a 474 2349 2 a 474 2338 -1 a 474 2337 1 a 474 2334 1 a 474 2333 -1 a 474 2332 -1 a 474 2331 1 a 474 2330 -1 a 474 2329 1 a 474 2314 1 a 474 2313 -1 a 474 1708 -2 a 474 1707 2 a 474 1706 -2 a 474 1705 2 a 474 1704 -2 a 474 1703 2 a 474 1384 -2 a 474 1383 2 a 474 1382 -2 a 474 1381 2 a 474 1380 -2 a 474 1379 2 a 474 16 0.006715 a 474 15 -0.006715 a 474 14 0.006388 a 474 13 -0.006388 a 475 2424 -2 a 475 2423 2 a 475 2422 -2 a 475 2421 2 a 475 2368 -1 a 475 2367 1 a 475 2366 2 a 475 2365 -2 a 475 2344 -1 a 475 2343 1 a 475 2340 -1 a 475 2339 1 a 475 2210 -1 a 475 2209 1 a 475 16 0.007808 a 475 15 -0.007808 a 475 14 0.007428 a 475 13 -0.007428 a 476 2346 -1 a 476 2345 1 a 476 2344 1 a 476 2343 -1 a 477 2360 -1 a 477 2359 1 a 477 2354 2 a 477 2353 -2 a 477 2352 2 a 477 2351 -2 a 477 2350 2 a 477 2349 -2 a 478 2356 1 a 478 2355 -1 a 478 2354 -2 a 478 2353 2 a 478 2352 -2 a 478 2351 2 a 478 2350 -2 a 478 2349 2 a 479 2382 -1 a 479 2381 1 a 479 2376 2 a 479 2375 -2 a 479 2374 2 a 479 2373 -2 a 479 2372 2 a 479 2371 -2 a 480 2380 1 a 480 2379 -1 a 480 2376 -2 a 480 2375 2 a 480 2374 -2 a 480 2373 2 a 480 2372 -2 a 480 2371 2 a 481 2394 -1 a 481 2393 1 a 481 2392 2 a 481 2391 -2 a 481 2390 2 a 481 2389 -2 a 481 2388 2 a 481 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2325 1 a 488 2324 -1 a 488 2323 1 a 488 2322 -1 a 488 2321 1 a 488 2320 -1 a 488 2319 1 a 488 1706 -1 a 488 1705 1 a 488 1382 -1 a 488 1381 1 a 489 2444 -1 a 489 2443 1 a 489 2442 -1 a 489 2441 1 a 489 2406 1 a 489 2405 -1 a 489 2390 1 a 489 2389 -1 a 489 2374 1 a 489 2373 -1 a 489 2352 1 a 489 2351 -1 a 489 2328 1 a 489 2327 -1 a 489 2326 1 a 489 2325 -1 a 489 2324 1 a 489 2323 -1 a 489 2322 1 a 489 2321 -1 a 489 2320 1 a 489 2319 -1 a 489 1706 1 a 489 1705 -1 a 489 1382 1 a 489 1381 -1 a 490 5066 -1 a 490 5065 1 a 490 4698 -1 a 490 4697 1 a 490 4134 -1 a 490 4133 1 a 490 3956 -1 a 490 3955 1 a 490 2906 -1 a 490 2905 1 a 490 2904 -1 a 490 2903 1 a 490 2634 1 a 490 2633 -1 a 490 2484 1 a 490 2483 -1 a 490 2482 -1 a 490 2481 1 a 490 2454 1 a 490 2453 -1 a 490 2450 -1 a 490 2449 1 a 490 2448 -1 a 490 2447 1 a 490 2430 1 a 490 2429 -1 a 490 1910 -1 a 490 1909 1 a 490 1316 -2 a 490 1315 2 a 491 4022 -1 a 491 4021 1 a 491 2448 1 a 491 2447 -1 a 492 4414 -1 a 492 4413 1 a 492 4404 1 a 492 4403 -1 a 493 3132 -1 a 493 3131 1 a 493 1210 -1 a 493 1209 1 a 494 5014 1 a 494 5013 -1 a 494 3966 1 a 494 3965 -1 a 494 2984 1 a 494 2983 -1 a 494 2874 -1 a 494 2873 1 a 495 5142 -1 a 495 5141 1 a 495 3004 1 a 495 3003 -1 a 495 2428 -1 a 495 2427 1 a 495 2230 -1 a 495 2229 1 a 496 2468 -1 a 496 2467 1 a 496 2466 1 a 496 2465 -1 a 496 2464 -1 a 496 2463 1 a 497 2466 -1 a 497 2465 1 a 497 2462 1 a 497 2461 -1 a 498 3470 -1 a 498 3469 1 a 498 2928 -1 a 498 2927 1 a 498 2472 -1 a 498 2471 1 a 499 2488 -1 a 499 2487 1 a 499 2308 1 a 499 2307 -1 a 500 4772 -1 a 500 4771 1 a 500 4770 -1 a 500 4769 1 a 500 4388 3 a 500 4387 -3 a 500 3028 -1 a 500 3027 1 a 500 2494 -1 a 500 2493 1 a 500 2492 -1 a 500 2491 1 a 500 2490 1 a 500 2489 -1 a 500 2458 1 a 500 2457 -1 a 500 2456 1 a 500 2455 -1 a 500 1996 1 a 500 1995 -1 a 500 1470 -1 a 500 1469 1 a 500 1466 1 a 500 1465 -1 a 500 1416 -1 a 500 1415 1 a 500 1154 -1 a 500 1153 1 a 500 1152 -1 a 500 1151 1 a 501 5044 -1 a 501 5043 1 a 501 4164 1 a 501 4163 -1 a 501 3628 -1 a 501 3627 1 a 501 3626 -1 a 501 3625 1 a 501 3624 -1 a 501 3623 1 a 501 2716 1 a 501 2715 -1 a 501 2680 -1 a 501 2679 1 a 501 2678 -1 a 501 2677 1 a 501 2614 1 a 501 2613 -1 a 501 2506 1 a 501 2505 -1 a 502 4938 -1 a 502 4937 1 a 502 4934 -1 a 502 4933 1 a 502 4912 -1 a 502 4911 1 a 502 4886 -1 a 502 4885 1 a 502 4884 -1 a 502 4883 1 a 502 4834 -1 a 502 4833 1 a 502 4796 1 a 502 4795 -1 a 502 2630 1 a 502 2629 -1 a 502 2302 -1 a 502 2301 1 a 502 2228 -1 a 502 2227 1 a 502 2188 -1 a 502 2187 1 a 502 2106 1 a 502 2105 -1 a 502 2066 -1 a 502 2065 1 a 502 1940 -1 a 502 1939 1 a 503 2854 1 a 503 2853 -1 a 503 2530 -1 a 503 2529 1 a 504 3380 -1 a 504 3379 1 a 504 3378 -1 a 504 3377 1 a 504 3376 -1 a 504 3375 1 a 504 3374 -1 a 504 3373 1 a 504 3372 -1 a 504 3371 1 a 504 3370 -1 a 504 3369 1 a 504 3368 -1 a 504 3367 1 a 504 3352 -1 a 504 3351 1 a 504 3350 -1 a 504 3349 1 a 504 3348 -1 a 504 3347 1 a 504 3346 -1 a 504 3345 1 a 504 3344 -1 a 504 3343 1 a 504 3342 -1 a 504 3341 1 a 504 3340 -1 a 504 3339 1 a 504 2858 1 a 504 2857 -1 a 504 2542 -1 a 504 2541 1 a 505 3394 -1 a 505 3393 1 a 505 3392 -1 a 505 3391 1 a 505 3390 -1 a 505 3389 1 a 505 3388 -1 a 505 3387 1 a 505 3386 -1 a 505 3385 1 a 505 3384 -1 a 505 3383 1 a 505 3382 -1 a 505 3381 1 a 505 3366 -1 a 505 3365 1 a 505 3364 -1 a 505 3363 1 a 505 3362 -1 a 505 3361 1 a 505 3360 -1 a 505 3359 1 a 505 3358 -1 a 505 3357 1 a 505 3356 -1 a 505 3355 1 a 505 3354 -1 a 505 3353 1 a 505 2866 1 a 505 2865 -1 a 505 2546 -1 a 505 2545 1 a 506 2870 1 a 506 2869 -1 a 506 2550 -1 a 506 2549 1 a 507 2862 1 a 507 2861 -1 a 507 2554 -1 a 507 2553 1 a 508 4922 1 a 508 4921 -1 a 508 4918 -1 a 508 4917 1 a 508 4164 -1 a 508 4163 1 a 508 4154 1 a 508 4153 -1 a 508 3000 -1 a 508 2999 1 a 508 2706 -1 a 508 2705 1 a 508 2570 -1 a 508 2569 1 a 508 2568 1 a 508 2567 -1 a 508 2566 1 a 508 2565 -1 a 508 2462 -1 a 508 2461 1 a 508 2428 -1 a 508 2427 1 a 508 954 -1 a 508 953 1 a 509 2694 1 a 509 2693 -1 a 509 2604 -1 a 509 2603 1 a 510 3212 -1 a 510 3211 1 a 510 2618 -1 a 510 2617 1 a 510 2616 -1 a 510 2615 1 a 510 2602 -1 a 510 2601 1 a 510 2598 1 a 510 2597 -1 a 511 5044 1 a 511 5043 -1 a 511 2598 -1 a 511 2597 1 a 512 2594 -1 a 512 2593 1 a 512 2580 1 a 512 2579 -1 a 513 2590 -1 a 513 2589 1 a 513 2584 1 a 513 2583 -1 a 514 2586 -1 a 514 2585 1 a 514 2582 1 a 514 2581 -1 a 515 2726 1 a 515 2725 -1 a 515 2606 -1 a 515 2605 1 a 516 2930 1 a 516 2929 -1 a 516 2610 -1 a 516 2609 1 a 517 4150 1 a 517 4149 -1 a 517 2502 1 a 517 2501 -1 a 518 4150 -1 a 518 4149 1 a 518 2652 -1 a 518 2651 1 a 518 2626 -1 a 518 2625 1 a 518 2622 -1 a 518 2621 1 a 518 2564 1 a 518 2563 -1 a 518 1158 -1 a 518 1157 1 a 519 4402 -1 a 519 4401 1 a 519 2634 1 a 519 2633 -1 a 519 2632 1 a 519 2631 -1 a 520 3944 -1 a 520 3943 1 a 520 3942 1 a 520 3941 -1 a 521 1750 1 a 521 1749 -1 a 521 1728 -1 a 521 1727 1 a 522 2638 1 a 522 2637 -1 a 522 2320 -1 a 522 2319 1 a 522 1988 -1 a 522 1987 1 a 523 5096 -1 a 523 5095 1 a 523 4646 -1 a 523 4645 1 a 523 4606 1 a 523 4605 -1 a 523 4604 1 a 523 4603 -1 a 523 4370 -1 a 523 4369 1 a 523 3890 -1 a 523 3889 1 a 523 3754 1 a 523 3753 -1 a 523 3462 1 a 523 3461 -1 a 523 2852 -2 a 523 2851 2 a 523 2666 -1 a 523 2665 1 a 523 2644 -1 a 523 2643 1 a 523 2642 1 a 523 2641 -1 a 523 1838 -1 a 523 1837 1 a 523 1156 -1 a 523 1155 1 a 523 1120 -1 a 523 1119 1 a 524 2832 -1 a 524 2831 1 a 524 2640 1 a 524 2639 -1 a 525 2850 -1 a 525 2849 1 a 526 3462 -1 a 526 3461 1 a 526 2832 1 a 526 2831 -1 a 526 2646 1 a 526 2645 -1 a 526 2644 1 a 526 2643 -1 a 527 2850 1 a 527 2849 -1 a 527 2640 -1 a 527 2639 1 a 528 2910 -1 a 528 2909 1 a 528 2908 1 a 528 2907 -1 a 528 2648 1 a 528 2647 -1 a 529 3946 1 a 529 3945 -1 a 529 3942 -1 a 529 3941 1 a 530 2656 1 a 530 2655 -1 a 530 2654 -1 a 530 2653 1 a 531 2696 1 a 531 2695 -1 a 531 2694 -1 a 531 2693 1 a 532 2658 -1 a 532 2657 1 a 532 2654 1 a 532 2653 -1 a 533 2996 1 a 533 2995 -1 a 533 2696 -1 a 533 2695 1 a 534 2660 -1 a 534 2659 1 a 534 2658 1 a 534 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18 s 0 n j 18 PFK_reverse_d24a6 j 19 d 0 1000 n j 19 PGI j 20 d 0 1000 n j 20 PGI_reverse_27efc j 21 d 0 1000 n j 21 PGK j 22 d 0 1000 n j 22 PGK_reverse_02696 j 23 d 0 1000 n j 23 PGM j 24 d 0 1000 n j 24 PGM_reverse_fc9af j 25 d 0 1000 n j 25 PIt2r j 26 d 0 1000 n j 26 PIt2r_reverse_1cd61 j 27 d 0 1000 n j 27 PYK j 28 s 0 n j 28 PYK_reverse_bc8ff j 29 d 0 1000 n j 29 TPI j 30 d 0 1000 n j 30 TPI_reverse_c2c3b j 31 d 0 1000 n j 31 LDH_D j 32 d 0 1000 n j 32 LDH_D_reverse_f8507 j 33 d 0 1000 n j 33 EX_lac__D_e j 34 s 0 n j 34 EX_lac__D_e_reverse_f95b4 j 35 d 0 1000 n j 35 D_LACt2 j 36 d 0 1000 n j 36 D_LACt2_reverse_d0dc8 a 0 1 1 a 0 2 -1 a 0 17 1 a 0 18 -1 a 1 28 -1 a 1 27 1 a 1 18 1 a 1 17 -1 a 1 1 -1 a 1 2 1 a 1 21 -1 a 1 22 1 a 2 16 -1 a 2 15 1 a 2 2 1 a 2 1 -1 a 2 3 1 a 2 4 -1 a 3 28 1 a 3 27 -1 a 3 18 -1 a 3 17 1 a 3 1 1 a 3 2 -1 a 3 21 1 a 3 22 -1 a 4 32 -1 a 4 31 1 a 4 26 -1 a 4 25 1 a 4 12 -1 a 4 11 1 a 4 1 1 a 4 2 -1 a 4 17 1 a 4 18 -1 a 4 27 -1 a 4 28 1 a 5 26 -1 a 5 25 1 a 5 2 -1 a 5 1 1 a 5 11 -1 a 5 12 1 a 6 24 1 a 6 23 -1 a 6 3 -1 a 6 4 1 a 7 28 1 a 7 27 -1 a 7 4 -1 a 7 3 1 a 7 13 -1 a 7 14 1 a 8 14 1 a 8 13 -1 a 8 5 -1 a 8 6 1 a 9 26 1 a 9 25 -1 a 9 7 -1 a 9 8 1 a 10 18 -1 a 10 17 1 a 10 9 -1 a 10 10 1 a 11 30 1 a 11 29 -1 a 11 9 1 a 11 10 -1 a 12 30 -1 a 12 29 1 a 12 10 -1 a 12 9 1 a 12 11 -1 a 12 12 1 a 13 32 1 a 13 31 -1 a 13 11 -1 a 13 12 1 a 14 22 -1 a 14 21 1 a 14 11 1 a 14 12 -1 a 15 32 -1 a 15 31 1 a 15 11 1 a 15 12 -1 a 16 20 1 a 16 19 -1 a 16 13 1 a 16 14 -1 a 17 32 -1 a 17 31 1 a 17 14 -1 a 17 13 1 a 17 27 1 a 17 28 -1 a 18 16 1 a 18 15 -1 a 19 20 -1 a 19 19 1 a 19 17 -1 a 19 18 1 a 20 24 -1 a 20 23 1 a 20 21 -1 a 20 22 1 a 21 26 1 a 21 25 -1 a 22 32 1 a 22 31 -1 a 23 34 1 a 23 33 -1 e o f rX glpk_statusrNXconfigrcoptlang.glpk_interface Configuration r)r}r(XpresolverXautorX verbosityrKXtimeoutrNX tolerancesr}rX feasibilityrG>z򚼯HsububX _tolerancerG>z򚼯Hub.cobrapy-0.29.1/tests/data/mini_cobra.xml000066400000000000000000003104041467301562300201520ustar00rootroot00000000000000 cobrapy-0.29.1/tests/data/mini_fbc1.xml000066400000000000000000000712721467301562300177060ustar00rootroot00000000000000

FORMULA: C3H4O10P2

FORMULA: C3H4O7P

FORMULA: C3H4O7P

FORMULA: C10H12N5O10P2

FORMULA: C10H12N5O13P3

FORMULA: C3H5O6P

FORMULA: C6H11O9P

FORMULA: C6H10O12P2

FORMULA: C3H5O6P

FORMULA: C6H11O9P

FORMULA: C6H12O6

FORMULA: H2O

FORMULA: H2O

FORMULA: H

FORMULA: H

FORMULA: C3H5O3

FORMULA: C3H5O3

FORMULA: C21H26N7O14P2

FORMULA: C21H27N7O14P2

FORMULA: C3H2O6P

FORMULA: HO4P

FORMULA: HO4P

FORMULA: C3H3O3

FORMULA: C6H12O6

FORMULA: H

FORMULA: C3H5O3

GENE ASSOCIATION: b3603 or b2975

GENE ASSOCIATION: b2779

GENE ASSOCIATION: b1773 or b2097 or b2925

GENE ASSOCIATION: b1779

GENE ASSOCIATION: ( b2417 and b1621 and b2415 and b2416 ) or ( b2417 and b1101 and b2415 and b2416 ) or ( b1817 and b1818 and b1819 and b2415 and b2416 )

GENE ASSOCIATION: b0875 or s0001

GENE ASSOCIATION: b2133 or b1380

GENE ASSOCIATION: b3916 or b1723

GENE ASSOCIATION: b4025

GENE ASSOCIATION: b2926

GENE ASSOCIATION: b4395 or b3612 or b0755

GENE ASSOCIATION: b2987 or b3493

GENE ASSOCIATION: b1854 or b1676

GENE ASSOCIATION: b3919

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REACT_29370, REACT_196180, REACT_113581, REACT_113582, REACT_114564, REACT_114565, REACT_429153mmREACT_190078, REACT_113592, REACT_113593, REACT_114570, REACT_29358, REACT_389573, REACT_139836, REACT_211579X''REACT_188451, REACT_75970, REACT_390404000@  REACT_16297560REACT_947593, REACT_189422, REACT_141343, REACT_113518, REACT_1605715, REACT_109276, REACT_113521, REACT_113519, REACT_2022884, REACT_351603, REACT_29356REACT_947593, REACT_189422, REACT_141343, REACT_113518, REACT_1605715, REACT_109276, REACT_113521, REACT_113519, REACT_2022884, REACT_351603, REACT_29356@  REACT_194688, REACT_425978, REACT_193465, REACT_374900, REACT_74722, REACT_425999, REACT_428040, REACT_163953, REACT_372511, REACT_2000349, REACT_70106, REACT_1470067, REACT_113529, REACT_425969, REACT_428548, REACT_156540, REACT_1614597, REACT_351626, REACT_427899@  REACT_194688, REACT_425978, REACT_193465, REACT_374900, REACT_74722, REACT_425999, REACT_428040, REACT_163953, REACT_372511, REACT_2000349, REACT_70106, 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16372000000080000000000PMETA:DLACTDEHYDROGNAD-RXN000000080ATPM00ENO0glc0h8lac__D0FBA0GAPD8GLCpts0H2Ot8LDH_D0PFK0PGI0PGK0PGM8PIt2r0PYK0TPIcobrapy-0.29.1/tests/data/raven.mat000066400000000000000000000047271467301562300171540ustar00rootroot00000000000000MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Thu Dec 22 17:19:49 2016 IMO xr0Ƿ!ICUny`9)CC< 'n-fcٝrx^7DGMҁ3y} uĬbS[|ᾘ{~bD 8VvOP^&/Z~.j?jŷB J9mCWENqx4e'ER|ڠet戰%Ei,bX@ QK4M~ߧ)r3,fX\8Blc0!R(LИ׽!ﰓ,O˾~~Asg-Ew|V vx/[ߠ+V}:?Wq߿&FW9mbNRe$9Wq!Iֳ-IΖ\>m{"9r91 w!ooY(w"?Ugȫ4yn qp}IsaX?3(ߝE"KfwE{wC   Om7wM0Sc\;x]n=K)|D#|fB2iqyeo\)J) eQ+Wbg^K0;u+(> Z٠sN|t ٌ,6%o6hj}Q8?݁9' [skYXKΔ,hK.~sDAɼvɼÒyY޿JW1F|:3ϞF ECGƽ4aEqIFc>p‚:+g9 ڴ?}|qeӿ"yz?ȗ?c6K>2>h8t8IR3㉻){g{J#q<>q4 VGTVbUg|"΢"˶᳭|1S*T1?a!ŹcQCG_LQh <>Nbu{;?W~n4|dXd4GHEc HȆ8|a+A~UD&ůnɸw^`$nK <=exo=eܹ+s_~U-ϟEg|nԧy?XN@v~+FZN-vd7Ҧ|(Q7 ɱ Sx G݄%$@+9ɘf<; I*W5W=\W E^x/W}!|~Fdrzfo'^xGI|>&^;N/)JG9F{~'^W'O 5=moIW˹!p/5o-ʹW4WoT3}ތS@[A' DQ^yv[4_^ fsR=|@g߲<ćd`_/./oo.Շ$?Gx^ X걔#^EFW=ZW}XΏ0Utp/뮱~<ܾt*J'?egVv:Uo\ce``bF f h2@UgENJ07 x~Q}JgG;{CgD/ ;F :}}LhPؠ| KMe`(K)MW;R?r2W??)_/9#5)jˍf/iehnal6~䇟h~&FFG~h~ͿhKoSGa7o(=1cobrapy-0.29.1/tests/data/raven.pickle000066400000000000000000000244071467301562300176370ustar00rootroot00000000000000ccobra.core.model Model q)q}q(X_idqXHHqXnameqNXnotesq}qX _annotationq}q Xgenesq ccobra.core.dictlist DictList q )Rq (ccobra.core.gene Gene q )q}q(hXYBL030CqhXqh}qh}qX_modelqNX _reactionqc__builtin__ set q]qqRqX _functionalqubh )q}q(hXYBR085Wqhhh}qh}qhNhh]q q!Rq"hubh )q#}q$(hXYMR056Cq%hhh}q&h}q'hNhh]q(q)Rq*hube}q+X_dictq,}q-(hKhKh%KusbX reactionsq.h )Rq/(ccobra.core.reaction Reaction q0)q1}q2(hX2MBACtq3hX'2-methylbutyl transport (extracellular)q4h}q5h}q6X_gprq7ccobra.core.gene GPR q8)Rq9}q:(X_genesq;h]qXbodyq?NubX subsystemq@XTransport, ExtracellularqAh;h]qBqCRqDX _metabolitesqE}qF(ccobra.core.metabolite Metabolite qG)qH}qI(hXm208qJhX2-methylbutyl acetateqKh}qLh}qMhNhh]qNqORqPXformulaqQNX compartmentqRX2qSXchargeqTNX_boundqUGubcnumpy.core.multiarray scalar qVcnumpy dtype 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m1404 n i 18 m1405 n i 19 m1406 n i 20 m1407 n i 21 m1408 n i 22 m1409 n i 23 m1410 n i 24 m1411 n i 25 m1412 j 1 d 0 1000 n j 1 2MBACt j 2 s 0 n j 2 2MBACt_reverse_99a6d j 3 d 0 1000 n j 3 2MBALDt j 4 d 0 1000 n j 4 2MBALDt_reverse_e0735 j 5 d 0 1000 n j 5 2MBTOHt j 6 d 0 1000 n j 6 2MBTOHt_reverse_0c1c8 j 7 d 0 1000 n j 7 2MPPALt j 8 d 0 1000 n j 8 2MPPALt_reverse_91d12 j 9 d 0 1000 n j 9 2PHETOHt j 10 d 0 1000 n j 10 2PHETOHt_reverse_5a477 j 11 d 0 1000 n j 11 3C3HMPt j 12 d 0 1000 n j 12 3C3HMPt_reverse_b492c j 13 d 0 1000 n j 13 3MBALDt j 14 d 0 1000 n j 14 3MBALDt_reverse_83979 j 15 d 0 1000 n j 15 3MOPt j 16 d 0 1000 n j 16 3MOPt_reverse_6a118 j 17 d 0 1000 n j 17 4ABZt j 18 d 0 1000 n j 18 4ABZt_reverse_518c9 j 19 d 0 1000 n j 19 AAC1 j 20 s 0 n j 20 AAC1_reverse_a6f01 a 1 16 -1 a 1 15 1 a 2 6 1 a 2 5 -1 a 3 2 1 a 3 1 -1 a 4 4 1 a 4 3 -1 a 5 8 1 a 5 7 -1 a 6 10 -1 a 6 9 1 a 7 12 1 a 7 11 -1 a 8 14 1 a 8 13 -1 a 9 18 1 a 9 17 -1 a 10 20 1 a 10 19 -1 a 11 20 -1 a 11 19 1 a 12 20 1 a 12 19 -1 a 13 20 -1 a 13 19 1 a 14 20 1 a 14 19 -1 a 15 20 -1 a 15 19 1 a 16 20 -1 a 16 19 1 a 17 2 -1 a 17 1 1 a 18 4 -1 a 18 3 1 a 19 6 -1 a 19 5 1 a 20 8 -1 a 20 7 1 a 21 10 1 a 21 9 -1 a 22 12 -1 a 22 11 1 a 23 14 -1 a 23 13 1 a 24 16 1 a 24 15 -1 a 25 18 -1 a 25 17 1 e o f rX glpk_statusrNXconfigrcoptlang.glpk_interface Configuration r)r}r(XpresolverXautorX verbosityrKXtimeoutrNX tolerancesr}rX feasibilityrG>z򚼯HsububX _tolerancerG>z򚼯Hub.cobrapy-0.29.1/tests/data/salmonella.media000066400000000000000000000110531467301562300204540ustar00rootroot00000000000000ccollections defaultdict p1 (c__builtin__ dict p2 tRp3 S'LB' p4 (dp5 S'EX_ser__L_e' p6 F-5 sS'EX_dcyt_e' p7 F-5 sS'EX_hg2_e' p8 F-1000 sS'EX_ins_e' p9 F-5 sS'EX_cd2_e' p10 F-1000 sS'EX_nac_e' p11 F-5 sS'EX_tungs_e' p12 F-1000 sS'EX_glu__L_e' p13 F-5 sS'EX_trp__L_e' p14 F-5 sS'EX_h_e' p15 F-100 sS'EX_mobd_e' p16 F-1000 sS'EX_val__L_e' p17 F-5 sS'EX_cobalt2_e' p18 F-1000 sS'EX_so4_e' p19 F-1000 sS'EX_co2_e' p20 F-1000 sS'EX_k_e' p21 F-1000 sS'EX_cu2_e' p22 F-1000 sS'EX_zn2_e' p23 F-1000 sS'EX_na1_e' p24 F-1000 sS'EX_cl_e' p25 F-1000 sS'EX_leu__L_e' p26 F-5 sS'EX_arg__L_e' p27 F-5 sS'EX_pnto__R_e' p28 F-5 sS'EX_lys__L_e' p29 F-5 sS'EX_ala__L_e' p30 F-5 sS'EX_cbl1_e' p31 F-0.01 sS'EX_thr__L_e' p32 F-5 sS'EX_fe3_e' p33 F-1000 sS'EX_adn_e' p34 F-5 sS'EX_pi_e' p35 F-1000 sS'EX_thymd_e' p36 F-5 sS'EX_mn2_e' p37 F-1000 sS'EX_phe__L_e' p38 F-5 sS'EX_ura_e' p39 F-5 sS'EX_dad__2_e' p40 F-5 sS'EX_h2o_e' p41 F-100 sS'EX_aso3_e' p42 F-1000 sS'EX_hxan_e' p43 F-5 sS'EX_glc__D_e' p44 F-5 sS'EX_uri_e' p45 F-5 sS'EX_his__L_e' p46 F-5 sS'EX_o2_e' p47 F-18.5 sS'EX_pro__L_e' p48 F-5 sS'EX_asp__L_e' p49 F-5 sS'EX_gly_e' p50 F-5 sS'EX_fe2_e' p51 F-1000 sS'EX_ca2_e' p52 F-1000 sS'EX_mg2_e' p53 F-1000 sS'EX_cysi__L_e' p54 F-5 sS'EX_tyr__L_e' p55 F-5 sS'EX_met__L_e' p56 F-5 sS'EX_ile__L_e' p57 F-5 ssS'Host' p58 (dp59 g7 F-5 sS'EX_melib_e' p60 F-5 sg9 F-5 sg18 F-10 sS'EX_dmso_e' p61 F-1000 sS'EX_acnam_e' p62 F-5 sS'EX_thm_e' p63 F-5 sS'EX_glcn_e' p64 F-5 sg45 F-5 sS'EX_rib__D_e' p65 F-5 sg12 F-10 sS'EX_malt_e' p66 F-5 sg13 F-5 sS'EX_arab__L_e' p67 F-5 sS'EX_12ppd__S_e' p68 F-5 sg15 F-100 sg16 F-10 sS'EX_mnl_e' p69 F-5 sg53 F-10 sg19 F-1000 sg20 F-1000 sS'EX_glcr_e' p70 F-5 sg21 F-10 sg22 F-10 sg24 F-1000 sg31 F-0.01 sg25 F-1000 sS'EX_sbt__D_e' p71 F-5 sg27 F-5 sg28 F-5 sg29 F-5 sS'EX_fuc__L_e' p72 F-5 sg30 F-5 sS'EX_csn_e' p73 F-5 sS'EX_gal_e' p74 F-5 sS'EX_crn_e' p75 F-5 sS'EX_no3_e' p76 F-1000 sg32 F-5 sg33 F-10 sg34 F-5 sg35 F-1000 sS'EX_glcur_e' p77 F-5 sg36 F-5 sg37 F-10 sS'EX_etha_e' p78 F-5 sS'EX_galt_e' p79 F-5 sg39 F-5 sg40 F-5 sS'EX_gsn_e' p80 F-5 sg41 F-100 sg43 F-5 sg44 F-5 sS'EX_galct__D_e' p81 F-5 sg46 F-5 sg56 F-5 sg47 F-18.5 sS'EX_asn__L_e' p82 F-5 sg48 F-5 sS'EX_acgam_e' p83 F-5 sS'EX_so3_e' p84 F-1000 sS'EX_cys__L_e' p85 F-5 sS'EX_rmn_e' p86 F-5 sg52 F-10 sg17 F-5 sS'EX_man_e' p87 F-5 sg23 F-10 sg26 F-5 sS'EX_fru_e' p88 F-5 sg51 F-10 sS'EX_alltn_e' p89 F-5 sS'EX_galctn__D_e' p90 F-5 sg57 F-5 ssS'M9' p91 (dp92 g18 F-1000 sg12 F-1000 sg15 F-100 sg16 F-1000 sg53 F-1000 sg19 F-1000 sg20 F-1000 sg21 F-1000 sg33 F-1000 sg24 F-1000 sg25 F-1000 sg22 F-1000 sS'EX_nh4_e' p93 F-1000 sg51 F-1000 sg31 F-0.01 sg35 F-1000 sg37 F-1000 sg41 F-100 sg44 F-5 sg47 F-18.5 sg52 F-1000 sg23 F-1000 ssS'LPM' p94 (dp95 S'EX_ca2_e' p96 F-0.0050000000000000001 sS'EX_cit_e' p97 F-0.00050000000000000001 sS'EX_h_e' p98 I-100 sS'EX_mobd_e' p99 F-0.0050000000000000001 sS'EX_o2_e' p100 F-18.5 sS'EX_cobalt2_e' p101 F-0.0050000000000000001 sS'EX_mg2_e' p102 F-0.0080000000000000002 sS'EX_thm_e' p103 F-2.9649835443413292e-08 sS'EX_so4_e' p104 F-1 sS'EX_glyc_e' p105 F-41.046802041481158 sS'EX_co2_e' p106 F-18.5 sS'EX_pi_e' p107 F-0.33700000000000002 sS'EX_k_e' p108 F-6 sS'EX_cu2_e' p109 F-0.0050000000000000001 sS'EX_cl_e' p110 F-5.016 sS'EX_zn2_e' p111 F-0.0050000000000000001 sS'EX_h2o_e' p112 I-1000 sS'EX_nh4_e' p113 F-15 sS'EX_mn2_e' p114 F-0.0050000000000000001 sS'EX_fe3_e' p115 F-0.0050000000000000001 ssS'MgM' p116 (dp117 S'EX_ser__L_e' p118 F-0.3115 sS'EX_cobalt2_e' p119 F-0.01 sS'EX_pro__L_e' p120 F-0.61850000000000005 sS'EX_glu__L_e' p121 F-0.80200000000000005 sS'EX_glyc_e' p122 F-38 sS'EX_h_e' p123 I-100 sS'EX_mobd_e' p124 F-0.01 sS'EX_val__L_e' p125 F-0.29999999999999999 sS'EX_so4_e' p126 F-8 sS'EX_co2_e' p127 F-18.5 sS'EX_k_e' p128 F-7.0026855387574392 sS'EX_fe3_e' p129 F-0.01 sS'EX_na1_e' p130 F-0.55198392330511803 sS'EX_cl_e' p131 F-5.6458532974921516 sS'EX_leu__L_e' p132 F-0.4385 sS'EX_arg__L_e' p133 F-0.1135 sS'EX_nh4_e' p134 F-0.35449999999999998 sS'EX_lys__L_e' p135 F-0.32450000000000001 sS'EX_ala__L_e' p136 F-0.22 sS'EX_thr__L_e' p137 F-0.20849999999999999 sS'EX_pi_e' p138 F-1 sS'EX_mn2_e' p139 F-0.01 sS'EX_phe__L_e' p140 F-0.045499999999999999 sS'EX_h2o_e' p141 I-1000 sS'EX_his__L_e' p142 F-0.097000000000000003 sS'EX_o2_e' p143 F-18.5 sS'EX_tyr__L_e' p144 F-0.0035000000000000001 sS'EX_asp__L_e' p145 F-0.32850000000000001 sS'EX_gly_e' p146 F-0.1555 sS'EX_cys__L_e' p147 F-0.014500000000000001 sS'EX_cu2_e' p148 F-0.01 sS'EX_ca2_e' p149 F-0.0020709616248315785 sS'EX_mg2_e' p150 F-0.0080983336761983136 sS'EX_zn2_e' p151 F-0.01 sS'EX_met__L_e' p152 F-0.1125 sS'EX_ile__L_e' p153 F-0.19600000000000001 ss.cobrapy-0.29.1/tests/data/salmonella.pickle000066400000000000000000130444721467301562300206630ustar00rootroot00000000000000ccobra.core.model Model q)q}q(X_idqXSalmonella_consensus_build_1qXnameqNXnotesq}qX _annotationq}q Xgenesq ccobra.core.dictlist DictList q )Rq (ccobra.core.gene Gene q )q}q(hXSTM2267qhXompCqh}qh}qX_modelqNX _reactionqc__builtin__ set q]qqRqX _functionalqubh )q}q(hXSTM1473qhXompNqh}qh}q hNhh]q!q"Rq#hubh )q$}q%(hXSTM0320q&hXphoEq'h}q(h}q)hNhh]q*q+Rq,hubh )q-}q.(hXSTM0999q/hXompFq0h}q1h}q2hNhh]q3q4Rq5hubh )q6}q7(hXSTM2037q8hXpduFq9h}q:h}q;hNhh]qhubh )q?}q@(hXSTM4227qAhXmalGqBh}qCh}qDhNhh]qEqFRqGhubh )qH}qI(hXSTM4228qJhXmalFqKh}qLh}qMhNhh]qNqORqPhubh )qQ}qR(hXSTM4229qShXmalEqTh}qUh}qVhNhh]qWqXRqYhubh 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ö?†ã8Žã80@ ÇqÇq.@”ôIŸôI1@[°[°#@¥Oú¤O(@Ú8Žã8Ž-@†ã8Žã80@r:hr;Rr<tr=ba]r>hh}r?(hhhKr@hrARrB(KKHrCh ]rD(hhhhhhhhhhhhhhhhhhhhhhjjjjjjjjjj j j j j jjjjjjjjjjjjjjjjjjj j!j"j#j$j%j&j'j(j)j*j+j,j-j.j/j0j1etrEbhNurFRrGa}rHh}rI(hhh]rJ}rK(hj8hhKKHKrLRrMuaustrNbhhhhh}rOh}rPhshhubub.cobrapy-0.29.1/tests/data/update_pickles.py000077500000000000000000000104221467301562300206740ustar00rootroot00000000000000#!/usr/bin/env python """ Regenerate model pickle files. This should be performed after updating core classes in order to prevent suble bugs. """ from collections import OrderedDict from json import dump as json_dump from pickle import dump, load import importlib_resources import cobra from cobra.io import ( load_matlab_model, load_model, save_json_model, save_matlab_model, save_yaml_model, write_sbml_model, ) config = cobra.Configuration() config.solver = "glpk" if __name__ == "__main__": # ecoli ecoli_model = load_model("iJO1366", cache=False) with open("iJO1366.pickle", "wb") as outfile: dump(ecoli_model, outfile, protocol=2) # salmonella salmonella = load_model("salmonella", cache=False) with open("salmonella.media", "rb") as infile: salmonella.media_compositions = load(infile) with open("salmonella.pickle", "wb") as outfile: dump(salmonella, outfile, protocol=2) # create mini model from textbook textbook = load_model("textbook", cache=False) mini = cobra.Model("mini_textbook") mini.compartments = textbook.compartments for r in textbook.reactions: if r.id in ( "GLCpts", "PGI", "PFK", "FBA", "TPI", "GAPD", "PGK", "PGM", "ENO", "PYK", "EX_glc__D_e", "EX_h_e", "H2Ot", "ATPM", "PIt2r", ): mini.add_reaction(r.copy()) mini.reactions.ATPM.upper_bound = mini.reactions.PGI.upper_bound mini.objective = ["PFK", "ATPM"] # No biomass, 2 reactions # add in some information from iJO1366 mini.add_reaction(ecoli_model.reactions.LDH_D.copy()) mini.add_reaction(ecoli_model.reactions.EX_lac__D_e.copy()) r = cobra.Reaction("D_LACt2") mini.add_reaction(r) mini.reactions.GLCpts.gene_reaction_rule = ( ecoli_model.reactions.GLCptspp.gene_reaction_rule ) # adjust bounds for i in ["ATPM", "D_LACt2", "EX_lac__D_e", "LDH_D"]: mini.reactions.get_by_id(i).upper_bound = mini.reactions.PGI.upper_bound for i in ["D_LACt2", "LDH_D"]: mini.reactions.get_by_id(i).lower_bound = mini.reactions.PGI.lower_bound # set names and annotation for g in mini.genes: try: tg = textbook.genes.get_by_id(g.id) except KeyError: continue g.name = tg.name g.annotation = tg.annotation mini.reactions.sort() mini.genes.sort() mini.metabolites.sort() # output to various formats with open("mini.pickle", "wb") as outfile: dump(mini, outfile, protocol=2) save_matlab_model(mini, importlib_resources.files(cobra.data).joinpath("mini.mat")) save_json_model( mini, importlib_resources.files(cobra.data).joinpath("mini.json"), pretty=True ) save_yaml_model(mini, importlib_resources.files(cobra.data).joinpath("mini.yml")) write_sbml_model(mini, "mini_fbc2.xml") write_sbml_model(mini, "mini_fbc2.xml.bz2") write_sbml_model(mini, "mini_fbc2.xml.gz") write_sbml_model( mini, importlib_resources.files(cobra.data).joinpath("mini_cobra.xml") ) raven = load_matlab_model("raven.mat") with open("raven.pickle", "wb") as outfile: dump(raven, outfile, protocol=2) # TODO:these need a reference solutions rather than circular solution checking! # fva results fva_result = cobra.flux_analysis.flux_variability_analysis(textbook) clean_result = OrderedDict() for key in sorted(fva_result): clean_result[key] = {k: round(v, 5) for k, v in fva_result[key].items()} with open("textbook_fva.json", "w") as outfile: json_dump(clean_result, outfile) # fva with pfba constraint fva_result = cobra.flux_analysis.flux_variability_analysis( textbook, pfba_factor=1.1 ) clean_result = OrderedDict() for key in sorted(fva_result): clean_result[key] = {k: round(v, 5) for k, v in fva_result[key].items()} with open("textbook_pfba_fva.json", "w") as outfile: json_dump(clean_result, outfile) # textbook solution solution = cobra.flux_analysis.parsimonious.pfba(textbook) with open("textbook_solution.pickle", "wb") as f: dump(solution, f, protocol=2) cobrapy-0.29.1/tests/data/valid_annotation_format.json000066400000000000000000000005251467301562300231220ustar00rootroot00000000000000{ "metabolites":[ { "id":"4crsol_c", "name":"", "compartment":"c", "annotation": { "bigg.reaction": [["is", "PFK26"]], "kegg.reaction": [["is", "R02732"]], "rhea": [["is", "15656"]] } } ], "reactions":[], "genes":[], "id":"tesut", "compartments":{}, "version": 1 } cobrapy-0.29.1/tests/data/valid_annotation_output.xml000066400000000000000000000046331467301562300230250ustar00rootroot00000000000000 cobrapy-0.29.1/tests/data/validation.xml000066400000000000000000000010041467301562300201730ustar00rootroot00000000000000 cobrapy-0.29.1/tests/test_core/000077500000000000000000000000001467301562300164025ustar00rootroot00000000000000cobrapy-0.29.1/tests/test_core/conftest.py000066400000000000000000000014771467301562300206120ustar00rootroot00000000000000"""Define module level fixtures.""" from typing import TYPE_CHECKING, Tuple import pytest from cobra.util.solver import solvers if TYPE_CHECKING: from cobra import Model, Solution solver_trials = [ "glpk", pytest.param( "cplex", marks=pytest.mark.skipif( "cplex" not in solvers, reason="No CPLEX found on PYTHONPATH" ), ), pytest.param( "gurobi", marks=pytest.mark.skipif( "gurobi" not in solvers, reason="No Gurobi found on PYTHONPATH" ), ), ] @pytest.fixture(scope="function", params=solver_trials) def solved_model( request: pytest.FixtureRequest, model: "Model" ) -> Tuple["Solution", "Model"]: """Return solved model.""" model.solver = request.param solution = model.optimize() return solution, model cobrapy-0.29.1/tests/test_core/test_configuration.py000066400000000000000000000030561467301562300226660ustar00rootroot00000000000000"""Test functions of configuration.py .""" from cobra.core import Configuration, Model from cobra.util.solver import interface_to_str def test_default_bounds(): """Verify the default bounds.""" config = Configuration() assert config.bounds == (-1000.0, 1000.0) def test_bounds(): """Test changing bounds.""" config = Configuration() config.bounds = 100.0, 10000.0 assert config.bounds == (100.0, 10000.0) # Restore default values. config.bounds = -1000.0, 1000.0 def test_solver(): """Test assignment of different solvers.""" config = Configuration() config.solver = "glpk" assert interface_to_str(config.solver) == "glpk" config.solver = "glpk_exact" assert interface_to_str(config.solver) == "glpk_exact" # Restore default solver. config.solver = "glpk" def test_default_tolerance(model): """Verify the default solver tolerance.""" config = Configuration() config.solver = "glpk" assert config.tolerance == 1e-07 # Test the consistency between cobra.core.Configuration.tolerance and # cobra.core.Model.tolerance assert config.tolerance == model.tolerance def test_toy_model_tolerance_with_different_default(): """Verify that different default tolerance is respected by Model.""" config = Configuration() config.tolerance = 1e-05 toy_model = Model(name="toy model") assert toy_model.tolerance == 1e-05 def test_tolerance_assignment(model): """Test assignment of solver tolerance.""" model.tolerance = 1e-06 assert model.tolerance == 1e-06 cobrapy-0.29.1/tests/test_core/test_core_reaction.py000066400000000000000000001167721467301562300226450ustar00rootroot00000000000000"""Test functions of cobra.core.reaction .""" import warnings from typing import Iterable import numpy as np import pytest from cobra import Gene from cobra.core import GPR, Configuration, Metabolite, Model, Reaction config = Configuration() stable_optlang = ["glpk", "cplex", "gurobi"] def test_gpr() -> None: """Test GPR evaluation.""" model = Model() reaction = Reaction("test") # Set GPR to an empty string reaction.gene_reaction_rule = "" # Empty gene_reaction_rule leads to an empty GPR assert reaction.gpr.body is None assert reaction.gpr.to_string() == "" assert reaction.gpr.to_string(names={"goo": "blah"}) == "" # Set GPR directly (shouldn't really be used, but just a test) reaction.gpr = GPR() assert reaction.gene_reaction_rule == "" assert reaction.gpr.eval() # Set GPR to a reaction not in a model reaction.gene_reaction_rule = "(g1 or g2) and g3" assert reaction.gene_reaction_rule == "(g1 or g2) and g3" assert len(reaction.genes) == 3 # Adding reaction with a GPR propagates to the model model.add_reactions([reaction]) assert len(model.genes) == 3 # Ensure the gene objects are the same in the model and reaction reaction_gene = list(reaction.genes)[0] model_gene = model.genes.get_by_id(reaction_gene.id) assert reaction_gene is model_gene def test_gpr_uppercase() -> None: """Test ability to handle uppercase AND/OR.""" reaction = Reaction("test") with pytest.warns(SyntaxWarning): reaction.gene_reaction_rule = "(b1 AND b2) OR (b3 and b4)" assert reaction.gene_reaction_rule == "(b1 and b2) or (b3 and b4)" assert len(reaction.genes) == 4 @pytest.mark.parametrize("input_gpr", ["(a1 or a2", "(forT or "]) def test_gpr_malformed(input_gpr: str) -> None: """Test ability to deal with malformed GPR. Malformed GPR strings will lead to empty GPRs with no genes. Parameters ---------- input_gpr: str String representing a malformed GPR string. """ reaction = Reaction("test") with warnings.catch_warnings(): warnings.simplefilter("ignore") reaction.gene_reaction_rule = input_gpr assert len(reaction.genes) == 0 def test_gpr_modification(model: Model) -> None: """Test GPR manipulations.""" reaction = model.reactions.get_by_id("PGI") old_gene = list(reaction.genes)[0] new_gene = model.genes.get_by_id("s0001") # Add an existing 'gene' to the GPR reaction.gene_reaction_rule = "s0001" assert new_gene in reaction.genes assert reaction in new_gene.reactions # Remove old gene correctly, keep it in model assert old_gene not in reaction.genes assert reaction not in old_gene.reactions assert old_gene in model.genes # Add a new 'gene' to the GPR reaction.gene_reaction_rule = "fake_gene" assert model.genes.has_id("fake_gene") fake_gene = model.genes.get_by_id("fake_gene") assert fake_gene in reaction.genes assert reaction in fake_gene.reactions fake_gene.name = "foo_gene" assert reaction.gene_name_reaction_rule == fake_gene.name def test_gpr_modification_with_context(model: Model) -> None: """Test GPR manipulations are reversed in context.""" empty_model = Model() reaction = Reaction("test") reaction.gene_reaction_rule = "(g1 or g2) and g3" assert reaction.gene_reaction_rule == "(g1 or g2) and g3" assert len(reaction.genes) == 3 with empty_model: # Adding reaction with a GPR propagates to the model empty_model.add_reactions([reaction]) assert len(empty_model.genes) == 3 assert len(empty_model.reactions) == 0 assert len(empty_model.genes) == 0 assert reaction._model is None reaction = model.reactions.get_by_id("PGI") old_reaction_rule = reaction.gene_reaction_rule old_gene = list(reaction.genes)[0] new_gene = model.genes.get_by_id("s0001") with model: # Add an existing 'gene' to the GPR reaction.gene_reaction_rule = "s0001" assert new_gene in reaction.genes assert reaction in new_gene.reactions # Remove old gene correctly, keep it in model assert old_gene not in reaction.genes assert reaction not in old_gene.reactions assert old_gene in model.genes assert reaction.gene_reaction_rule == old_reaction_rule assert new_gene not in reaction.genes assert reaction not in new_gene.reactions # Remove old gene correctly, keep it in model assert old_gene in reaction.genes assert reaction in old_gene.reactions assert old_gene in model.genes with model: # Add a new 'gene' to the GPR reaction.gene_reaction_rule = "fake_gene" assert model.genes.has_id("fake_gene") fake_gene = model.genes.get_by_id("fake_gene") assert fake_gene in reaction.genes assert reaction in fake_gene.reactions fake_gene.name = "foo_gene" assert reaction.gene_name_reaction_rule == fake_gene.name assert not model.genes.has_id("fake_gene") fake_gene = Gene("fake_gene") assert fake_gene not in reaction.genes assert reaction not in fake_gene.reactions def test_gene_knock_out(model: Model) -> None: """Test gene knockout effect on reaction.""" rxn = Reaction("rxn") rxn.add_metabolites({Metabolite("A"): -1, Metabolite("B"): 1}) rxn.gene_reaction_rule = "A2B1 or A2B2 and A2B3" assert hasattr(list(rxn.genes)[0], "knock_out") model.add_reactions([rxn]) with model: model.genes.A2B1.knock_out() assert not model.genes.A2B1.functional model.genes.A2B3.knock_out() assert not rxn.functional assert model.genes.A2B3.functional assert rxn.functional model.genes.A2B1.knock_out() assert not model.genes.A2B1.functional assert model.reactions.rxn.functional model.genes.A2B3.knock_out() assert not model.reactions.rxn.functional def test_str() -> None: """Test `str` output for a reaction.""" rxn = Reaction("rxn") rxn.add_metabolites({Metabolite("A"): -1, Metabolite("B"): 1}) assert str(rxn) == "rxn: A --> B" def test_str_from_model(model: Model) -> None: """Test `str` output for a reaction associated with a model.""" assert model.reactions[0].__str__().startswith("ACALD") def test_add_metabolite_from_solved_model(solved_model: Model) -> None: """Test metabolite addition to a reaction from a solved model.""" solution, model = solved_model pgi_reaction = model.reactions.PGI test_met = model.metabolites[0] pgi_reaction.add_metabolites({test_met: 42}, combine=False) assert pgi_reaction.metabolites[test_met] == 42.0 assert ( model.constraints[test_met.id].expression.as_coefficients_dict()[ pgi_reaction.forward_variable ] == 42.0 ) assert ( model.constraints[test_met.id].expression.as_coefficients_dict()[ pgi_reaction.reverse_variable ] == -42.0 ) pgi_reaction.add_metabolites({test_met: -10}, combine=True) assert pgi_reaction.metabolites[test_met] == 32.0 assert ( model.constraints[test_met.id].expression.as_coefficients_dict()[ pgi_reaction.forward_variable ] == 32.0 ) assert ( model.constraints[test_met.id].expression.as_coefficients_dict()[ pgi_reaction.reverse_variable ] == -32.0 ) pgi_reaction.add_metabolites({test_met: 0}, combine=False) with pytest.raises(KeyError): pgi_reaction.metabolites[test_met] assert ( model.constraints[test_met.id].expression.as_coefficients_dict()[ pgi_reaction.forward_variable ] == 0 ) assert ( model.constraints[test_met.id].expression.as_coefficients_dict()[ pgi_reaction.reverse_variable ] == 0 ) @pytest.mark.parametrize("solver", stable_optlang) def test_add_metabolite_benchmark(model: Model, benchmark, solver: Iterable) -> None: """Benchmark metabolite addition to a reaction associated with a model.""" reaction = model.reactions.get_by_id("PGI") many_metabolites = dict((m, 1) for m in model.metabolites[0:50]) def add_remove_metabolite(): reaction.add_metabolites(many_metabolites) if not getattr(model, "solver", None): stable_optlang[solver].create_problem(model) for met in many_metabolites: try: reaction.subtract_metabolites({met: reaction.get_coefficient(met)}) except KeyError: pass benchmark(add_remove_metabolite) def test_add_metabolite(model: Model) -> None: """Test metabolite addition to a reaction from an unsolved model.""" with pytest.raises(ValueError): model.add_metabolites(Metabolite()) with model: with model: reaction = model.reactions.get_by_id("PGI") reaction.add_metabolites({model.metabolites[0]: 1}) assert model.metabolites[0] in reaction._metabolites fake_metabolite = Metabolite("fake") reaction.add_metabolites({fake_metabolite: 1}) assert fake_metabolite in reaction._metabolites assert model.metabolites.has_id("fake") assert model.metabolites.get_by_id("fake") is fake_metabolite assert len(model._contexts[0]._history) == 0 assert fake_metabolite._model is None assert fake_metabolite not in reaction._metabolites assert "fake" not in model.metabolites # Test adding by string with model: reaction.add_metabolites({"g6p_c": -1}) # already in reaction assert reaction._metabolites[model.metabolites.get_by_id("g6p_c")] == -2 reaction.add_metabolites({"h_c": 1}) assert reaction._metabolites[model.metabolites.get_by_id("h_c")] == 1 with pytest.raises(KeyError): reaction.add_metabolites({"missing": 1}) assert reaction._metabolites[model.metabolites.get_by_id("g6p_c")] == -1 assert model.metabolites.h_c not in reaction._metabolites # Test combine=False reaction = model.reactions.get_by_id("ATPM") old_stoich = reaction._metabolites[model.metabolites.get_by_id("h2o_c")] with model: reaction.add_metabolites({"h2o_c": 2.5}, combine=False) assert reaction._metabolites[model.metabolites.get_by_id("h2o_c")] == 2.5 assert reaction._metabolites[model.metabolites.get_by_id("h2o_c")] == old_stoich # Test adding to a new Reaction reaction = Reaction("test") assert len(reaction._metabolites) == 0 reaction.add_metabolites({Metabolite("test_met"): -1}) assert len(reaction._metabolites) == 1 @pytest.mark.parametrize("solver", stable_optlang) def test_subtract_metabolite_benchmark( model: Model, benchmark, solver: Iterable ) -> None: """Benchmark metabolite deletion from a reaction.""" benchmark(test_subtract_metabolite, model, solver) @pytest.mark.parametrize("solver", stable_optlang) def test_subtract_metabolite(model: Model, solver: Iterable) -> None: """Test metabolite deletion from a reaction associated with an unsolved model.""" reaction = model.reactions.get_by_id("PGI") reaction.subtract_metabolites(reaction.metabolites) if not getattr(model, "solver", None): stable_optlang[solver].create_problem(model) assert len(reaction.metabolites) == 0 def test_mass_balance(model: Model) -> None: """Test mass balance of metabolites of a reaction.""" reaction = model.reactions.get_by_id("PGI") # Should be balanced now assert len(reaction.check_mass_balance()) == 0 # Should not be balanced after adding a hydrogen reaction.add_metabolites({model.metabolites.get_by_id("h_c"): 1}) imbalance = reaction.check_mass_balance() assert "H" in imbalance assert imbalance["H"] == 1 def test_build_from_string(model: Model) -> None: """Test reaction building from string evaluation.""" m = len(model.metabolites) pgi = model.reactions.get_by_id("PGI") old_bounds = pgi.bounds with model: pgi.reaction = "g6p_c --> f6p_c" assert pgi.lower_bound == 0 assert pgi.bounds == old_bounds pgi.bounds = (0, 1000) assert pgi.bounds == (0, 1000) assert not pgi.reversibility pgi.reaction = "g6p_c <== f6p_c" assert pgi.upper_bound == 0 assert pgi.reaction.strip() == "g6p_c <-- f6p_c" pgi.reaction = "g6p_c --> f6p_c + h2o_c" assert model.metabolites.h2o_c, pgi._metabolites with model: pgi.build_reaction_from_string("g6p_c --> f6p_c + foo", verbose=False) assert model.metabolites.h2o_c not in pgi._metabolites assert "foo" in model.metabolites assert model.metabolites.foo in pgi._metabolites assert len(model.metabolites) == m + 1 assert model.metabolites.h2o_c in pgi._metabolites assert "foo" not in model.metabolites with pytest.raises(AttributeError): assert model.metabolites.foo assert len(model.metabolites) == m with model: old_bounds = config.bounds assert old_bounds == (-1000, 1000) config.bounds = (-5, 5) pgi.build_reaction_from_string("g6p_c <--> f6p_c + new", verbose=False) assert pgi.bounds == (-5, 5) config.bounds = old_bounds pgi.build_reaction_from_string("g6p_c --> f6p_c + new", verbose=False) assert pgi.bounds == (0, 1000) def test_bounds_setter(model: Model) -> None: """Test reaction bounds setter.""" rxn = model.reactions.get_by_id("PGI") with pytest.raises(ValueError): rxn.bounds = (1, 0) def test_copy(model: Model) -> None: """Test reaction copying.""" PGI = model.reactions.PGI copied = PGI.copy() assert PGI is not copied assert PGI._model is model assert copied._model is not model # The copy should refer to different metabolites and genes for met in copied.metabolites: assert met is not model.metabolites.get_by_id(met.id) assert met.model is not model for gene in copied.genes: assert gene is not model.genes.get_by_id(gene.id) assert gene.model is not model assert len(model.get_associated_groups(copied.id)) == 0 def test_iadd(model: Model) -> None: """Test in-place addition of reaction.""" PGI = model.reactions.PGI EX_h2o = model.reactions.EX_h2o_e original_PGI_gpr = PGI.gene_reaction_rule PGI += EX_h2o assert PGI.gene_reaction_rule == original_PGI_gpr assert PGI.metabolites[model.metabolites.h2o_e] == -1.0 # Original should not change assert EX_h2o.gene_reaction_rule == "" assert EX_h2o.metabolites[model.metabolites.h2o_e] == -1.0 # Add a reaction not in the model new_reaction = Reaction("test") new_reaction.add_metabolites({Metabolite("A"): -1, Metabolite("B"): 1}) PGI += new_reaction assert PGI.gene_reaction_rule == original_PGI_gpr assert len(PGI.gene_reaction_rule) == 5 # And vice versa new_reaction += PGI assert len(new_reaction.metabolites) == 5 # not assert len(new_reaction.genes) == 1 assert new_reaction.gene_reaction_rule == original_PGI_gpr # Combine two GPRs model.reactions.ACKr += model.reactions.ACONTa expected_rule = "(b2296 or b3115 or b1849) and (b0118 or b1276)" assert model.reactions.ACKr.gene_reaction_rule == expected_rule assert len(model.reactions.ACKr.genes) == 5 def test_iadd_with_context(model: Model) -> None: """Test in-place addition of reaction is reversed with context.""" PGI = model.reactions.PGI EX_h2o = model.reactions.EX_h2o_e original_PGI_gene_reaction_rule = PGI.gene_reaction_rule with model: PGI += EX_h2o assert PGI.gene_reaction_rule == original_PGI_gene_reaction_rule assert PGI.metabolites[model.metabolites.h2o_e] == -1.0 assert PGI.gene_reaction_rule == original_PGI_gene_reaction_rule assert model.metabolites.h2o_e not in PGI.metabolites.keys() # Add a reaction not in the model new_reaction = Reaction("test") new_reaction.add_metabolites({Metabolite("A"): -1, Metabolite("B"): 1}) with model: PGI += new_reaction assert PGI.gene_reaction_rule == original_PGI_gene_reaction_rule assert len(PGI.gene_reaction_rule) == 5 # Combine two GPRs expected_rule = "(b2296 or b3115 or b1849) and (b0118 or b1276)" old_rule = model.reactions.ACKr.gene_reaction_rule with model: model.reactions.ACKr += model.reactions.ACONTa assert model.reactions.ACKr.gene_reaction_rule == expected_rule assert len(model.reactions.ACKr.genes) == 5 assert model.reactions.ACKr.gene_reaction_rule == old_rule assert old_rule != expected_rule assert len(model.reactions.ACKr.genes) == 3 def test_add(model: Model) -> None: """Test reaction addition to model.""" # Not in place addition should work on a copy new = model.reactions.PGI + model.reactions.EX_h2o_e assert new._model is not model assert len(new.metabolites) == 3 # The copy should refer to different metabolites and genes # This currently fails because add_metabolites does not copy. # Should that be changed? # for met in new.metabolites: # assert met is not model.metabolites.get_by_id(met.id) # assert met.model is not model for gene in new.genes: assert gene is not model.genes.get_by_id(gene.id) assert gene.model is not model def test_radd(model: Model) -> None: """Test __radd__ for a reaction.""" new = sum([model.reactions.PGI, model.reactions.EX_h2o_e]) assert new._model is not model assert len(new.metabolites) == 3 def test_mul(model: Model) -> None: """Test scalar multiplication of factors with a reaction.""" new = model.reactions.PGI * 2 assert set(new.metabolites.values()) == {-2, 2} def test_sub(model: Model) -> None: """Test reaction subtraction.""" new = model.reactions.PGI - model.reactions.EX_h2o_e assert new._model is not model assert len(new.metabolites) == 3 def test_removal_from_model_retains_bounds(model: Model) -> None: """Test reaction removal from a model, retains its bounds.""" model_cp = model.copy() reaction = model_cp.reactions.ACALD assert reaction.model == model_cp assert reaction.lower_bound == -1000.0 assert reaction.upper_bound == 1000.0 assert reaction._lower_bound == -1000.0 assert reaction._upper_bound == 1000.0 model_cp.remove_reactions([reaction]) assert reaction.model is None assert reaction.lower_bound == -1000.0 assert reaction.upper_bound == 1000.0 assert reaction._lower_bound == -1000.0 assert reaction._upper_bound == 1000.0 def test_set_bounds_scenario_1(model: Model) -> None: """Test reaction bounds setting for a scenario.""" acald_reaction = model.reactions.ACALD assert acald_reaction.lower_bound == -1000.0 assert acald_reaction.upper_bound == 1000.0 assert acald_reaction.forward_variable.lb == 0.0 assert acald_reaction.forward_variable.ub == 1000.0 assert acald_reaction.reverse_variable.lb == 0 assert acald_reaction.reverse_variable.ub == 1000.0 acald_reaction.bounds = ( acald_reaction.lower_bound - 100, acald_reaction.lower_bound - 100, ) assert acald_reaction.lower_bound == -1100.0 assert acald_reaction.upper_bound == -1100.0 assert acald_reaction.forward_variable.lb == 0 assert acald_reaction.forward_variable.ub == 0 assert acald_reaction.reverse_variable.lb == 1100.0 assert acald_reaction.reverse_variable.ub == 1100.0 acald_reaction.upper_bound = 100 assert acald_reaction.lower_bound == -1100.0 assert acald_reaction.upper_bound == 100 assert acald_reaction.forward_variable.lb == 0 assert acald_reaction.forward_variable.ub == 100 assert acald_reaction.reverse_variable.lb == 0 assert acald_reaction.reverse_variable.ub == 1100.0 def test_set_bounds_scenario_2(model: Model) -> None: """Test reaction bounds setting for a scenario.""" acald_reaction = model.reactions.ACALD assert acald_reaction.lower_bound == -1000.0 assert acald_reaction.upper_bound == 1000.0 assert acald_reaction.forward_variable.lb == 0.0 assert acald_reaction.forward_variable.ub == 1000.0 assert acald_reaction.reverse_variable.lb == 0 assert acald_reaction.reverse_variable.ub == 1000.0 acald_reaction.bounds = ( acald_reaction.upper_bound + 100, acald_reaction.upper_bound + 100, ) assert acald_reaction.lower_bound == 1100.0 assert acald_reaction.upper_bound == 1100.0 assert acald_reaction.forward_variable.lb == 1100.0 assert acald_reaction.forward_variable.ub == 1100.0 assert acald_reaction.reverse_variable.lb == 0 assert acald_reaction.reverse_variable.ub == 0 acald_reaction.lower_bound = -100 assert acald_reaction.lower_bound == -100.0 assert acald_reaction.upper_bound == 1100.0 assert acald_reaction.forward_variable.lb == 0 assert acald_reaction.forward_variable.ub == 1100.0 assert acald_reaction.reverse_variable.lb == 0 assert acald_reaction.reverse_variable.ub == 100 def test_set_bounds_scenario_3(model: Model) -> None: """Test reaction bounds setting for a scenario.""" reac = model.reactions.ACALD reac.bounds = (-10, -10) assert reac.lower_bound == -10 assert reac.upper_bound == -10 reac.bounds = (-9, -9) assert reac.lower_bound == -9 assert reac.upper_bound == -9 reac.bounds = (2, 2) assert reac.lower_bound == 2 assert reac.upper_bound == 2 reac.bounds = (-10, -10) assert reac.lower_bound == -10 assert reac.upper_bound == -10 reac.bounds = (-11, -11) assert reac.lower_bound == -11 assert reac.upper_bound == -11 reac.upper_bound = 2 assert reac.lower_bound == -11 assert reac.upper_bound == 2 def test_set_bounds_scenario_4(model: Model) -> None: """Test reaction bounds setting for a scenario.""" reac = model.reactions.ACALD reac.bounds = (2, 2) assert reac.lower_bound == 2 assert reac.upper_bound == 2 assert reac.forward_variable.lb == 2 assert reac.forward_variable.ub == 2 reac.knock_out() reac.bounds = (-2, -2) assert reac.lower_bound == -2 assert reac.upper_bound == -2 assert reac.reverse_variable.lb == 2 assert reac.reverse_variable.ub == 2 def test_set_upper_before_lower_bound_to_0(model: Model) -> None: """Test reaction bounds setting to zero.""" model.reactions.GAPD.bounds = (0, 0) assert model.reactions.GAPD.lower_bound == 0 assert model.reactions.GAPD.upper_bound == 0 assert model.reactions.GAPD.forward_variable.lb == 0 assert model.reactions.GAPD.forward_variable.ub == 0 assert model.reactions.GAPD.reverse_variable.lb == 0 assert model.reactions.GAPD.reverse_variable.ub == 0 def test_change_bounds(model: Model) -> None: """Test reaction bounds change.""" reac = model.reactions.ACALD reac.bounds = (2, 2) assert reac.lower_bound == 2 assert reac.upper_bound == 2 with model: reac.bounds = (5, 5) assert reac.lower_bound == 5 assert reac.upper_bound == 5 assert reac.lower_bound == 2 assert reac.upper_bound == 2 def test_make_irreversible(model: Model) -> None: """Test reaction irreversibility.""" acald_reaction = model.reactions.ACALD assert acald_reaction.lower_bound == -1000.0 assert acald_reaction.upper_bound == 1000.0 assert acald_reaction.forward_variable.lb == 0.0 assert acald_reaction.forward_variable.ub == 1000.0 assert acald_reaction.reverse_variable.lb == 0 assert acald_reaction.reverse_variable.ub == 1000.0 acald_reaction.lower_bound = 0 assert acald_reaction.lower_bound == 0 assert acald_reaction.upper_bound == 1000.0 assert acald_reaction.forward_variable.lb == 0 assert acald_reaction.forward_variable.ub == 1000.0 assert acald_reaction.reverse_variable.lb == 0 assert acald_reaction.reverse_variable.ub == 0 acald_reaction.lower_bound = -100 assert acald_reaction.lower_bound == -100.0 assert acald_reaction.upper_bound == 1000.0 assert acald_reaction.forward_variable.lb == 0 assert acald_reaction.forward_variable.ub == 1000.0 assert acald_reaction.reverse_variable.lb == 0 assert acald_reaction.reverse_variable.ub == 100 def test_make_reversible(model: Model) -> None: """Test reaction reversibility.""" pfk_reaction = model.reactions.PFK assert pfk_reaction.lower_bound == 0.0 assert pfk_reaction.upper_bound == 1000.0 assert pfk_reaction.forward_variable.lb == 0.0 assert pfk_reaction.forward_variable.ub == 1000.0 assert pfk_reaction.reverse_variable.lb == 0 assert pfk_reaction.reverse_variable.ub == 0 pfk_reaction.lower_bound = -100.0 assert pfk_reaction.lower_bound == -100.0 assert pfk_reaction.upper_bound == 1000.0 assert pfk_reaction.forward_variable.lb == 0 assert pfk_reaction.forward_variable.ub == 1000.0 assert pfk_reaction.reverse_variable.lb == 0 assert pfk_reaction.reverse_variable.ub == 100.0 pfk_reaction.lower_bound = 0 assert pfk_reaction.lower_bound == 0 assert pfk_reaction.upper_bound == 1000.0 assert pfk_reaction.forward_variable.lb == 0 assert pfk_reaction.forward_variable.ub == 1000.0 assert pfk_reaction.reverse_variable.lb == 0 assert pfk_reaction.reverse_variable.ub == 0 def test_make_irreversible_irreversible_to_the_other_side(model: Model) -> None: """Test reaction irreversibility to irreversibility.""" pfk_reaction = model.reactions.PFK assert pfk_reaction.lower_bound == 0.0 assert pfk_reaction.upper_bound == 1000.0 assert pfk_reaction.forward_variable.lb == 0.0 assert pfk_reaction.forward_variable.ub == 1000.0 assert pfk_reaction.reverse_variable.lb == 0 assert pfk_reaction.reverse_variable.ub == 0 pfk_reaction.bounds = (-100.0, -100.0) assert pfk_reaction.forward_variable.lb == 0 assert pfk_reaction.forward_variable.ub == 0 assert pfk_reaction.reverse_variable.lb == 100 assert pfk_reaction.reverse_variable.ub == 100 pfk_reaction.lower_bound = -1000.0 assert pfk_reaction.lower_bound == -1000.0 assert pfk_reaction.upper_bound == -100.0 assert pfk_reaction.forward_variable.lb == 0 assert pfk_reaction.forward_variable.ub == 0 assert pfk_reaction.reverse_variable.lb == 100 assert pfk_reaction.reverse_variable.ub == 1000.0 def test_make_lhs_irreversible_reversible(model: Model) -> None: """Test reaction LHS irreversibility to reversibility.""" rxn = Reaction("test") rxn.add_metabolites({model.metabolites[0]: -1.0, model.metabolites[1]: 1.0}) rxn.bounds = (-1000.0, -100) model.add_reactions([rxn]) assert rxn.lower_bound == -1000.0 assert rxn.upper_bound == -100.0 assert rxn.forward_variable.lb == 0.0 assert rxn.forward_variable.ub == 0.0 assert rxn.reverse_variable.lb == 100.0 assert rxn.reverse_variable.ub == 1000.0 rxn.upper_bound = 666.0 assert rxn.lower_bound == -1000.0 assert rxn.upper_bound == 666.0 assert rxn.forward_variable.lb == 0.0 assert rxn.forward_variable.ub == 666 assert rxn.reverse_variable.lb == 0.0 assert rxn.reverse_variable.ub == 1000.0 def test_model_less_reaction(model: Model) -> None: """Test model without reactions.""" model.slim_optimize() for reaction in model.reactions: assert isinstance(reaction.flux, float) assert isinstance(reaction.reduced_cost, float) for reaction in model.reactions: model.remove_reactions([reaction]) with pytest.raises(RuntimeError): reaction.flux with pytest.raises(RuntimeError): reaction.reduced_cost def test_knockout(model: Model) -> None: """Test reaction knockouts.""" original_bounds = dict() for reaction in model.reactions: original_bounds[reaction.id] = (reaction.lower_bound, reaction.upper_bound) reaction.knock_out() assert reaction.lower_bound == 0 assert reaction.upper_bound == 0 for k, (lb, ub) in original_bounds.items(): model.reactions.get_by_id(k).bounds = (lb, ub) for reaction in model.reactions: assert reaction.lower_bound == original_bounds[reaction.id][0] assert reaction.upper_bound == original_bounds[reaction.id][1] with model: for reaction in model.reactions: original_bounds[reaction.id] = (reaction.lower_bound, reaction.upper_bound) reaction.knock_out() assert reaction.lower_bound == 0 assert reaction.upper_bound == 0 for reaction in model.reactions: assert reaction.lower_bound == original_bounds[reaction.id][0] assert reaction.upper_bound == original_bounds[reaction.id][1] def test_reaction_without_model() -> None: """Test reaction without model association.""" r = Reaction("blub") assert r.flux_expression is None assert r.forward_variable is None assert r.reverse_variable is None def test_weird_left_to_right_reaction_issue(tiny_toy_model: Model) -> None: """Test absurd left to right reaction.""" d1 = tiny_toy_model.reactions.get_by_id("ex1") assert not d1.reversibility assert d1.lower_bound == -1000 assert d1._lower_bound == -1000 assert d1.upper_bound == 0 assert d1._upper_bound == 0 with tiny_toy_model: d1.knock_out() assert d1.lower_bound == 0 assert d1._lower_bound == 0 assert d1.upper_bound == 0 assert d1._upper_bound == 0 assert d1.lower_bound == -1000 assert d1._lower_bound == -1000 assert d1.upper_bound == 0 assert d1._upper_bound == 0 def test_one_left_to_right_reaction_set_positive_ub(tiny_toy_model: Model) -> None: """Test left to right reaction with positive upper bound.""" d1 = tiny_toy_model.reactions.get_by_id("ex1") assert d1.reverse_variable.lb == 0 assert d1.reverse_variable.ub == 1000 assert d1._lower_bound == -1000 assert d1.lower_bound == -1000 assert d1._upper_bound == 0 assert d1.upper_bound == 0 assert d1.forward_variable.lb == 0 assert d1.forward_variable.ub == 0 d1.upper_bound = 0.1 assert d1.forward_variable.lb == 0 assert d1.forward_variable.ub == 0.1 assert d1.reverse_variable.lb == 0 assert d1.reverse_variable.ub == 1000 assert d1._lower_bound == -1000 assert d1.upper_bound == 0.1 assert d1._lower_bound == -1000 assert d1.upper_bound == 0.1 def test_irrev_reaction_set_negative_lb(model: Model) -> None: """Test reaction irreversibility with negative lower bound.""" assert not model.reactions.PFK.reversibility assert model.reactions.PFK.lower_bound == 0 assert model.reactions.PFK.upper_bound == 1000.0 assert model.reactions.PFK.forward_variable.lb == 0 assert model.reactions.PFK.forward_variable.ub == 1000.0 assert model.reactions.PFK.reverse_variable.lb == 0 assert model.reactions.PFK.reverse_variable.ub == 0 model.reactions.PFK.lower_bound = -1000 assert model.reactions.PFK.lower_bound == -1000 assert model.reactions.PFK.upper_bound == 1000.0 assert model.reactions.PFK.forward_variable.lb == 0 assert model.reactions.PFK.forward_variable.ub == 1000.0 assert model.reactions.PFK.reverse_variable.lb == 0 assert model.reactions.PFK.reverse_variable.ub == 1000 def test_twist_irrev_right_to_left_reaction_to_left_to_right(model: Model) -> None: """Test irreversibility reversal from right to left to left to right.""" assert not model.reactions.PFK.reversibility assert model.reactions.PFK.lower_bound == 0 assert model.reactions.PFK.upper_bound == 1000.0 assert model.reactions.PFK.forward_variable.lb == 0 assert model.reactions.PFK.forward_variable.ub == 1000.0 assert model.reactions.PFK.reverse_variable.lb == 0 assert model.reactions.PFK.reverse_variable.ub == 0 model.reactions.PFK.bounds = (-1000, 0) assert model.reactions.PFK.lower_bound == -1000 assert model.reactions.PFK.upper_bound == 0 assert model.reactions.PFK.forward_variable.lb == 0 assert model.reactions.PFK.forward_variable.ub == 0 assert model.reactions.PFK.reverse_variable.lb == 0 assert model.reactions.PFK.reverse_variable.ub == 1000 def test_set_lb_higher_than_ub_sets_ub_to_new_lb(model: Model) -> None: """Test lower bound > upper bound makes upper bound to new lower bound.""" for reaction in model.reactions: assert reaction.lower_bound <= reaction.upper_bound reaction.bounds = (reaction.upper_bound + 100, reaction.upper_bound + 100) assert reaction.lower_bound == reaction.upper_bound def test_set_ub_lower_than_lb_sets_lb_to_new_ub(model: Model) -> None: """Test upper bound < lower bound makes lower bound to new upper bound.""" for reaction in model.reactions: assert reaction.lower_bound <= reaction.upper_bound reaction.bounds = (reaction.lower_bound - 100, reaction.lower_bound - 100) assert reaction.lower_bound == reaction.upper_bound def test_add_metabolites_combine_true(model: Model) -> None: """Test metabolite addition to reaction (with combine = True).""" test_metabolite = Metabolite("test") for reaction in model.reactions: reaction.add_metabolites({test_metabolite: -66}, combine=True) assert reaction.metabolites[test_metabolite] == -66 assert ( model.constraints["test"].get_linear_coefficients( [reaction.forward_variable] )[reaction.forward_variable] == -66 ) assert ( model.constraints["test"].get_linear_coefficients( [reaction.reverse_variable] )[reaction.reverse_variable] == 66 ) already_included_metabolite = list(reaction.metabolites.keys())[0] previous_coefficient = reaction.get_coefficient(already_included_metabolite.id) reaction.add_metabolites({already_included_metabolite: 10}, combine=True) new_coefficient = previous_coefficient + 10 assert reaction.metabolites[already_included_metabolite] == new_coefficient assert ( model.constraints[already_included_metabolite.id].get_linear_coefficients( [reaction.forward_variable] )[reaction.forward_variable] == new_coefficient ) assert ( model.constraints[already_included_metabolite.id].get_linear_coefficients( [reaction.reverse_variable] )[reaction.reverse_variable] == -new_coefficient ) @pytest.mark.xfail(reason="non-deterministic test") def test_add_metabolites_combine_false(model: Model) -> None: """Test metabolite addition to reaction (with combine = False).""" test_metabolite = Metabolite("test") for reaction in model.reactions: reaction.add_metabolites({test_metabolite: -66}, combine=False) assert reaction.metabolites[test_metabolite] == -66 assert model.constraints["test"].expression.has( -66.0 * reaction.forward_variable ) assert model.constraints["test"].expression.has( 66.0 * reaction.reverse_variable ) already_included_metabolite = list(reaction.metabolites.keys())[0] reaction.add_metabolites({already_included_metabolite: 10}, combine=False) assert reaction.metabolites[already_included_metabolite] == 10 assert model.constraints[already_included_metabolite.id].expression.has( 10 * reaction.forward_variable ) assert model.constraints[already_included_metabolite.id].expression.has( -10 * reaction.reverse_variable ) def test_reaction_imul(model: Model) -> None: """Test in-place scalar factor multiplication to reaction.""" with model: model.reactions.EX_glc__D_e *= 100 assert ( model.constraints.glc__D_e.expression.coeff(model.variables.EX_glc__D_e) == -100.0 ) assert model.reactions.EX_glc__D_e.reaction == "100.0 glc__D_e <=> " assert ( model.constraints.glc__D_e.expression.coeff(model.variables.EX_glc__D_e) == -1.0 ) assert model.reactions.EX_glc__D_e.reaction == "glc__D_e <=> " with model: model.reactions.EX_glc__D_e *= -2 assert model.reactions.EX_glc__D_e.bounds == (-1000.0, 10.0) assert model.reactions.EX_glc__D_e.reaction == " <=> 2.0 glc__D_e" assert model.reactions.EX_glc__D_e.bounds == (-10, 1000.0) assert model.reactions.EX_glc__D_e.reaction == "glc__D_e <=> " # def test_pop(model): # pgi = model.reactions.PGI # g6p = model.metabolites.get_by_id("g6p_c") # f6p = model.metabolites.get_by_id("f6p_c") # g6p_expr = model.solver.constraints["g6p_c"].expression # g6p_coef = pgi.pop("g6p_c") # assert g6p not in pgi.metabolites # actual = model.solver.constraints[ # "g6p_c"].expression.as_coefficients_dict() # expected = (g6p_expr - g6p_coef * pgi.flux_expression # ).as_coefficients_dict() # assert actual == expected # assert pgi.metabolites[f6p] == 1 # # f6p_expr = model.solver.constraints["f6p_c"].expression # f6p_coef = pgi.pop(f6p) # assert f6p not in pgi.metabolites # assert model.solver.constraints[ # "f6p_c"].expression.as_coefficients_dict() == ( # f6p_expr - f6p_coef * pgi.flux_expression # ).as_coefficients_dict() def test_remove_from_model(model: Model) -> None: """Test reaction removal from model.""" pgi = model.reactions.PGI g6p = model.metabolites.g6p_c pgi_flux = model.optimize().fluxes["PGI"] assert abs(pgi_flux) > 1e-6 with model: pgi.remove_from_model() assert pgi.model is None assert "PGI" not in model.reactions assert pgi.id not in model.variables assert pgi.reverse_id not in model.variables assert pgi not in g6p.reactions model.optimize() assert "PGI" in model.reactions assert pgi.id in model.variables assert pgi.reverse_id in model.variables assert pgi.forward_variable.problem is model.solver assert pgi in g6p.reactions assert g6p in pgi.metabolites assert np.isclose(pgi_flux, model.optimize().fluxes["PGI"]) def test_change_id_is_reflected_in_solver(model: Model) -> None: """Test reaction ID change reflection in solver.""" for i, reaction in enumerate(model.reactions): old_reaction_id = reaction.id assert model.variables[old_reaction_id].name == old_reaction_id assert old_reaction_id in model.variables new_reaction_id = reaction.id + "_" + str(i) reaction.id = new_reaction_id assert reaction.id == new_reaction_id assert not (old_reaction_id in model.variables) assert reaction.id in model.variables assert reaction.reverse_id in model.variables name = model.variables[reaction.id].name assert name == reaction.id def test_repr_html_(model: Model) -> None: """Test __repr_html__ functionality.""" assert "" in model.reactions[0]._repr_html_() def test_compartment_changes(model: Model) -> None: """Test reaction compartment change.""" rxn = model.reactions.EX_ac_e assert rxn.reactants[0].compartment in rxn.compartments rxn.reactants[0].compartment = "blub" assert rxn.reactants[0].compartment in rxn.compartments def test_gpr_serialization(model: Model) -> None: """Verify that reactions GPRs are serialized compactly as str.""" state = model.reactions[0].__getstate__() assert isinstance(state["_gpr"], str) def test_gpr_serialization_backwards_compatibility(model: Model) -> None: """Verify that GPR serialization is backwards compatible.""" state = model.reactions[0].__getstate__() print(state) state["gene_reaction_rule"] = state["_gpr"] # old format del state["_gpr"] rxn = Reaction("test") rxn.__setstate__(state) assert isinstance(rxn.gpr, GPR) cobrapy-0.29.1/tests/test_core/test_dictlist.py000066400000000000000000000321521467301562300216350ustar00rootroot00000000000000"""Test functions of dictlist.py.""" import re from copy import copy, deepcopy from pickle import HIGHEST_PROTOCOL, dumps, loads from typing import Tuple import pytest from cobra.core import DictList, Object @pytest.fixture(scope="function") def dict_list() -> Tuple[Object, DictList]: """Provide function-level fixture for a filled dictlist. Returns ------- tuple of Object and DictList The tuple with an Object and a filled DictList. """ obj = Object("test1") test_list = DictList() test_list.append(obj) return obj, test_list def test_contains(dict_list: Tuple[Object, DictList]) -> None: """Test containment check for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list assert obj in test_list assert obj.id in test_list assert Object("not_in") not in test_list assert "not_in" not in test_list def test_index(dict_list: Tuple[Object, DictList]) -> None: """Test indexing for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list assert test_list.index("test1") == 0 assert test_list.index(obj) == 0 with pytest.raises(ValueError): test_list.index("f") with pytest.raises(ValueError): test_list.index(Object("f")) # Ensure indexing with an object that is a different object # also raises an error with pytest.raises(ValueError): test_list.index(Object("test1")) def test_independent() -> None: """Test proper instance creation for dictlist.""" a = DictList([Object("o1"), Object("o2")]) b = DictList() assert "o1" in a assert "o1" not in b b.append(Object("o3")) assert "o3" not in a assert "o3" in b def test_get_by_any(dict_list: Tuple[Object, DictList]) -> None: """Test get_by_any() for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list assert test_list.get_by_any(0) == [obj] assert test_list.get_by_any("test1") == [obj] with pytest.raises(KeyError): test_list.get_by_any("not-in-list") with pytest.raises(TypeError): test_list.get_by_any(1.1) assert test_list.get_by_any(obj) == [obj] def test_append(dict_list: Tuple[Object, DictList]) -> None: """Test append() for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list obj2 = Object("test2") test_list.append(obj2) with pytest.raises(ValueError): test_list.append(Object("test1")) assert test_list.index(obj2) == 1 assert test_list[1] == obj2 assert test_list.get_by_id("test2") is obj2 assert len(test_list) == 2 def test_insert(dict_list: Tuple[Object, DictList]) -> None: """Test insert() for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list obj2 = Object("a") test_list.insert(0, obj2) assert test_list.index(obj2) == 0 assert test_list.index("test1") == 1 assert test_list.get_by_id("a") is obj2 assert len(test_list) == 2 with pytest.raises(ValueError): test_list.append(obj2) def test_extend(dict_list: Tuple[Object, DictList]) -> None: """Test extend() for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list obj_list = [Object(f"test{i:d}") for i in range(2, 10)] test_list.extend(obj_list) assert test_list[1].id == "test2" assert test_list.get_by_id("test2") == obj_list[0] assert test_list[8].id == "test9" assert len(test_list) == 9 with pytest.raises(ValueError): test_list.extend([Object("test1")]) # Even if the object is unique, if it is present twice in the new # list, it should still raise an exception with pytest.raises(ValueError): test_list.extend([Object("testd"), Object("testd")]) def test_iadd(dict_list: Tuple[Object, DictList]) -> None: """Test in-place addition for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list obj_list = [Object(f"test{i:d}") for i in range(2, 10)] test_list += obj_list assert test_list[1].id == "test2" assert test_list.get_by_id("test2") == obj_list[0] assert test_list[8].id == "test9" assert len(test_list) == 9 def test_add(dict_list: Tuple[Object, DictList]) -> None: """Test addition for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list obj_list = [Object(f"test{i:d}") for i in range(2, 10)] sum_ = test_list + obj_list assert sum_ is not test_list assert sum_ is not obj_list assert test_list[0].id == "test1" assert sum_[1].id == "test2" # noinspection PyUnresolvedReferences assert sum_.get_by_id("test2") == obj_list[0] assert sum_[8].id == "test9" assert len(test_list) == 1 assert len(sum_) == 9 def test_sub(dict_list: Tuple[Object, DictList]) -> None: """Test subtraction for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list obj_list = [Object("test%d" % i) for i in range(2, 10)] sum_ = test_list + obj_list sub = sum_ - test_list assert test_list[0].id == "test1" assert sub[0].id == "test2" assert len(sub) == 8 assert sum_ - obj_list == test_list def test_isub(dict_list: Tuple[Object, DictList]) -> None: """Test in-place subtraction for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list obj_list = [Object("test%d" % i) for i in range(2, 10)] sum_ = test_list + obj_list sum_ -= obj_list[2:4] assert len(sum_) == 7 with pytest.raises(ValueError): sum_ -= [Object("bogus")] def test_init_copy(dict_list: Tuple[Object, DictList]) -> None: """Test instance comparison for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list test_list.append(Object("test2")) copied = DictList(test_list) assert test_list is not copied assert isinstance(copied, test_list.__class__) assert len(test_list) == len(copied) for i, v in enumerate(test_list): assert test_list[i].id == copied[i].id assert i == copied.index(v.id) assert test_list[i] is copied[i] assert v is copied.get_by_id(v.id) def test_slice(dict_list: Tuple[Object, DictList]) -> None: """Test slicing for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list test_list.append(Object("test2")) test_list.append(Object("test3")) sliced = test_list[:-1] assert test_list is not sliced assert isinstance(sliced, test_list.__class__) assert len(test_list) == len(sliced) + 1 for i, v in enumerate(sliced): assert test_list[i].id == sliced[i].id assert i == sliced.index(v.id) assert test_list[i] is sliced[i] assert test_list[i] is sliced.get_by_id(v.id) def test_copy(dict_list: Tuple[Object, DictList]) -> None: """Test soft-copy for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list test_list.append(Object("test2")) copied = copy(test_list) assert test_list is not copied assert isinstance(copied, test_list.__class__) assert len(test_list) == len(copied) for i, v in enumerate(test_list): assert test_list[i].id == copied[i].id assert i == copied.index(v.id) assert test_list[i] is copied[i] assert v is copied.get_by_id(v.id) def test_deepcopy(dict_list: Tuple[Object, DictList]) -> None: """Test deep-copy for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list test_list.append(Object("test2")) copied = deepcopy(test_list) assert test_list is not copied assert isinstance(copied, test_list.__class__) assert len(test_list) == len(copied) for i, v in enumerate(test_list): assert test_list[i].id == copied[i].id assert i == copied.index(v.id) assert test_list[i] is not copied[i] assert v is not copied.get_by_id(v.id) def test_pickle(dict_list: Tuple[Object, DictList]) -> None: """Test pickling for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list test_list.append(Object("test2")) for protocol in range(HIGHEST_PROTOCOL): pickle_str = dumps(test_list, protocol=protocol) copied = loads(pickle_str) assert test_list is not copied assert isinstance(copied, test_list.__class__) assert len(test_list) == len(copied) for i, v in enumerate(test_list): assert test_list[i].id == copied[i].id assert i == copied.index(v.id) assert test_list[i] is not copied[i] assert v is not copied.get_by_id(v.id) def test_query(dict_list: Tuple[Object, DictList]) -> None: """Test query() for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list obj2 = Object("test2") obj2.name = "foobar1" test_list.append(obj2) result = test_list.query("test1") # matches only test1 assert len(result) == 1 result = test_list.query("test1", "id") assert len(result) == 1 assert result[0] == obj result = test_list.query("foo", "name") # matches only test2 assert len(result) == 1 assert result[0] == obj2 result = test_list.query("test", "id") # matches test1 and test2 assert len(result) == 2 # Test with a regular expression result = test_list.query(re.compile("test[0-9]"), "id") assert len(result) == 2 result = test_list.query(re.compile("test[29]"), "id") assert len(result) == 1 # Test query of name result = test_list.query(re.compile("foobar."), "name") assert len(result) == 1 # Test query with lambda function result = test_list.query(lambda x: x.id == "test1") assert len(result) == 1 def test_removal() -> None: """Test pop() for dictlist.""" obj_list = DictList(Object(f"test{i:d}") for i in range(2, 10)) del obj_list[3] assert "test5" not in obj_list assert obj_list.index(obj_list[-1]) == len(obj_list) - 1 assert len(obj_list) == 7 del obj_list[3:5] assert "test6" not in obj_list assert "test7" not in obj_list assert obj_list.index(obj_list[-1]) == len(obj_list) - 1 assert len(obj_list) == 5 removed = obj_list.pop(1) assert obj_list.index(obj_list[-1]) == len(obj_list) - 1 assert removed.id == "test3" assert "test3" not in obj_list assert len(obj_list) == 4 removed = obj_list.pop() assert removed.id == "test9" assert removed.id not in obj_list assert len(obj_list) == 3 def test_set() -> None: """Test set item for dictlist.""" obj_list = DictList(Object(f"test{i:d}") for i in range(10)) obj_list[4] = Object("testa") assert obj_list.index("testa") == 4 assert obj_list[4].id == "testa" obj_list[5:7] = [Object("testb"), Object("testc")] assert obj_list.index("testb") == 5 assert obj_list[5].id == "testb" assert obj_list.index("testc") == 6 assert obj_list[6].id == "testc" # Even if the object is unique, if it is present twice in the new # list, it should still raise an exception with pytest.raises(ValueError): obj_list.__setitem__(slice(5, 7), [Object("testd"), Object("testd")]) def test_sort_and_reverse() -> None: """Test sort() and reverse() for dictlist.""" dl = DictList(Object(f"test{i:d}") for i in reversed(range(10))) assert dl[0].id == "test9" dl.sort() assert len(dl) == 10 assert dl[0].id == "test0" assert dl.index("test0") == 0 dl.reverse() assert dl[0].id == "test9" assert dl.index("test0") == 9 def test_dir(dict_list: Tuple[Object, DictList]) -> None: """Test local scope item listings for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list # Make sure tab completion works attrs = dir(test_list) assert "test1" in attrs assert "_dict" in attrs # attribute of DictList def test_union(dict_list: Tuple[Object, DictList]) -> None: """Test union() for dictlist. Parameters ---------- dict_list : tuple The fixture for filled dictlist. """ obj, test_list = dict_list test_list.union([Object("test1"), Object("test2")]) # Add only 1 element assert len(test_list) == 2 assert test_list.index("test2") == 1 cobrapy-0.29.1/tests/test_core/test_formula.py000066400000000000000000000013331467301562300214600ustar00rootroot00000000000000"""Test functions of formula.py .""" import pytest from cobra.core.formula import Formula def test_formula_init() -> None: """Test initialization.""" f = Formula("H2O") assert "Formula H2O" in repr(f) f = Formula("H2O", name="Water") assert f.name == "Water" @pytest.mark.parametrize( ["formula", "weight"], [["H2O", 18.01528], ["C6H12O6", 180.15588], ["NO3", 62.0049]] ) def test_formula_weight(formula, weight) -> None: """Test molecular weight calculation.""" assert Formula(formula).weight == pytest.approx(weight) def test_formula_wrong() -> None: """Test incorrect formula elements.""" with pytest.warns(UserWarning): w = Formula("NOX").weight assert w is None cobrapy-0.29.1/tests/test_core/test_gene.py000066400000000000000000000004641467301562300207350ustar00rootroot00000000000000"""Test functions of gene.py .""" from cobra.core import Model def test_repr_html_(model: Model) -> None: """Test HTML represenation is correct for a gene. Parameters ---------- model : cobra.Model The textbook model. """ assert "
" in model.genes[0]._repr_html_() cobrapy-0.29.1/tests/test_core/test_gpr.py000066400000000000000000000346661467301562300206220ustar00rootroot00000000000000"""Test functions of cobra.core.gene.GPR .""" import itertools from ast import parse as ast_parse from typing import Dict, Iterable, Iterator, List, Set, Tuple, Union import pytest from sympy.core.expr import Expr from sympy.core.symbol import Symbol from sympy.logic import And, Or from sympy.logic.boolalg import BooleanFunction from cobra.core.gene import GPR, ast2str, eval_gpr, parse_gpr from cobra.core.model import Model def test_gpr() -> None: """Test GPR instance creation and basic usage.""" gpr1 = GPR() assert len(gpr1.genes) == 0 gpr1.update_genes() assert len(gpr1.genes) == 0 assert gpr1.to_string() == "" assert gpr1.eval() gpr2 = gpr1.copy() assert isinstance(gpr2, GPR) assert len(gpr1.genes) == 0 @pytest.mark.parametrize("test_input", ["", "", None]) def test_empty_gpr(test_input) -> None: """Test empty GPR.""" gpr1 = GPR(test_input) assert not gpr1.body assert len(gpr1.genes) == 0 gpr1.update_genes() assert len(gpr1.genes) == 0 assert gpr1.to_string() == "" assert gpr1.eval() assert len(gpr1.genes) == 0 def test_one_gene_gpr() -> None: """Test single gene GPR.""" gpr1 = GPR.from_string("a") assert len(gpr1.genes) == 1 gpr1.update_genes() assert len(gpr1.genes) == 1 assert gpr1.genes == {"a"} assert gpr1.to_string() == "a" assert gpr1.eval() assert len(gpr1.genes) == 1 def powerset_ne(iterable: Iterable[str]) -> Iterator[Tuple[str, ...]]: """Get all combinations of an iterable except the empty list. Gets an iterable of all combinations of genes except the empty list. Used to evaluate AND gprs powerset_ne([1,2,3]) --> (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3) """ s = list(iterable) return itertools.chain.from_iterable( itertools.combinations(s, r) for r in range(1, len(s) + 1) ) @pytest.mark.parametrize( "gpr_input, num_genes, gpr_genes, gpr_output_string", [ ("a & b", 2, {"a", "b"}, "a and b"), ("a and b", 2, {"a", "b"}, "a and b"), pytest.param( "a AND b", 2, {"a", "b"}, "a and b", marks=pytest.mark.filterwarnings ), ("a & b & c", 3, {"a", "b", "c"}, "(a and b) and c"), ("a and b and c", 3, {"a", "b", "c"}, "a and b and c"), ], ) def test_and_gpr(gpr_input, num_genes, gpr_genes, gpr_output_string) -> None: """Test 'and' GPR.""" gpr1 = GPR.from_string(gpr_input) assert len(gpr1.genes) == num_genes gpr1.update_genes() assert len(gpr1.genes) == num_genes assert gpr1.genes == gpr_genes assert gpr1.to_string() == gpr_output_string assert gpr1.eval() for ko_genes in powerset_ne(gpr_genes): assert not gpr1.eval(ko_genes) assert gpr1.body gpr1.copy() def all_except_one(iterable: Iterable[str]) -> Iterator[Tuple[str, ...]]: """Generate all combinations from an iterable, while leaving one out. Gets an iterable of all combinations of genes except the complete list and the empty list. Used to evaluate OR gprs all_except_one([1,2,3]) --> (1,) (2,) (3,) (1,2) (1,3) (2,3) """ s = list(iterable) return itertools.chain.from_iterable( itertools.combinations(s, r) for r in range(1, len(s)) ) @pytest.mark.parametrize( "gpr_input, num_genes, gpr_genes, gpr_output_string", [ ("a | b", 2, {"a", "b"}, "a or b"), ("a or b", 2, {"a", "b"}, "a or b"), pytest.param( "a OR b", 2, {"a", "b"}, "a or b", marks=pytest.mark.filterwarnings ), ("a | b | c", 3, {"a", "b", "c"}, "(a or b) or c"), ("a or b or c", 3, {"a", "b", "c"}, "a or b or c"), ], ) def test_or_gpr( gpr_input: str, num_genes: int, gpr_genes: Set, gpr_output_string: str ) -> None: """Test 'or' GPR.""" gpr1 = GPR.from_string(gpr_input) assert len(gpr1.genes) == num_genes gpr1.update_genes() assert len(gpr1.genes) == num_genes assert gpr1.genes == gpr_genes assert gpr1.to_string() == gpr_output_string assert gpr1.eval() for ko_genes in all_except_one(gpr_genes): assert gpr1.eval(ko_genes) assert not gpr1.eval(gpr_genes) assert gpr1.body gpr1.copy() @pytest.mark.parametrize( "gpr_input", [ "(a | b) & c", "(a or b) and c", pytest.param("(a OR b) AND c", marks=pytest.mark.filterwarnings), ], ) def test_complicated_gpr(gpr_input: str) -> None: """Test complicated GPR.""" gpr1 = GPR.from_string(gpr_input) assert len(gpr1.genes) == 3 gpr1.update_genes() assert len(gpr1.genes) == 3 assert gpr1.to_string() == "(a or b) and c" assert gpr1.eval() assert gpr1.body assert gpr1.eval() assert gpr1.eval("a") assert gpr1.eval("b") assert not gpr1.eval("c") assert not gpr1.eval(["a", "b"]) assert not gpr1.eval(["a", "b", "c"]) gpr1.copy() @pytest.mark.parametrize( "string_to_ast, num_genes, gpr_genes, gpr_output_string", [ ("a", 1, {"a"}, "a"), ("a | b", 2, {"a", "b"}, "a or b"), ("a or b", 2, {"a", "b"}, "a or b"), pytest.param("a OR b", 2, {"a", "b"}, "a or b", marks=pytest.mark.xfail), ], ) def test_gpr_from_ast_or( string_to_ast: str, num_genes: int, gpr_genes: set, gpr_output_string: str ) -> None: """Test GPR from AST 'or'.""" ast_tree = ast_parse(string_to_ast, "", "eval") gpr1 = GPR(ast_tree) assert len(gpr1.genes) == num_genes gpr1.update_genes() assert len(gpr1.genes) == num_genes assert gpr1.genes == gpr_genes assert gpr1.to_string() == gpr_output_string assert gpr1.eval() for ko_genes in all_except_one(gpr_genes): assert gpr1.eval(ko_genes) assert not gpr1.eval(gpr_genes) gpr1.copy() @pytest.mark.parametrize( "string_to_ast, num_genes, gpr_genes, gpr_output_string", [ ("a & b", 2, {"a", "b"}, "a and b"), ("a and b", 2, {"a", "b"}, "a and b"), pytest.param("a AND b", 2, {"a", "b"}, "a and b", marks=pytest.mark.xfail), ], ) def test_gpr_from_ast_and( string_to_ast: str, num_genes: int, gpr_genes: set, gpr_output_string: str ) -> None: """Test GPR from AST 'and'.""" ast_tree = ast_parse(string_to_ast, "", "eval") gpr1 = GPR(ast_tree) assert len(gpr1.genes) == num_genes gpr1.update_genes() assert len(gpr1.genes) == num_genes assert gpr1.genes == gpr_genes assert gpr1.to_string() == gpr_output_string assert gpr1.eval() for ko_genes in powerset_ne(gpr_genes): assert not gpr1.eval(ko_genes) gpr1.copy() @pytest.mark.parametrize("test_input", [["a", "b"], {"a", "b"}]) def test_wrong_input_gpr_error(test_input: Union[list, set]) -> None: """Test error for incorrect GPR input.""" with pytest.raises(TypeError): GPR.from_string(test_input) with pytest.raises(TypeError): GPR(test_input) @pytest.mark.parametrize("test_input", ["a |", "a &", "a and ()", "a or ()"]) def test_wrong_input_gpr_warning(test_input: str) -> None: """Test warning for incorrect GPR input.""" with pytest.warns(SyntaxWarning): gpr1 = GPR.from_string(test_input) assert gpr1.body is None assert len(gpr1.genes) == 0 def test_gpr_that_needs_two_replacements() -> None: """Test GPR with multi replacements.""" gpr1 = GPR.from_string( "(591001.3.peg.1891 AND 591001.3.peg.1892 AND 591001.3.peg.1893)" ) assert len(gpr1.genes) == 3 assert "591001.3.peg.1891" in gpr1.genes assert "591001.3.peg.1892" in gpr1.genes assert "591001.3.peg.1893" in gpr1.genes def test_deprecated_gpr() -> None: """Test deprecated GPR.""" gpr1 = GPR.from_string("(a | b) & c") with pytest.deprecated_call(): assert ast2str(gpr1) == "(a or b) and c" with pytest.deprecated_call(): assert eval_gpr(gpr1, set()) with pytest.deprecated_call(): assert eval_gpr(gpr1, {"a"}) with pytest.deprecated_call(): assert eval_gpr(gpr1, {"b"}) with pytest.deprecated_call(): assert not eval_gpr(gpr1, {"c"}) with pytest.deprecated_call(): gpr1, genes = parse_gpr("(a | b) & c") assert genes == {"a", "b", "c"} with pytest.deprecated_call(): assert ast2str(gpr1) == "(a or b) and c" with pytest.deprecated_call(): assert eval_gpr(gpr1, set()) with pytest.deprecated_call(): assert eval_gpr(gpr1, {"a"}) with pytest.deprecated_call(): assert eval_gpr(gpr1, {"b"}) with pytest.deprecated_call(): assert not eval_gpr(gpr1, {"c"}) def test_gpr_as_symbolic() -> None: """Test GPR as symbolic expression.""" gpr1 = GPR() assert gpr1.as_symbolic() == Symbol("") gpr1 = GPR.from_string("") assert gpr1.as_symbolic() == Symbol("") gpr1 = GPR.from_string("a") assert isinstance(gpr1.as_symbolic(), Symbol) assert gpr1.as_symbolic() == Symbol("a") gpr2 = GPR.from_string("a & b") assert isinstance(gpr2.as_symbolic(), BooleanFunction) assert gpr2.as_symbolic() == And(Symbol("a"), Symbol("b")) assert gpr1 != gpr2 @pytest.mark.parametrize( "gpr_input, symbolic_gpr", [ ("a | b", Or(Symbol("a"), Symbol("b"))), ("a or b", Or(Symbol("a"), Symbol("b"))), pytest.param( "a OR b", Or(Symbol("a"), Symbol("b")), marks=pytest.mark.filterwarnings ), ("a | b", Or(Symbol("b"), Symbol("a"))), ("a | b | c", Or(Symbol("a"), Symbol("b"), Symbol("c"))), ("a or b or c", Or(Symbol("a"), Symbol("b"), Symbol("c"))), ("(a OR b) AND c", And(Symbol("c"), Or(Symbol("a"), Symbol("b")))), ], ) def test_gpr_as_symbolic_boolean(gpr_input: str, symbolic_gpr: Expr) -> None: """Test GPR as symbolic boolean expression.""" gpr1 = GPR().from_string(gpr_input) assert isinstance(gpr1.as_symbolic(), BooleanFunction) assert gpr1.as_symbolic() == symbolic_gpr if "OR" not in gpr_input and "AND" not in gpr_input: ast_tree = ast_parse(gpr_input, "", "eval") gpr1 = GPR(ast_tree) assert isinstance(gpr1.as_symbolic(), BooleanFunction) assert gpr1.as_symbolic() == symbolic_gpr def test_gpr_equality() -> None: """Test GPR equality.""" assert GPR() == GPR() assert GPR() == GPR.from_string("") assert GPR() != GPR.from_string("a") assert GPR.from_string("a") == GPR.from_string("a") gpr_str_lists = { "a_and_b_strs": ["a & b", "a and b", "a AND b", "b & a", "b and a", "b AND a"], "a_or_b_strs": ["a | b", "a or b", "a OR b", "b | a", "b or a", "b OR a"], "a_b_c_or_strs": [ "a | b | c", "a or b or c", "a OR b or c", "(a or b) or c", "b | a | c", "b or a or c", "b | c | a", "b or c | a", "b or c or a", "c or a or b", "c | a or b", "c or a | b", "c or b or a", "c | b or a", "c or b | a", "c | b | a", ], "a_b_c_and_strs": [ "a & b & c", "a and b and c", "a AND b and c", "(a and b) and c", "b & a & c", "b and a and c", "b & c & a", "b and c & a", "b and c and a", "c and a and b", "c & a and b", "c and a & b", "c and b and a", "c & b and a", "c and b & a", "c & b & a", ], "a_b_c_or_and_strs": [ "(a OR b) AND c", "(a | b) & c", "(a & c) | b & c", "c & (a | b)", ], } @pytest.fixture(params=list(gpr_str_lists.keys())) def gpr_list(request: pytest.FixtureRequest) -> List[str]: """Provide fixture for GPR list.""" gpr_str_list = gpr_str_lists[request.param] return gpr_str_list def test_gpr_equality_with_bolean_logic(gpr_list: List[str]) -> None: """Test GPR equality with boolean logic.""" for i in range(len(gpr_list)): for j in range(i + 1, len(gpr_list)): assert GPR().from_string(gpr_list[i]) == GPR.from_string(gpr_list[j]) @pytest.fixture(params=list(itertools.combinations(gpr_str_lists.keys(), 2))) def gpr_lists(request: pytest.FixtureRequest) -> Dict[str, List[str]]: """Provide fixture for GPR dictionary.""" gpr_dict = dict() gpr_dict["gpr1"] = gpr_str_lists[request.param[0]] gpr_dict["gpr2"] = gpr_str_lists[request.param[1]] return gpr_dict def test_gpr_inequality_boolean(gpr_lists) -> None: """Test GPR inequality for boolean expression.""" gpr_list1 = gpr_lists["gpr1"] gpr_list2 = gpr_lists["gpr2"] for i in range(len(gpr_list1)): for j in range(len(gpr_list2)): assert GPR.from_string(gpr_list1[i]) != GPR.from_string(gpr_list2[j]) def test_gpr_symbolism_benchmark(large_model: Model, benchmark) -> None: """Benchmark as symbolic time.""" model = large_model.copy() def gpr_symbolic(): for i in range(len(model.reactions)): rxn1 = model.reactions[i] gpr1 = rxn1.gpr gpr1.as_symbolic() benchmark(gpr_symbolic) def test_gpr_equality_benchmark(model: Model, benchmark) -> None: """Benchmark equality of GPR using the mini model.""" model2 = model.copy() def gpr_equality_all_reactions(): for i in range(len(model.reactions)): rxn1 = model.reactions[i] rxn2 = model2.reactions[i] assert rxn1.gpr == rxn2.gpr benchmark(gpr_equality_all_reactions) @pytest.mark.parametrize( "gpr_input, symbolic_gpr", [ ("", Symbol("")), ("a", Symbol("a")), ("a & b", And(Symbol("a"), Symbol("b"))), ("a | b", Or(Symbol("a"), Symbol("b"))), ("a or b", Or(Symbol("a"), Symbol("b"))), pytest.param( "a OR b", Or(Symbol("a"), Symbol("b")), marks=pytest.mark.filterwarnings ), ("a | b", Or(Symbol("b"), Symbol("a"))), ("a | b | c", Or(Symbol("a"), Symbol("b"), Symbol("c"))), ("a or b or c", Or(Symbol("a"), Symbol("b"), Symbol("c"))), ("(a OR b) AND c", And(Symbol("c"), Or(Symbol("a"), Symbol("b")))), ], ) def test_gpr_from_symbolic(gpr_input: str, symbolic_gpr: Expr) -> None: """Test GPR creation from symbolic and from string is equal.""" gpr1 = GPR().from_symbolic(symbolic_gpr) gpr2 = GPR().from_string(gpr_input) assert gpr1 == gpr2 def test_gpr_from_as_symbolic_equality(large_model) -> None: """Test as_symbolic followed by from_symbolic gives a GPR equivalent to original.""" model = large_model.copy() for i in range(len(model.reactions)): rxn1 = model.reactions[i] gpr1 = rxn1.gpr gpr2 = GPR().from_symbolic(gpr1.as_symbolic()) assert gpr1 == gpr2 cobrapy-0.29.1/tests/test_core/test_group.py000066400000000000000000000022041467301562300211450ustar00rootroot00000000000000"""Test functions of group via model.py.""" import pytest from cobra import Model from cobra.core import Group def test_group_add_elements(model: Model) -> None: """Test adding elements to a group.""" num_members = 5 reactions_for_group = model.reactions[0:num_members] group = Group("arbitrary_group1") group.add_members(reactions_for_group) group.kind = "collection" # number of member sin group should equal the number of reactions # assigned to the group assert len(group.members) == num_members # Choose an overlapping, larger subset of reactions for the group num_total_members = 12 reactions_for_larger_group = model.reactions[0:num_total_members] group.add_members(reactions_for_larger_group) assert len(group.members) == num_total_members def test_group_kind() -> None: """Test SBML compliance and group kind.""" group = Group("arbitrary_group1") with pytest.raises(ValueError) as excinfo: group.kind = "non-SBML compliant group kind" assert "Kind can only by one of:" in str(excinfo.value) group.kind = "collection" assert group.kind == "collection" cobrapy-0.29.1/tests/test_core/test_metabolite.py000066400000000000000000000034111467301562300221370ustar00rootroot00000000000000"""Test functions of metabolite.py.""" from typing import TYPE_CHECKING, Tuple import pytest from cobra.core import Metabolite if TYPE_CHECKING: from cobra import Model, Solution def test_metabolite_formula() -> None: """Test metabolite formula correctly leads to element dictionary.""" met = Metabolite("water") met.formula = "H2O" assert met.elements == {"H": 2, "O": 1} assert met.formula_weight == 18.01528 def test_formula_element_setting(model: "Model") -> None: """Test that formula and elements set each other when one is set.""" met = model.metabolites[1] orig_formula = str(met.formula) orig_elements = dict(met.elements) met.formula = "" assert met.elements == {} met.elements = orig_elements assert met.formula == orig_formula def test_set_id(solved_model: Tuple["Solution", "Model"]) -> None: """Test that setting id leads to change in the model metabolite dictlist.""" solution, model = solved_model met = Metabolite("test") with pytest.raises(TypeError): met.id = 1 model.add_metabolites([met]) with pytest.raises(ValueError): met.id = "g6p_c" met.id = "test2" assert "test2" in model.metabolites assert "test" not in model.metabolites def test_remove_from_model(solved_model: Tuple["Solution", "Model"]) -> None: """Test that removing a model removes it from relevant model dictlists.""" solution, model = solved_model met = model.metabolites.get_by_id("g6p_c") met.remove_from_model() assert not (met.id in model.metabolites) assert not (met.id in model.constraints) def test_repr_html_(model: "Model") -> None: """Test HTML represenation is correct for a metabolite.""" assert "
" in model.metabolites.h2o_c._repr_html_() cobrapy-0.29.1/tests/test_core/test_model.py000066400000000000000000001244051467301562300211210ustar00rootroot00000000000000"""Test functions of model.py.""" import os import warnings from copy import copy, deepcopy from math import isnan from typing import List, Tuple import numpy as np import pandas as pd import pytest from optlang.symbolics import Expr, Zero from cobra import Solution from cobra.core import Group, Metabolite, Model, Reaction from cobra.exceptions import OptimizationError from cobra.manipulation.delete import remove_genes from cobra.util import solver as su from cobra.util.solver import SolverNotFound, set_objective, solvers try: import pytest_benchmark except ImportError: pytest_benchmark = None if pytest_benchmark: from pytest_benchmark.fixture import BenchmarkFixture stable_optlang = ["glpk", "cplex", "gurobi"] optlang_solvers = ["optlang-" + s for s in stable_optlang if s in su.solvers] def same_ex(ex1: Expr, ex2: Expr) -> bool: """Compare two sympy-expressions for mathematical equality. Parameters ---------- ex1 : optlang.symbolics.Expr The first sympy-expression. ex2 : optlang.symbolics.Expr The second sympy-expression. Returns ------- bool Whether the two expression are mathematically equal. """ return ex1.simplify() == ex2.simplify() def test_add_metabolite(model: Model) -> None: """Tests adding a metabolite to a model, including with context. Parameters ---------- model: cobra.Model """ new_metabolite = Metabolite("test_met") assert new_metabolite not in model.metabolites with model: model.add_metabolites(new_metabolite) assert new_metabolite._model == model assert new_metabolite in model.metabolites assert new_metabolite.id in model.solver.constraints assert new_metabolite._model is None assert new_metabolite not in model.metabolites assert new_metabolite.id not in model.solver.constraints def test_remove_metabolite_subtractive(model: Model) -> None: """Remove metabolite from model in a subtractive (not destructive) way. Checks that the changes to model are reversed when using context. Parameters ---------- model: cobra.Model """ test_metabolite = model.metabolites[4] test_reactions = test_metabolite.reactions with model: model.remove_metabolites(test_metabolite, destructive=False) assert test_metabolite._model is None assert test_metabolite not in model.metabolites assert test_metabolite.id not in model.solver.constraints for reaction in test_reactions: assert reaction in model.reactions assert test_metabolite._model is model assert test_metabolite in model.metabolites assert test_metabolite.id in model.solver.constraints def test_remove_metabolite_destructive(model: Model) -> None: """Remove metabolite from a model in a destructive way. Checks that the changes to model are reversed when using context. Parameters ---------- model: cobra.Model """ test_metabolite = model.metabolites[4] test_reactions = test_metabolite.reactions with model: model.remove_metabolites(test_metabolite, destructive=True) assert test_metabolite._model is None assert test_metabolite not in model.metabolites assert test_metabolite.id not in model.solver.constraints for reaction in test_reactions: assert reaction not in model.reactions assert test_metabolite._model is model assert test_metabolite in model.metabolites assert test_metabolite.id in model.solver.constraints for reaction in test_reactions: assert reaction in model.reactions def test_compartments(model: Model) -> None: """Test setting and modifying model compartments. Parameters ---------- model: cobra.Model """ assert set(model.compartments) == {"c", "e"} model = Model("test", "test") met_c = Metabolite("a_c", compartment="c") met_e = Metabolite("a_e", compartment="e") rxn = Reaction("foo") rxn.add_metabolites({met_e: -1, met_c: 1}) model.add_reactions([rxn]) assert model.compartments == {"c": "", "e": ""} model.compartments = {"c": "cytosol"} assert model.compartments == {"c": "cytosol", "e": ""} def test_model_remove_reaction(model: Model) -> None: """Test remove_reactions() to remove reaction(s). Parameters ---------- model: cobra.Model """ old_reaction_count = len(model.reactions) with model: model.remove_reactions(["PGI"]) assert len(model.reactions) == old_reaction_count - 1 with pytest.raises(KeyError): model.reactions.get_by_id("PGI") model.remove_reactions(model.reactions[:1]) assert len(model.reactions) == old_reaction_count - 2 assert len(model.reactions) == old_reaction_count assert "PGI" in model.reactions tmp_metabolite = Metabolite("testing") model.reactions[0].add_metabolites({tmp_metabolite: 1}) assert tmp_metabolite in model.metabolites model.remove_reactions(model.reactions[:1], remove_orphans=True) assert tmp_metabolite not in model.metabolites with model: model.reactions[0].add_metabolites({tmp_metabolite: 1}) assert tmp_metabolite in model.metabolites assert tmp_metabolite not in model.metabolites biomass_before = model.slim_optimize() with model: model.remove_reactions([model.reactions.Biomass_Ecoli_core]) assert np.isclose(model.slim_optimize(), 0) assert np.isclose(model.slim_optimize(), biomass_before) def test_reaction_remove(model: Model) -> None: """Test remove orphans in Reaction().remove_from_model. This function test that remove_orphans=True removes related metabolites when supposed to and doesn't remove when not supposed to (when this metabolite is in reactions not removed). Parameters ---------- model: cobra.Model to use """ old_reaction_count = len(model.reactions) tmp_metabolite = Metabolite("testing") # Delete without removing orphan model.reactions[0].add_metabolites({tmp_metabolite: 1}) assert len(tmp_metabolite.reactions) == 1 # Esnsure the stoichiometry is still the same using different objects removed_reaction = model.reactions[0] original_stoich = { i.id: value for i, value in removed_reaction._metabolites.items() } model.reactions[0].remove_from_model(remove_orphans=False) assert len(original_stoich) == len(removed_reaction._metabolites) for met in removed_reaction._metabolites: assert original_stoich[met.id] == removed_reaction._metabolites[met] assert met is not model.metabolites # Make sure it's still in the model assert tmp_metabolite in model.metabolites assert len(tmp_metabolite.reactions) == 0 assert len(model.reactions) == old_reaction_count - 1 # Now try with removing orphans model.reactions[0].add_metabolites({tmp_metabolite: 1}) assert len(tmp_metabolite.reactions) == 1 model.reactions[0].remove_from_model(remove_orphans=True) assert tmp_metabolite not in model.metabolites assert len(tmp_metabolite.reactions) == 0 assert len(model.reactions) == old_reaction_count - 2 # It shouldn't remove orphans if it's in 2 reactions however model.reactions[0].add_metabolites({tmp_metabolite: 1}) model.reactions[1].add_metabolites({tmp_metabolite: 1}) assert len(tmp_metabolite.reactions) == 2 model.reactions[0].remove_from_model(remove_orphans=False) assert tmp_metabolite in model.metabolites assert len(tmp_metabolite.reactions) == 1 assert len(model.reactions) == old_reaction_count - 3 def test_reaction_delete(model: Model) -> None: """Test reaction removal using the Reaction.delete() function. This function calls Reaction().remove_from_model, since it is deprecated. Parameters ---------- model: cobra.Model to use """ old_reaction_count = len(model.reactions) tmp_metabolite = Metabolite("testing") # Delete without removing orphan model.reactions[0].add_metabolites({tmp_metabolite: 1}) assert len(tmp_metabolite.reactions) == 1 with pytest.warns(DeprecationWarning): model.reactions[0].delete(remove_orphans=False) # Make sure it's still in the model assert tmp_metabolite in model.metabolites assert len(tmp_metabolite.reactions) == 0 assert len(model.reactions) == old_reaction_count - 1 # Now try it with removing orphans model.reactions[0].add_metabolites({tmp_metabolite: 1}) assert len(tmp_metabolite.reactions) == 1 model.reactions[0].delete(remove_orphans=True) assert tmp_metabolite not in model.metabolites assert len(tmp_metabolite.reactions) == 0 assert len(model.reactions) == old_reaction_count - 2 # It shouldn't remove orphans if it's in 2 reactions however model.reactions[0].add_metabolites({tmp_metabolite: 1}) model.reactions[1].add_metabolites({tmp_metabolite: 1}) assert len(tmp_metabolite.reactions) == 2 model.reactions[0].delete(remove_orphans=False) assert tmp_metabolite in model.metabolites assert len(tmp_metabolite.reactions) == 1 assert len(model.reactions) == old_reaction_count - 3 def test_remove_gene(model: Model) -> None: """Test remove_gene from model. Parameters ---------- model: cobra.Model """ target_gene = model.genes[0] gene_reactions = list(target_gene.reactions) with warnings.catch_warnings(): warnings.simplefilter("ignore") remove_genes(model, [target_gene]) assert target_gene.model is None # Make sure the reaction was removed from the model assert target_gene not in model.genes # Ensure the old reactions no longer have a record of the gene for reaction in gene_reactions: assert target_gene not in reaction.genes def test_group_model_reaction_association(model: Model) -> None: """Test associating reactions with group in a model. This function will also remove the group from the model and check that reactions are no longer associated with the group Parameters ---------- model: cobra.Model """ num_members = 5 reactions_for_group = model.reactions[0:num_members] group = Group("arbitrary_group1") group.add_members(reactions_for_group) group.kind = "collection" model.add_groups([group]) # group should point to and be associated with the model assert group._model is model assert group in model.groups # model.get_associated_groups should find the group for each reaction # we added to the group for reaction in reactions_for_group: assert group in model.get_associated_groups(reaction) # remove the group from the model model.remove_groups([group]) assert group not in model.groups assert group._model is not model for reaction in reactions_for_group: assert group not in model.get_associated_groups(reaction) def test_group_members_add_to_model(model: Model) -> None: """Test adding a group with reactions to a model. This function will remove some reactions from a model, add them to a group, and test that the reactions aren't in the model. Later, the test will add the group to the model, and check the reactions were added to the model. Parameters ---------- model: cobra.Model """ # remove a few reactions from the model and add them to a new group num_members = 5 reactions_for_group = model.reactions[0:num_members] model.remove_reactions(reactions_for_group, remove_orphans=False) group = Group("arbitrary_group1") group.add_members(reactions_for_group) group.kind = "collection" # the old reactions should not be in the model for reaction in reactions_for_group: assert reaction not in model.reactions # add the group to the model and check that the reactions were added model.add_groups([group]) assert group in model.groups for reaction in reactions_for_group: assert reaction in model.reactions def test_group_loss_of_elements(model: Model) -> None: """Test removal from model removes elements from group. This function will test that when a metabolite, reaction or gene is removed from a model, it no longer is a member of any groups. Parameters ---------- model: cobra.Model """ num_members_each = 5 elements_for_group = model.reactions[0:num_members_each] elements_for_group.extend(model.metabolites[0:num_members_each]) elements_for_group.extend(model.genes[0:num_members_each]) group = Group("arbitrary_group1") group.add_members(elements_for_group) group.kind = "collection" model.add_groups([group]) remove_met = model.metabolites[0] model.remove_metabolites([remove_met]) remove_rxn = model.reactions[0] model.remove_reactions([remove_rxn]) remove_gene = model.genes[0] remove_genes(model, [remove_gene]) assert remove_met not in group.members assert remove_rxn not in group.members assert remove_gene not in group.members def test_exchange_reactions(model: Model) -> None: """Test model.exchanges works as intended. Parameters ---------- model: cobra.Model """ assert set(model.exchanges) == { rxn for rxn in model.reactions if rxn.id.startswith("EX") } @pytest.mark.parametrize( "metabolites, reaction_type, prefix", [ ("exchange", "exchange", "EX_"), ("demand", "demand", "DM_"), ("sink", "sink", "SK_"), ], indirect=["metabolites"], ) def test_add_boundary( model: Model, metabolites: List[Metabolite], reaction_type: str, prefix: str ) -> None: """Test add_boundary() function for model. Parameters ---------- model: cobra.Model metabolites: List[Metabolites] This list is generated by the pytest.fixture metabolites(), see conftest.py in the root test directory. reaction_type: {"exchange", "demand", "sink"} The allowed types for boundary, see add_boundary() for types. prefix: str """ for metabolite in metabolites: reaction = model.add_boundary(metabolite, reaction_type) assert model.reactions.get_by_id(reaction.id) == reaction assert reaction.reactants == [metabolite] assert model.constraints[metabolite.id].expression.has( model.variables[prefix + metabolite.id] ) @pytest.mark.parametrize( "metabolites, reaction_type, prefix", [ ("exchange", "exchange", "EX_"), ("demand", "demand", "DM_"), ("sink", "sink", "SK_"), ], indirect=["metabolites"], ) def test_add_boundary_context( model: Model, metabolites: List[Metabolite], reaction_type: str, prefix: str ) -> None: """Test add_boundary() function for model with context. Parameters ---------- model: cobra.Model metabolites: List[Metabolites] This list is generated by the pytest.fixture metabolites(), see conftest.py in the root test directory. reaction_type: {"exchange", "demand", "sink"} The allowed types for boundary, see add_boundary() for types. prefix: str """ with model: for metabolite in metabolites: reaction = model.add_boundary(metabolite, reaction_type) assert model.reactions.get_by_id(reaction.id) == reaction assert reaction.reactants == [metabolite] assert -model.constraints[metabolite.id].expression.has( model.variables[prefix + metabolite.id] ) for metabolite in metabolites: assert prefix + metabolite.id not in model.reactions assert prefix + metabolite.id not in model.variables.keys() @pytest.mark.parametrize( "metabolites, reaction_type", [("exchange", "exchange"), ("demand", "demand"), ("sink", "sink")], indirect=["metabolites"], ) def test_add_existing_boundary( model: Model, metabolites: List[Metabolite], reaction_type: str ) -> None: """Test add_boundary() function for model with existing boundary/metabolite. Parameters ---------- model: cobra.Model metabolites: List[Metabolites] This list is generated by the pytest.fixture metabolites(), see conftest.py in the root test directory. reaction_type: {"exchange", "demand", "sink"} The allowed types for boundary, see add_boundary() for types. """ for metabolite in metabolites: rxn_added = model.add_boundary(metabolite, reaction_type) rxn_dup = model.add_boundary(metabolite, reaction_type) assert rxn_dup is rxn_added @pytest.mark.parametrize("solver", optlang_solvers) def test_copy_benchmark(model: Model, solver: str, benchmark: BenchmarkFixture) -> None: """Test copying a model with benchmark. Parameters ---------- model: cobra.Model solver: str It is a string representing which solver to use. Parametized using 'optlang_solvers' defined above. benchmark: BenchmarkFixture """ def _() -> None: """Copy a model. If the model has no solver, it creates the problem using the solver given as parameter to the external function. """ model.solver = solver model.copy() benchmark(_) @pytest.mark.parametrize("solver", optlang_solvers) def test_copy_benchmark_large_model( large_model: Model, solver: str, benchmark: BenchmarkFixture, ) -> None: """Test copying a large model with benchmark. Parameters ---------- large_model: cobra.Model solver: str It is a string representing which solver to use. Parametized using 'optlang_solvers' defined above. benchmark: BenchmarkFixture """ def _() -> None: """Copy a large model. If the model has no solver, it creates the problem using the solver given as parameter to the external function. """ large_model.solver = solver large_model.copy() benchmark(_) def test_copy(model: Model) -> None: """Test copying a model and modifying the copy. This function tests that modifying the copy should not modifying the original, by deleting reactions in the copy (# of reactions in the original should not change). This function also tests that GPRs are copied by content, not by reference, and that the model copy does not copy the context. Parameters ---------- model: cobra.Model """ # Deleting reactions in copy does not change number of reactions in the original model_copy = model.copy() old_reaction_count = len(model.reactions) assert model_copy.notes is not model.notes assert model_copy.annotation is not model.annotation assert len(model.reactions) == len(model_copy.reactions) assert len(model.metabolites) == len(model_copy.metabolites) assert len(model.groups) == len(model_copy.groups) assert len(model.genes) == len(model_copy.genes) # test if GPRs are copied by content but not by reference assert model.reactions[0].gpr == model_copy.reactions[0].gpr assert id(model.reactions[0].gpr.body) != id(model_copy.reactions[0].gpr.body) model_copy.remove_reactions(model_copy.reactions[0:5]) assert old_reaction_count == len(model.reactions) assert len(model.reactions) != len(model_copy.reactions) # Copying a model should not copy its context with model: model.remove_reactions([model.reactions.ACALD]) cp_model = model.copy() assert len(cp_model._contexts) == 0 assert "ACALD" not in cp_model.reactions def test_copy_with_groups(model: Model) -> None: """Copy model with groups and check that groups are copied correctly. Parameters ---------- model: cobra.Model """ sub = Group("pathway", members=[model.reactions.PFK, model.reactions.FBA]) model.add_groups([sub]) model_copy = model.copy() assert len(model_copy.groups) == len(model.groups) assert len(model_copy.groups.get_by_id("pathway")) == len( model.groups.get_by_id("pathway") ) def test_deepcopy_benchmark(model: Model, benchmark: BenchmarkFixture) -> None: """Benchmark deepcopying a model. Parameters ---------- model: cobra.Model benchmark: BenchmarkFixture """ benchmark(deepcopy, model) def test_deepcopy(model: Model) -> None: """Test deepcopying works, and maintains reference structures. Parameters ---------- model: cobra.Model """ # Reference structures are maintained when deepcopying model_copy = deepcopy(model) for gene, gene_copy in zip(model.genes, model_copy.genes): assert gene.id == gene_copy.id reactions = sorted(i.id for i in gene.reactions) reactions_copy = sorted(i.id for i in gene_copy.reactions) assert reactions == reactions_copy for reaction, reaction_copy in zip(model.reactions, model_copy.reactions): assert reaction.id == reaction_copy.id metabolites = sorted(i.id for i in reaction._metabolites) metabolites_copy = sorted(i.id for i in reaction_copy._metabolites) assert metabolites == metabolites_copy def test_add_reaction_orphans(model: Model) -> None: """Test orphan behavior when adding reactions. Need to verify that no orphan genes or metabolites are contained in reactions after adding them to the model. Parameters --------- model: cobra.Model """ model = model.__class__("test") model.add_reactions((x.copy() for x in model.reactions)) genes = [] metabolites = [] for x in model.reactions: genes.extend(x.genes) metabolites.extend(x._metabolites) orphan_genes = [x for x in genes if x.model is not model] orphan_metabolites = [x for x in metabolites if x.model is not model] # Check for dangling genes when running Model.add_reactions assert len(orphan_genes) == 0 # Check for dangling metabolites when running Model.add_reactions assert len(orphan_metabolites) == 0 def test_merge_models(model: Model, tiny_toy_model: Model) -> None: """Test merging models. Parameters ---------- model: cobra.Model tiny_toy_model: cobra.Model """ with model, tiny_toy_model: # Add some cons/vars to tiny_toy_model for testing merging tiny_toy_model.add_reactions([Reaction("EX_glc__D_e")]) variable = tiny_toy_model.problem.Variable("foo") constraint = tiny_toy_model.problem.Constraint( variable, ub=0, lb=0, name="constraint" ) tiny_toy_model.add_cons_vars([variable, constraint]) merged = model.merge( tiny_toy_model, inplace=False, objective="sum", prefix_existing="tiny_" ) assert "ex1" in merged.reactions assert "ex1" not in model.reactions assert merged.reactions.ex1.objective_coefficient == 1 assert ( merged.reactions.get_by_id("Biomass_Ecoli_core").objective_coefficient == 1 ) assert "tiny_EX_glc__D_e" in merged.reactions assert "foo" in merged.variables # Test reversible in-place model merging with model: model.merge( tiny_toy_model, inplace=True, objective="left", prefix_existing="tiny_" ) assert "ex1" in model.reactions assert "constraint" in model.constraints assert "foo" in model.variables assert "tiny_EX_glc__D_e" in model.reactions assert ( model.objective.expression.simplify() == model.reactions.get_by_id( "Biomass_Ecoli_core" ).flux_expression.simplify() ) assert "ex1" not in model.reactions assert "constraint" not in model.constraints assert "foo" not in model.variables assert "tiny_EX_glc__D_e" not in model.reactions @pytest.mark.parametrize("solver", optlang_solvers) def test_change_objective_benchmark( model: Model, benchmark: BenchmarkFixture, solver: str ) -> None: """Benchmark changing objective in model. Parameters ---------- model: cobra.Model benchmark: BenchmarkFixture solver: str Solver to use. Parametized using 'optlang_solvers' defined above. """ atpm = model.reactions.get_by_id("ATPM") def benchmark_change_objective(): model.objective = atpm.id model.solver = solver benchmark(benchmark_change_objective) def test_get_objective_direction(model: Model) -> None: """Test getting objective. Parameters ---------- model: cobra.Model """ assert model.objective_direction == "max" value = model.slim_optimize() assert np.isclose(value, 0.874, 1e-3) def test_set_objective_direction(model: Model) -> None: """Test setting objective. Parameters ---------- model: cobra.Model """ with model: model.objective_direction = "min" assert model.objective_direction == "min" value = model.slim_optimize() assert value == 0.0 assert model.objective_direction == "max" def test_slim_optimize(model: Model) -> None: """Test slim_optimize with context. Parameters ---------- model: cobra.Model """ with model: assert model.slim_optimize() > 0.872 model.reactions.Biomass_Ecoli_core.lower_bound = 10 assert isnan(model.slim_optimize()) with pytest.raises(OptimizationError): model.slim_optimize(error_value=None) @pytest.mark.parametrize("solver", optlang_solvers) def test_optimize(model: Model, solver: str) -> None: """Test optimizing a model. Parameters ---------- model: cobra.Model solver: str Solver to use. Parametized using 'optlang_solvers' defined above. """ model.solver = solver with model: assert model.optimize().objective_value > 0.872 model.reactions.Biomass_Ecoli_core.lower_bound = 10 with pytest.warns(UserWarning): model.optimize() with pytest.raises(OptimizationError): model.optimize(raise_error=True) def test_change_objective(model: Model) -> None: """Test changing objective. Parameters ---------- model: cobra.Model """ # Test for correct optimization behavior model.optimize() assert model.reactions.Biomass_Ecoli_core.x > 0.5 with model: model.objective = model.reactions.EX_etoh_e model.optimize() assert model.reactions.Biomass_Ecoli_core.x < 0.5 assert model.reactions.Biomass_Ecoli_core.objective_coefficient == 1 model.optimize() assert model.reactions.Biomass_Ecoli_core.x > 0.5 # Test changing objective biomass = model.reactions.get_by_id("Biomass_Ecoli_core") atpm = model.reactions.get_by_id("ATPM") model.objective = atpm.id assert atpm.objective_coefficient == 1.0 assert biomass.objective_coefficient == 0.0 assert su.linear_reaction_coefficients(model) == {atpm: 1.0} # Change it back using object itself model.objective = biomass assert atpm.objective_coefficient == 0.0 assert biomass.objective_coefficient == 1.0 # Set both to 1 with a list model.objective = [atpm, biomass] assert atpm.objective_coefficient == 1.0 assert biomass.objective_coefficient == 1.0 # Set both using a dict model.objective = {atpm: 0.2, biomass: 0.3} assert abs(atpm.objective_coefficient - 0.2) < 10**-9 assert abs(biomass.objective_coefficient - 0.3) < 10**-9 # Test setting by index model.objective = model.reactions.index(atpm) assert su.linear_reaction_coefficients(model) == {atpm: 1.0} # Test by setting list of indexes model.objective = [model.reactions.index(reaction) for reaction in [atpm, biomass]] assert su.linear_reaction_coefficients(model) == {atpm: 1.0, biomass: 1.0} def test_problem_properties(model: Model) -> None: """Test model problem properties. Parameters ---------- model: cobra.Model """ new_variable = model.problem.Variable("test_variable") new_constraint = model.problem.Constraint(Zero, name="test_constraint", lb=0) model.add_cons_vars([new_variable, new_constraint]) assert "test_variable" in model.variables assert "test_constraint" in model.constraints model.remove_cons_vars([new_constraint, new_variable]) assert "test_variable" not in model.variables assert "test_constraint" not in model.variables def test_solution_data_frame(model: Model) -> None: """Test that solution is transformed correctly to a Pandas data frame. Parameters ---------- model: cobra. Model """ solution = model.optimize().to_frame() assert isinstance(solution, pd.DataFrame) assert "fluxes" in solution assert "reduced_costs" in solution def test_context_manager(model: Model) -> None: """Test that the context manager works. Parameters ---------- model: cobra.Model """ bounds0 = model.reactions[0].bounds bounds1 = (1, 2) bounds2 = (3, 4) # Trigger a nested model context, ensuring that bounds are # preserved at each level with model: model.reactions[0].bounds = bounds1 with model: model.reactions[0].bounds = bounds2 assert model.reactions[0].bounds == bounds2 assert model.reactions[0].bounds == bounds1 assert model.reactions[0].bounds == bounds0 def test_objective_coefficient_reflects_changed_objective(model: Model) -> None: """Test that changing objectives is reflected in the objectives changing. Parameters ---------- model: cobra.Model """ biomass_r = model.reactions.get_by_id("Biomass_Ecoli_core") assert biomass_r.objective_coefficient == 1 model.objective = "PGI" assert biomass_r.objective_coefficient == 0 assert model.reactions.PGI.objective_coefficient == 1 def test_change_objective_through_objective_coefficient(model: Model) -> None: """Test that changing the objective coefficients will change the objective. Parameters ---------- model: cobra.Model """ biomass_r = model.reactions.get_by_id("Biomass_Ecoli_core") pgi = model.reactions.PGI pgi.objective_coefficient = 2 coef_dict = model.objective.expression.as_coefficients_dict() # Check that objective has been updated assert coef_dict[pgi.forward_variable] == 2.0 assert coef_dict[pgi.reverse_variable] == -2.0 # Check that original objective is still in there assert coef_dict[biomass_r.forward_variable] == 1.0 assert coef_dict[biomass_r.reverse_variable] == -1.0 def test_transfer_objective(model: Model) -> None: """Test assigning objective from a different mdoel objective. Parameters ---------- model: cobra.Model """ new_mod = Model("new model") new_mod.add_reactions(model.reactions) new_mod.objective = model.objective assert {str(x) for x in model.objective.expression.args} == { str(x) for x in new_mod.objective.expression.args } new_mod.slim_optimize() assert abs(new_mod.objective.value - 0.874) < 0.001 def test_model_from_other_model(model: Model) -> None: """Test creating model from other model. Parameters ---------- model: cobra.Model """ model = Model(id_or_model=model) for reaction in model.reactions: assert reaction == model.reactions.get_by_id(reaction.id) def test_add_reactions(model: Model) -> None: """Test add_reactions() function to add reactions to model. Parameters ---------- model: cobra.Model """ r1 = Reaction("r1") r1.add_metabolites({Metabolite("A"): -1, Metabolite("B"): 1}) r1.lower_bound, r1.upper_bound = -999999.0, 999999.0 r2 = Reaction("r2") r2.add_metabolites({Metabolite("A"): -1, Metabolite("C"): 1, Metabolite("D"): 1}) r2.lower_bound, r2.upper_bound = 0.0, 999999.0 model.add_reactions([r1, r2]) r2.objective_coefficient = 3.0 assert r2.objective_coefficient == 3.0 assert model.reactions[-2] == r1 assert model.reactions[-1] == r2 assert isinstance(model.reactions[-2].reverse_variable, model.problem.Variable) coefficients_dict = model.objective.expression.as_coefficients_dict() biomass_r = model.reactions.get_by_id("Biomass_Ecoli_core") assert coefficients_dict[biomass_r.forward_variable] == 1.0 assert coefficients_dict[biomass_r.reverse_variable] == -1.0 assert coefficients_dict[model.reactions.r2.forward_variable] == 3.0 assert coefficients_dict[model.reactions.r2.reverse_variable] == -3.0 def test_add_reactions_single_existing(model: Model) -> None: """Test adding a reaction already present to a model. Parameters ---------- model: cobra.Model """ rxn = model.reactions[0] r1 = Reaction(rxn.id) r1.add_metabolites({Metabolite("A"): -1, Metabolite("B"): 1}) r1.lower_bound, r1.upper_bound = -999999.0, 999999.0 model.add_reactions([r1]) assert rxn in model.reactions assert r1 is not model.reactions.get_by_id(rxn.id) def test_add_reactions_duplicate(model: Model) -> None: """Test adding duplicate reactions to a model. Parameters ---------- model: cobra.Model """ rxn = model.reactions[0] r1 = Reaction("r1") r1.add_metabolites({Metabolite("A"): -1, Metabolite("B"): 1}) r1.lower_bound, r1.upper_bound = -999999.0, 999999.0 r2 = Reaction(rxn.id) r2.add_metabolites({Metabolite("A"): -1, Metabolite("C"): 1, Metabolite("D"): 1}) model.add_reactions([r1, r2]) assert r1 in model.reactions assert rxn in model.reactions assert r2 is not model.reactions.get_by_id(rxn.id) def test_all_objects_point_to_all_other_correct_objects(model: Model) -> None: """Test that objects point to needed other objects. This will test that the reaction.genes, reaction.metabolites point to the correct genes and metabolites in the model. Parameters ---------- model: cobra.Model """ for reaction in model.reactions: assert reaction.model == model for gene in reaction.genes: assert gene == model.genes.get_by_id(gene.id) assert gene.model == model for reaction2 in gene.reactions: assert reaction2.model == model assert reaction2 == model.reactions.get_by_id(reaction2.id) for metabolite in reaction.metabolites: assert metabolite.model == model assert metabolite == model.metabolites.get_by_id(metabolite.id) for reaction2 in metabolite.reactions: assert reaction2.model == model assert reaction2 == model.reactions.get_by_id(reaction2.id) def test_objects_point_to_correct_other_after_copy(model: Model) -> None: """Test that objects point to correct other objects after copying a model. Parameters ---------- model: cobra.Model """ for reaction in model.reactions: assert reaction.model == model for gene in reaction.genes: assert gene == model.genes.get_by_id(gene.id) assert gene.model == model for reaction2 in gene.reactions: assert reaction2.model == model assert reaction2 == model.reactions.get_by_id(reaction2.id) for metabolite in reaction.metabolites: assert metabolite.model == model assert metabolite == model.metabolites.get_by_id(metabolite.id) for reaction2 in metabolite.reactions: assert reaction2.model == model assert reaction2 == model.reactions.get_by_id(reaction2.id) def test_remove_reactions(model: Model) -> None: """Test remove_reactions() from Model. Parameters ---------- model: cobra.Model """ reactions_to_remove = model.reactions[10:30] assert all([reaction.model is model for reaction in reactions_to_remove]) assert all( [ model.reactions.get_by_id(reaction.id) == reaction for reaction in reactions_to_remove ] ) model.remove_reactions(reactions_to_remove) assert all([reaction.model is None for reaction in reactions_to_remove]) for reaction in reactions_to_remove: assert reaction.id not in list(model.variables.keys()) model.add_reactions(reactions_to_remove) for reaction in reactions_to_remove: assert reaction in model.reactions def test_objective(model: Model) -> None: """Test that objective contains the correct coefficients. Parameters ---------- model: cobra.Model """ obj = model.objective assert obj.get_linear_coefficients(obj.variables) == { model.variables["Biomass_Ecoli_core_reverse_2cdba"]: -1, model.variables["Biomass_Ecoli_core"]: 1, } assert obj.direction == "max" def test_change_objective_with_context(model: Model) -> None: """Test changing objective is reversed with context. Parameters ---------- model: cobra.Model """ expression = 1.0 * model.variables["ENO"] + 1.0 * model.variables["PFK"] model.objective = model.problem.Objective(expression) assert same_ex(model.objective.expression, expression) model.objective = "ENO" eno_obj = model.problem.Objective( model.reactions.ENO.flux_expression, direction="max" ) pfk_obj = model.problem.Objective( model.reactions.PFK.flux_expression, direction="max" ) assert same_ex(model.objective.expression, eno_obj.expression) with model: model.objective = "PFK" assert same_ex(model.objective.expression, pfk_obj.expression) assert same_ex(model.objective.expression, eno_obj.expression) expression = model.objective.expression atpm = model.reactions.get_by_id("ATPM") biomass = model.reactions.get_by_id("Biomass_Ecoli_core") with model: model.objective = atpm assert same_ex(model.objective.expression, expression) with model: atpm.objective_coefficient = 1 biomass.objective_coefficient = 2 assert same_ex(model.objective.expression, expression) with model: set_objective(model, model.problem.Objective(atpm.flux_expression)) assert same_ex(model.objective.expression, atpm.flux_expression) assert same_ex(model.objective.expression, expression) expression = model.objective.expression with model: with model: # Test to make sure nested contexts are OK set_objective(model, atpm.flux_expression, additive=True) assert same_ex( model.objective.expression, expression + atpm.flux_expression ) assert same_ex(model.objective.expression, expression) def test_set_reaction_objective(model: Model) -> None: """Test setting reaction objective. Parameters ---------- model: cobra.Model """ model.objective = model.reactions.ACALD assert same_ex( model.objective.expression, 1.0 * model.reactions.ACALD.forward_variable - 1.0 * model.reactions.ACALD.reverse_variable, ) def test_set_reaction_objective_str(model: Model) -> None: """Test setting reaction objective using string. Parameters ---------- model: cobra.Model """ model.objective = model.reactions.ACALD.id assert same_ex( model.objective.expression, 1.0 * model.reactions.ACALD.forward_variable - 1.0 * model.reactions.ACALD.reverse_variable, ) def test_invalid_objective_raises(model: Model) -> None: """Test that an invalid objective will raise appropriate errors. Parameters ---------- model: cobra.Model """ with pytest.raises(ValueError): model.objective = "This is not a valid objective!" with pytest.raises(TypeError): model.objective = 3.0 @pytest.mark.skipif("cplex" not in solvers, reason="need cplex") def test_solver_change(model: Model) -> None: """Test changing the solver to cplex. This test will be skipped if cplex is not installed and available to python. Parameters ---------- model: cobra.Model """ model.solver = "glpk" solver_id = id(model.solver) problem_id = id(model.solver.problem) solution = model.optimize().fluxes model.solver = "cplex" assert id(model.solver) != solver_id assert id(model.solver.problem) != problem_id new_solution = model.optimize().fluxes assert np.allclose(solution, new_solution, rtol=0, atol=1e-06) def test_no_change_for_same_solver(model: Model) -> None: """Test no change in variables if changing to the same solver as before. Parameters ---------- model: cobra.Model """ model.solver = "glpk" solver_id = id(model.solver) problem_id = id(model.solver.problem) model.solver = "glpk" assert id(model.solver) == solver_id assert id(model.solver.problem) == problem_id def test_invalid_solver_change_raises(model: Model) -> None: """Test changing to an invalid solver will raise SovlerNotFound. Parameters ---------- model: cobra.Model """ with pytest.raises(SolverNotFound): model.solver = [1, 2, 3] with pytest.raises(SolverNotFound): model.solver = "ThisIsDefinitelyNotAvalidSolver" with pytest.raises(SolverNotFound): model.solver = os @pytest.mark.skipif("cplex" not in solvers, reason="no cplex") def test_change_solver_to_cplex_and_check_copy_works(model: Model) -> None: """Test changing solver and copying model work. This test will be skipped if cplex is not installed and available for python. Parameters ---------- model: cobra.Model """ assert (model.slim_optimize() - 0.8739215069684306) == pytest.approx(0.0) model_copy = model.copy() assert (model_copy.slim_optimize() - 0.8739215069684306) == pytest.approx(0.0) # Second, change existing glpk based model to cplex model.solver = "cplex" assert (model.slim_optimize() - 0.8739215069684306) == pytest.approx(0.0) model_copy = copy(model) assert (model_copy.slim_optimize() - 0.8739215069684306) == pytest.approx(0.0) def test_copy_preserves_existing_solution(solved_model: Tuple[Solution, Model]) -> None: """Test copy keeps the existing solution. Primal values are the same when copying a solved model. Parameters ---------- solved_model: Tuple A Tuple that contains a Solution and a Model """ solution, model = solved_model model_cp = copy(model) primals_original = [variable.primal for variable in model.variables] primals_copy = [variable.primal for variable in model_cp.variables] abs_diff = abs(np.array(primals_copy) - np.array(primals_original)) assert not any(abs_diff > 1e-6) def test_repr_html_(model: Model) -> None: """Test HTML representation of model. Parameters ---------- model: cobra.Model """ assert "
" in model._repr_html_() cobrapy-0.29.1/tests/test_core/test_solution.py000066400000000000000000000010531467301562300216660ustar00rootroot00000000000000"""Test functions of solution.py .""" from typing import TYPE_CHECKING, Tuple from cobra.core import Solution if TYPE_CHECKING: from cobra import Model def test_solution_contains_only_reaction_specific_values( solved_model: Tuple[Solution, "Model"] ) -> None: """Test solution contains specific reaction values.""" solution, model = solved_model reaction_ids = set([reaction.id for reaction in model.reactions]) assert set(solution.fluxes.index) == reaction_ids # assert set(solution.reduced_costs.index) == reaction_ids cobrapy-0.29.1/tests/test_flux_analysis/000077500000000000000000000000001467301562300203335ustar00rootroot00000000000000cobrapy-0.29.1/tests/test_flux_analysis/conftest.py000066400000000000000000000074711467301562300225430ustar00rootroot00000000000000"""Define module level fixtures.""" import json from os.path import join from typing import List import pandas as pd import pytest from cobra.core import Model, Reaction, Solution from cobra.util import solver as sutil # The scipy interface is currently unstable and may yield errors or infeasible # solutions. @pytest.fixture( scope="session", params=[s for s in ["glpk", "cplex", "gurobi", "hybrid"] if s in sutil.solvers], ) def all_solvers(request) -> List[str]: """Return the avaialble solvers.""" return request.param @pytest.fixture( scope="session", params=[s for s in ["cplex", "gurobi", "hybrid"] if s in sutil.solvers], ) def qp_solvers(request) -> List[str]: """Return the available QP solvers.""" return request.param @pytest.fixture(scope="module") def fva_results(data_directory) -> pd.DataFrame: """Load and return saved FVA results for textbook model.""" with open(join(data_directory, "textbook_fva.json"), "r") as infile: df = pd.DataFrame(json.load(infile)) df.sort_index(inplace=True) return df[["minimum", "maximum"]] @pytest.fixture(scope="module") def pfba_fva_results(data_directory) -> pd.DataFrame: """Load and return saved pFBA FVA results for textbook model.""" with open(join(data_directory, "textbook_pfba_fva.json"), "r") as infile: df = pd.DataFrame(json.load(infile)) df.sort_index(inplace=True) return df[["minimum", "maximum"]] @pytest.fixture(scope="module") def room_model() -> Model: """Generate ROOM model as described in [1]_ . References ---------- .. [1] Tomer Shlomi, Omer Berkman and Eytan Ruppin, "Regulatory on/off minimization of metabolic flux changes after genetic perturbations", PNAS 2005 102 (21) 7695-7700; doi:10.1073/pnas.0406346102 """ test_model = Model("papin_2003") v_1 = Reaction("v1") v_2 = Reaction("v2") v_3 = Reaction("v3") v_4 = Reaction("v4") v_5 = Reaction("v5") v_6 = Reaction("v6", upper_bound=0.0) b_1 = Reaction("b1", upper_bound=10.0, lower_bound=0.0) b_2 = Reaction("b2") b_3 = Reaction("b3") test_model.add_reactions([v_1, v_2, v_3, v_4, v_5, v_6, b_1, b_2, b_3]) v_1.reaction = "A -> B" v_2.reaction = "2 B -> C + byp" v_3.reaction = "2 B + cof -> D" v_4.reaction = "D -> E + cof" v_5.reaction = "C + cof -> D" v_6.reaction = "C -> E" b_1.reaction = "-> A" b_2.reaction = "E ->" b_3.reaction = "byp ->" test_model.objective = "b2" return test_model @pytest.fixture(scope="module") def room_solution() -> Solution: """Generate ROOM solution as described in [1]_ . References ---------- .. [1] Tomer Shlomi, Omer Berkman and Eytan Ruppin, "Regulatory on/off minimization of metabolic flux changes after genetic perturbations", PNAS 2005 102 (21) 7695-7700; doi:10.1073/pnas.0406346102 """ fluxes = pd.Series( { "b1": 10.0, "b2": 5.0, "b3": 5.0, "v1": 10.0, "v2": 5.0, "v3": 0.0, "v4": 0.0, "v5": 0.0, "v6": 5.0, } ) reduced_costs = pd.Series( { "b1": 0.0, "b2": 0.0, "b3": 0.0, "v1": 0.0, "v2": 0.0, "v3": 0.0, "v4": 0.0, "v5": 0.0, "v6": 0.0, } ) shadow_prices = pd.Series( { "b1": 0.0, "b2": 0.0, "b3": 0.0, "v1": 0.0, "v2": 0.0, "v3": 0.0, "v4": 0.0, "v5": 0.0, "v6": 0.0, } ) sol = Solution( objective_value=5.000, status="optimal", fluxes=fluxes, reduced_costs=reduced_costs, shadow_prices=shadow_prices, ) return sol cobrapy-0.29.1/tests/test_flux_analysis/test_deletion.py000066400000000000000000000326321467301562300235550ustar00rootroot00000000000000"""Test functionalities of reaction and gene deletions.""" import math import os from typing import Callable, List import numpy as np import pandas as pd import pytest from cobra import Model, Solution from cobra.flux_analysis.deletion import ( double_gene_deletion, double_reaction_deletion, single_gene_deletion, single_reaction_deletion, ) from cobra.flux_analysis.room import add_room # Single gene deletion FBA def test_single_gene_deletion_fba_benchmark( model: Model, benchmark: Callable, all_solvers: List[str] ) -> None: """Benchmark single gene deletion using FBA.""" model.solver = all_solvers benchmark(single_gene_deletion, model, model.genes[1::10]) def test_single_gene_deletion_fba(model: Model, all_solvers: List[str]) -> None: """Test single gene deletion using FBA.""" # expected knockouts for textbook model model.solver = all_solvers growth_dict = { "b0008": 0.87, "b0114": 0.80, "b0116": 0.78, "b2276": 0.21, "b1779": 0.00, } result = single_gene_deletion( model=model, gene_list=list(growth_dict), method="fba", processes=1 ) for gene, value in growth_dict.items(): assert np.isclose(result.knockout[gene].growth, value, atol=1e-02) # Singe gene deletion MOMA def test_single_gene_deletion_moma_benchmark( model: Model, benchmark: Callable, qp_solvers: List[str] ) -> None: """Benchmark single gene deletion using MOMA.""" model.solver = qp_solvers genes = ["b0008", "b0114", "b2276", "b1779"] benchmark( single_gene_deletion, model=model, gene_list=genes, method="moma", processes=1, ) def test_single_gene_deletion_moma(model: Model, qp_solvers: List[str]) -> None: """Test single gene deletion using MOMA.""" model.solver = qp_solvers # expected knockouts for textbook model growth_dict = { "b0008": 0.87, "b0114": 0.71, "b0116": 0.56, "b2276": 0.11, "b1779": 0.00, } result = single_gene_deletion( model=model, gene_list=list(growth_dict), method="moma", processes=1 ) for gene, value in growth_dict.items(): assert np.isclose(result.knockout[gene].growth, value, atol=1e-02) def test_single_gene_deletion_moma_reference( model: Model, qp_solvers: List[str] ) -> None: """Test single gene deletion using MOMA (reference solution).""" model.solver = qp_solvers # expected knockouts for textbook model growth_dict = { "b0008": 0.87, "b0114": 0.71, "b0116": 0.56, "b2276": 0.11, "b1779": 0.00, } sol = model.optimize() result = single_gene_deletion( model=model, gene_list=list(growth_dict), method="moma", solution=sol, processes=1, ) for gene, value in growth_dict.items(): assert np.isclose(result.knockout[gene].growth, value, atol=1e-02) # Single gene deletion linear MOMA def test_single_gene_deletion_linear_moma_benchmark( model: Model, benchmark: Callable, all_solvers: List[str] ) -> None: """Benchmark single gene deletion using linear MOMA.""" model.solver = all_solvers genes = ["b0008", "b0114", "b2276", "b1779"] benchmark( single_gene_deletion, model=model, gene_list=genes, method="linear moma", processes=1, ) def test_single_gene_deletion_linear_moma(model: Model, all_solvers: List[str]) -> None: """Test single gene deletion using linear MOMA (reference solution).""" model.solver = all_solvers # expected knockouts for textbook model growth_dict = { "b0008": 0.87, "b0114": 0.76, "b0116": 0.65, "b2276": 0.08, "b1779": 0.00, } sol = model.optimize() result = single_gene_deletion( model=model, gene_list=list(growth_dict), method="linear moma", solution=sol, processes=1, ) for gene, value in growth_dict.items(): assert np.isclose(result.knockout[gene].growth, value, atol=1e-02) # Single gene deletion ROOM def test_single_gene_deletion_room_benchmark( model: Model, benchmark: Callable, all_solvers: List[str] ) -> None: """Benchmark single gene deletion using ROOM.""" if all_solvers == "glpk": pytest.skip("GLPK is too slow to run ROOM.") model.solver = all_solvers genes = ["b0008", "b0114", "b2276", "b1779"] benchmark( single_gene_deletion, model=model, gene_list=genes, method="room", processes=1, ) # Single gene deletion linear ROOM def test_single_gene_deletion_linear_room_benchmark( model: Model, benchmark: Callable, all_solvers: List[str] ) -> None: """Benchmark single gene deletion using linear ROOM.""" model.solver = all_solvers genes = ["b0008", "b0114", "b2276", "b1779"] benchmark( single_gene_deletion, model=model, gene_list=genes, method="linear room", processes=1, ) # Single reaction deletion def test_single_reaction_deletion_benchmark( model: Model, benchmark: Callable, all_solvers: List[str] ) -> None: """Benchmark single reaction deletion.""" model.solver = all_solvers benchmark( single_reaction_deletion, model=model, genes=model.genes[1::10], processes=1 ) def test_single_reaction_deletion(model: Model, all_solvers) -> None: """Test single reaction deletion.""" model.solver = all_solvers expected_results = { "FBA": 0.70404, "FBP": 0.87392, "CS": 0, "FUM": 0.81430, "GAPD": 0, "GLUDy": 0.85139, } result = single_reaction_deletion( model=model, reaction_list=list(expected_results), processes=1 ) for reaction, value in expected_results.items(): assert np.isclose(result.knockout[reaction].growth, value, atol=1e-05) # Single reaction deletion ROOM def test_single_reaction_deletion_room( room_model: Model, room_solution: Solution, all_solvers: List[str] ) -> None: """Test single reaction deletion using ROOM.""" room_model.solver = all_solvers expected = pd.Series( { "v1": 10.0, "v2": 5.0, "v3": 0.0, "v4": 5.0, "v5": 5.0, "v6": 0.0, "b1": 10.0, "b2": 5.0, "b3": 5.0, }, index=["v1", "v2", "v3", "v4", "v5", "v6", "b1", "b2", "b3"], ) with room_model: room_model.reactions.v6.knock_out() add_room(room_model, solution=room_solution, delta=0.0, epsilon=0.0) room_sol = room_model.optimize() assert np.allclose(room_sol.fluxes, expected) # Single reaction deletion linear ROOM def test_single_reaction_deletion_linear_room( room_model: Model, room_solution: Solution, all_solvers: List[str] ) -> None: """Test single reaction deletion using linear ROOM.""" room_model.solver = all_solvers expected = pd.Series( { "v1": 10.0, "v2": 5.0, "v3": 0.0, "v4": 5.0, "v5": 5.0, "v6": 0.0, "b1": 10.0, "b2": 5.0, "b3": 5.0, }, index=["v1", "v2", "v3", "v4", "v5", "v6", "b1", "b2", "b3"], ) with room_model: room_model.reactions.v6.knock_out() add_room( room_model, solution=room_solution, delta=0.0, epsilon=0.0, linear=True, ) linear_room_sol = room_model.optimize() assert np.allclose(linear_room_sol.fluxes, expected) # Double gene deletion def test_double_gene_deletion_benchmark( large_model: Model, benchmark: Callable ) -> None: """Benchmark double gene deletion.""" genes = [ "b0726", "b4025", "b0724", "b0720", "b2935", "b2935", "b1276", "b1241", ] benchmark(double_gene_deletion, large_model, gene_list1=genes, processes=1) @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_double_gene_deletion(model: Model) -> None: """Test double gene deletion.""" genes = [ "b0726", "b4025", "b0724", "b0720", "b2935", "b2935", "b1276", "b1241", ] growth_dict = { "b0720": { "b0720": 0.0, "b0724": 0.0, "b0726": 0.0, "b1241": 0.0, "b1276": 0.0, "b2935": 0.0, "b4025": 0.0, }, "b0724": { "b0720": 0.0, "b0724": 0.814, "b0726": 0.814, "b1241": 0.814, "b1276": 0.814, "b2935": 0.814, "b4025": 0.739, }, "b0726": { "b0720": 0.0, "b0724": 0.814, "b0726": 0.858, "b1241": 0.858, "b1276": 0.858, "b2935": 0.858, "b4025": 0.857, }, "b1241": { "b0720": 0.0, "b0724": 0.814, "b0726": 0.858, "b1241": 0.874, "b1276": 0.874, "b2935": 0.874, "b4025": 0.863, }, "b1276": { "b0720": 0.0, "b0724": 0.814, "b0726": 0.858, "b1241": 0.874, "b1276": 0.874, "b2935": 0.874, "b4025": 0.863, }, "b2935": { "b0720": 0.0, "b0724": 0.814, "b0726": 0.858, "b1241": 0.874, "b1276": 0.874, "b2935": 0.874, "b4025": 0.863, }, "b4025": { "b0720": 0.0, "b0724": 0.739, "b0726": 0.857, "b1241": 0.863, "b1276": 0.863, "b2935": 0.863, "b4025": 0.863, }, } solution = double_gene_deletion(model, gene_list1=genes, processes=3) solution_one_process = double_gene_deletion(model, gene_list1=genes, processes=1) for rxn_a, sub in growth_dict.items(): for rxn_b, growth in sub.items(): sol = solution.knockout[{rxn_a, rxn_b}] sol_one = solution_one_process.knockout[{rxn_a, rxn_b}] assert np.isclose(sol.growth, growth, atol=1e-3) assert np.isclose(sol_one.growth, growth, atol=1e-3) def test_double_gene_knockout_bug(large_model: Model) -> None: """Test that the bug reported in #1102 is fixed.""" genes = ["b0118", "b1276"] expected = ( pd.DataFrame( data={ "ids": [ {"b0118"}, {"b1276"}, {"b1276", "b0118"}, ], "growth": [0.98, 0.98, 0.0], "status": ["optimal"] * 3, } ) .sort_values("ids") .reset_index() ) result = ( double_gene_deletion(large_model, genes, processes=1) .sort_values("ids") .reset_index() ) assert result["growth"].values == pytest.approx(expected["growth"].values, abs=0.01) assert (result["status"] == expected["status"]).all() # Double reaction deletion def test_double_reaction_deletion_benchmark( large_model: Model, benchmark: Callable ) -> None: """Benchmark double reaction deletion.""" reactions = large_model.reactions[100:105] benchmark(double_reaction_deletion, large_model, reaction_list1=reactions) @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_double_reaction_deletion(model: Model) -> None: """Test double reaction deletion.""" reactions = ["FBA", "ATPS4r", "ENO", "FRUpts2"] growth_dict = { "FBA": {"ATPS4r": 0.135, "ENO": float("nan"), "FRUpts2": 0.704}, "ATPS4r": {"ENO": float("nan"), "FRUpts2": 0.374}, "ENO": {"FRUpts2": 0.0}, } solution = double_reaction_deletion(model, reaction_list1=reactions, processes=3) solution_one_process = double_reaction_deletion( model, reaction_list1=reactions, processes=1 ) for rxn_a, sub in growth_dict.items(): for rxn_b, growth in sub.items(): sol = solution.knockout[{rxn_a, rxn_b}] sol_one = solution_one_process.knockout[{rxn_a, rxn_b}] if math.isnan(growth): assert math.isnan(sol.growth) assert math.isnan(sol_one.growth) else: assert np.isclose(sol.growth, growth, atol=1e-3) assert np.isclose(sol_one.growth, growth, atol=1e-3) def test_deletion_accessor(small_model: Model) -> None: """Test the DataFrame accessor.""" single = single_reaction_deletion(small_model, small_model.reactions[0:10]) double = double_reaction_deletion(small_model, small_model.reactions[0:10]) rxn1 = small_model.reactions[0] rxn2 = small_model.reactions[1] with pytest.raises(ValueError): single.knockout[1] with pytest.raises(ValueError): single.knockout[{"a": 1}] assert single.knockout[rxn1].ids.iloc[0] == {rxn1.id} assert double.knockout[{rxn1, rxn2}].ids.iloc[0] == {rxn1.id, rxn2.id} assert all(single.knockout[rxn1.id] == single.knockout[rxn1]) assert all(double.knockout[{rxn1.id, rxn2.id}] == double.knockout[{rxn1, rxn2}]) assert single.knockout[rxn1, rxn2].shape == (2, 3) assert double.knockout[rxn1, rxn2].shape == (2, 3) assert double.knockout[{rxn1, rxn2}].shape == (1, 3) assert double.knockout[{rxn1}, {rxn2}].shape == (2, 3) cobrapy-0.29.1/tests/test_flux_analysis/test_fastcc.py000066400000000000000000000063321467301562300232130ustar00rootroot00000000000000"""Test functionalities of FASTCC.""" from typing import Callable, List import pytest from cobra import Model, Reaction from cobra.flux_analysis import fastcc, flux_variability_analysis @pytest.fixture(scope="module") def figure1_model() -> Model: """Generate a toy model as described in [1]_ figure 1. References ---------- .. [1] Vlassis N, Pacheco MP, Sauter T (2014) Fast Reconstruction of Compact Context-Specific Metabolic Network Models. PLoS Comput Biol 10(1): e1003424. doi:10.1371/journal.pcbi.1003424 """ test_model = Model("figure 1") v1 = Reaction("v1") v2 = Reaction("v2") v3 = Reaction("v3") v4 = Reaction("v4") v5 = Reaction("v5") v6 = Reaction("v6") test_model.add_reactions([v1, v2, v3, v4, v5, v6]) v1.reaction = "-> 2 A" v2.reaction = "A <-> B" v3.reaction = "A -> D" v4.reaction = "A -> C" v5.reaction = "C -> D" v6.reaction = "D ->" v1.bounds = (0.0, 3.0) v2.bounds = (-3.0, 3.0) v3.bounds = (0.0, 3.0) v4.bounds = (0.0, 3.0) v5.bounds = (0.0, 3.0) v6.bounds = (0.0, 3.0) test_model.objective = v6 return test_model @pytest.fixture(scope="module") def opposing_model() -> Model: """Generate a toy model with opposing reversible reactions. This toy model ensures that two opposing reversible reactions do not appear as blocked. """ test_model = Model("opposing") v1 = Reaction("v1") v2 = Reaction("v2") v3 = Reaction("v3") v4 = Reaction("v4") test_model.add_reactions([v1, v2, v3, v4]) v1.reaction = "-> 2 A" v2.reaction = "A -> C" # Later made reversible via bounds. v3.reaction = "D -> C" # Later made reversible via bounds. v4.reaction = "D ->" v1.bounds = 0.0, 3.0 v2.bounds = -3.0, 3.0 v3.bounds = -3.0, 3.0 v4.bounds = 0.0, 3.0 test_model.objective = v4 return test_model def test_fastcc_benchmark( model: Model, benchmark: Callable, all_solvers: List[str] ) -> None: """Benchmark fastcc.""" model.solver = all_solvers benchmark(fastcc, model) def test_figure1(figure1_model: Model, all_solvers: List[str]) -> None: """Test FASTCC.""" figure1_model.solver = all_solvers consistent_model = fastcc(figure1_model) expected_reactions = {"v1", "v3", "v4", "v5", "v6"} assert expected_reactions == {rxn.id for rxn in consistent_model.reactions} def test_opposing(opposing_model: Model, all_solvers: List[str]) -> None: """Test FASTCC.""" opposing_model.solver = all_solvers consistent_model = fastcc(opposing_model) expected_reactions = {"v1", "v2", "v3", "v4"} assert expected_reactions == {rxn.id for rxn in consistent_model.reactions} def test_fastcc_against_fva_nonblocked_rxns( model: Model, all_solvers: List[str] ) -> None: """Test non-blocked reactions obtained by FASTCC against FVA.""" model.solver = all_solvers fastcc_consistent_model = fastcc(model) fva = flux_variability_analysis(model, fraction_of_optimum=0.0) nonblocked_rxns_fva = fva.index[ (fva.minimum.abs() > model.tolerance) | (fva.maximum.abs() > model.tolerance) ] assert all(fastcc_consistent_model.reactions) == all(nonblocked_rxns_fva.tolist()) cobrapy-0.29.1/tests/test_flux_analysis/test_gapfilling.py000066400000000000000000000063751467301562300240730ustar00rootroot00000000000000"""Test functionalities of gap filling.""" from cobra.core import Metabolite, Model, Reaction from cobra.flux_analysis.gapfilling import GapFiller, gapfill def test_gapfilling(salmonella: Model) -> None: """Test Gapfilling.""" m = Model() m.add_metabolites([Metabolite(m_id) for m_id in ["a", "b", "c"]]) exa = Reaction("EX_a") exa.add_metabolites({m.metabolites.a: 1}) b2c = Reaction("b2c") b2c.add_metabolites({m.metabolites.b: -1, m.metabolites.c: 1}) dmc = Reaction("DM_c") dmc.add_metabolites({m.metabolites.c: -1}) m.add_reactions([exa, b2c, dmc]) m.objective = "DM_c" universal = Model() a2b = Reaction("a2b") a2d = Reaction("a2d") universal.add_reactions([a2b, a2d]) a2b.build_reaction_from_string("a --> b", verbose=False) a2d.build_reaction_from_string("a --> d", verbose=False) # # GrowMatch # result = gapfilling.growMatch(m, universal)[0] result = gapfill(m, universal)[0] assert len(result) == 1 assert result[0].id == "a2b" # # SMILEY # result = gapfilling.SMILEY(m, "b", universal)[0] with m: m.objective = m.add_boundary(m.metabolites.b, type="demand") result = gapfill(m, universal)[0] assert len(result) == 1 assert result[0].id == "a2b" # # 2 rounds of GrowMatch with exchange reactions # result = gapfilling.growMatch(m, None, ex_rxns=True, iterations=2) result = gapfill(m, None, exchange_reactions=True, iterations=2) assert len(result) == 2 assert len(result[0]) == 1 assert len(result[1]) == 1 assert {i[0].id for i in result} == {"EX_b", "EX_c"} # # Gapfilling solution adds metabolites not present in original model # test for when demand = T # a demand reaction must be added to clear new metabolite universal_noDM = Model() a2b = Reaction("a2b") universal_noDM.add_reactions([a2b]) a2b.build_reaction_from_string("a --> b + d", verbose=False) result = gapfill( m, universal_noDM, exchange_reactions=False, demand_reactions=True )[0] # add reaction a2b and demand reaction to clear met d assert len(result) == 2 assert "a2b" in [x.id for x in result] # test for when demand = False # test for when metabolites are added to the model and # must be cleared by other reactions in universal model # (i.e. not necessarily a demand reaction) universal_withDM = universal_noDM.copy() d_dm = Reaction("d_dm") universal_withDM.add_reactions([d_dm]) d_dm.build_reaction_from_string("d -->", verbose=False) result = gapfill( m, universal_withDM, exchange_reactions=False, demand_reactions=False )[0] assert len(result) == 2 assert "a2b" in [x.id for x in result] # somewhat bigger model universal = Model("universal_reactions") with salmonella as model: for i in [i.id for i in model.metabolites.f6p_c.reactions]: reaction = model.reactions.get_by_id(i) universal.add_reactions([reaction.copy()]) model.remove_reactions([reaction]) gf = GapFiller( model, universal, penalties={"TKT2": 1e3}, demand_reactions=False ) solution = gf.fill() assert "TKT2" not in {r.id for r in solution[0]} assert gf.validate(solution[0]) cobrapy-0.29.1/tests/test_flux_analysis/test_geometric.py000066400000000000000000000041751467301562300237310ustar00rootroot00000000000000"""Test functionalities of Geometric FBA.""" from typing import Callable, List import numpy as np import pandas as pd import pytest from cobra.core import Metabolite, Model, Reaction from cobra.flux_analysis import geometric_fba @pytest.fixture(scope="module") def geometric_fba_model() -> Model: """Generate geometric FBA model as described in [1]_ . References ---------- .. [1] Smallbone, Kieran & Simeonidis, Vangelis. (2009). Flux balance analysis: A geometric perspective. Journal of theoretical biology.258. 311-5. 10.1016/j.jtbi.2009.01.027. """ test_model = Model("geometric_fba_paper_model") test_model.add_metabolites(Metabolite("A")) test_model.add_metabolites(Metabolite("B")) v_1 = Reaction("v1", upper_bound=1.0) v_1.add_metabolites({test_model.metabolites.A: 1.0}) v_2 = Reaction("v2", lower_bound=-1000.0) v_2.add_metabolites({test_model.metabolites.A: -1.0, test_model.metabolites.B: 1.0}) v_3 = Reaction("v3", lower_bound=-1000.0) v_3.add_metabolites({test_model.metabolites.A: -1.0, test_model.metabolites.B: 1.0}) v_4 = Reaction("v4", lower_bound=-1000.0) v_4.add_metabolites({test_model.metabolites.A: -1.0, test_model.metabolites.B: 1.0}) v_5 = Reaction("v5") v_5.add_metabolites({test_model.metabolites.A: 0.0, test_model.metabolites.B: -1.0}) test_model.add_reactions([v_1, v_2, v_3, v_4, v_5]) test_model.objective = "v5" return test_model def test_geometric_fba_benchmark( model: Model, benchmark: Callable, all_solvers: List[str] ) -> None: """Benchmark geometric_fba.""" model.solver = all_solvers benchmark(geometric_fba, model, processes=1) def test_geometric_fba(geometric_fba_model: Model, all_solvers: List[str]) -> None: """Test geometric_fba.""" geometric_fba_model.solver = all_solvers geometric_fba_sol = geometric_fba(geometric_fba_model, processes=1) expected = pd.Series( {"v1": 1.0, "v2": 0.33, "v3": 0.33, "v4": 0.33, "v5": 1.0}, index=["v1", "v2", "v3", "v4", "v5"], ) assert np.allclose(geometric_fba_sol.fluxes, expected, atol=1e-02) cobrapy-0.29.1/tests/test_flux_analysis/test_helpers.py000066400000000000000000000014301467301562300234040ustar00rootroot00000000000000"""Test functionalities of flux analysis helper functions.""" import pytest from cobra.core import Model from cobra.flux_analysis.helpers import normalize_cutoff def test_normalize_cutoff(model: Model) -> None: """Test normalize cutoff.""" cutoff = normalize_cutoff(model) assert cutoff == 1e-7 def test_normalize_cutoff_with_specified_cutoff_above_default( model: Model, ) -> None: """Test normalize cutoff with specified cutoff greater than default.""" cutoff = normalize_cutoff(model, 1e-3) assert cutoff == 1e-3 def test_normalize_cutoff_with_specified_cutoff_below_default( model: Model, ) -> None: """Test normalize cutoff with specified cutoff less than default.""" with pytest.raises(ValueError): normalize_cutoff(model, 1e-10) cobrapy-0.29.1/tests/test_flux_analysis/test_loopless.py000066400000000000000000000063411467301562300236100ustar00rootroot00000000000000"""Test functionalities of removing loops in model.""" from typing import Callable import pytest from optlang.interface import INFEASIBLE, OPTIMAL from cobra.core import Metabolite, Model, Reaction from cobra.flux_analysis.loopless import add_loopless, loopless_solution from cobra.util import solver as sutil def construct_ll_test_model() -> Model: """Construct test model.""" test_model = Model() test_model.add_metabolites(Metabolite("A")) test_model.add_metabolites(Metabolite("B")) test_model.add_metabolites(Metabolite("C")) EX_A = Reaction("EX_A") EX_A.add_metabolites({test_model.metabolites.A: 1}) DM_C = Reaction("DM_C") DM_C.add_metabolites({test_model.metabolites.C: -1}) v1 = Reaction("v1") v1.add_metabolites({test_model.metabolites.A: -1, test_model.metabolites.B: 1}) v2 = Reaction("v2") v2.add_metabolites({test_model.metabolites.B: -1, test_model.metabolites.C: 1}) v3 = Reaction("v3") v3.add_metabolites({test_model.metabolites.C: -1, test_model.metabolites.A: 1}) test_model.add_reactions([EX_A, DM_C, v1, v2, v3]) DM_C.objective_coefficient = 1 return test_model @pytest.fixture( scope="function", params=[s for s in ["glpk", "cplex", "gurobi"] if s in sutil.solvers], ) def ll_test_model(request: pytest.FixtureRequest) -> Model: """Return test model set with different solvers.""" test_model = construct_ll_test_model() test_model.solver = request.param return test_model def test_loopless_benchmark_before(benchmark: Callable) -> None: """Benchmark initial condition.""" test_model = construct_ll_test_model() def _(): with test_model: add_loopless(test_model) test_model.optimize() benchmark(_) def test_loopless_benchmark_after(benchmark: Callable) -> None: """Benchmark final condition.""" test_model = construct_ll_test_model() benchmark(loopless_solution, test_model) def test_loopless_solution(ll_test_model: Model) -> None: """Test loopless_solution().""" opt_feasible = ll_test_model.slim_optimize() solution_feasible = loopless_solution(ll_test_model) ll_test_model.reactions.v3.lower_bound = 1 opt_infeasible = ll_test_model.slim_optimize() solution_infeasible = loopless_solution(ll_test_model) assert solution_feasible.fluxes["v3"] == 0.0 assert solution_feasible.objective_value == pytest.approx(opt_feasible) assert solution_infeasible.fluxes["v3"] == 1.0 assert solution_infeasible.objective_value == pytest.approx(opt_infeasible) def test_loopless_solution_fluxes(model: Model) -> None: """Test fluxes of loopless_solution().""" sol = model.optimize() ll_solution = loopless_solution(model, fluxes=sol.fluxes) assert len(ll_solution.fluxes) == len(model.reactions) assert ll_solution.objective_value == pytest.approx(sol.objective_value) def test_add_loopless(ll_test_model: Model) -> None: """Test add_loopless().""" add_loopless(ll_test_model) feasible_status = ll_test_model.optimize().status ll_test_model.reactions.v3.lower_bound = 1 ll_test_model.slim_optimize() infeasible_status = ll_test_model.solver.status assert feasible_status == OPTIMAL assert infeasible_status == INFEASIBLE cobrapy-0.29.1/tests/test_flux_analysis/test_moma.py000066400000000000000000000044261467301562300227030ustar00rootroot00000000000000"""Test functionalities of MOMA.""" from typing import List import numpy as np import pytest from cobra import Model from cobra.flux_analysis.moma import add_moma def test_moma_sanity(model: Model, qp_solvers: List[str]) -> None: """Test optimization criterion and optimality for MOMA.""" model.solver = qp_solvers sol = model.optimize() with model: model.reactions.PFK.knock_out() knock_sol = model.optimize() ssq = (knock_sol.fluxes - sol.fluxes).pow(2).sum() with model: add_moma(model, linear=False) model.reactions.PFK.knock_out() moma_sol = model.optimize() moma_ssq = (moma_sol.fluxes - sol.fluxes).pow(2).sum() # Use normal FBA as reference solution. with model: add_moma(model, solution=sol, linear=False) model.reactions.PFK.knock_out() moma_ref_sol = model.optimize() moma_ref_ssq = (moma_ref_sol.fluxes - sol.fluxes).pow(2).sum() assert np.isclose(moma_sol.objective_value, moma_ssq) assert moma_ssq < ssq assert np.isclose(moma_sol.objective_value, moma_ref_sol.objective_value) assert np.isclose(moma_ssq, moma_ref_ssq) def test_linear_moma_sanity(model: Model, all_solvers: List[str]) -> None: """Test optimization criterion and optimality for linear MOMA.""" model.solver = all_solvers sol = model.optimize() with model: model.reactions.PFK.knock_out() knock_sol = model.optimize() sabs = (knock_sol.fluxes - sol.fluxes).abs().sum() with model: add_moma(model, linear=True) model.reactions.PFK.knock_out() moma_sol = model.optimize() moma_sabs = (moma_sol.fluxes - sol.fluxes).abs().sum() # Use normal FBA as reference solution. with model: add_moma(model, solution=sol, linear=True) model.reactions.PFK.knock_out() moma_ref_sol = model.optimize() moma_ref_sabs = (moma_ref_sol.fluxes - sol.fluxes).abs().sum() assert np.allclose(moma_sol.objective_value, moma_sabs) assert moma_sabs < sabs assert np.isclose(moma_sol.objective_value, moma_ref_sol.objective_value) assert np.isclose(moma_sabs, moma_ref_sabs) with model: add_moma(model, linear=True) with pytest.raises(ValueError): add_moma(model) cobrapy-0.29.1/tests/test_flux_analysis/test_parsimonious.py000066400000000000000000000042531467301562300245000ustar00rootroot00000000000000"""Test functionalities of pFBA.""" import warnings from typing import Callable, List import pytest from cobra.core import Model from cobra.exceptions import Infeasible from cobra.flux_analysis.parsimonious import add_pfba, pfba def test_pfba_benchmark( large_model: Model, benchmark: Callable, all_solvers: List[str] ) -> None: """Benchmark pFBA functionality.""" large_model.solver = all_solvers benchmark(pfba, large_model) def test_pfba(model: Model, all_solvers: List[str]) -> None: """Test pFBA functionality.""" model.solver = all_solvers with model: add_pfba(model) with pytest.raises(ValueError): add_pfba(model) expression = model.objective.expression n_constraints = len(model.constraints) solution = pfba(model) assert solution.status == "optimal" assert solution.fluxes["Biomass_Ecoli_core"] == pytest.approx( 0.8739, abs=1e-4, rel=0.0 ) assert solution.fluxes.abs().sum() == pytest.approx(518.4221, abs=1e-4, rel=0.0) # test changes to model reverted assert expression == model.objective.expression assert len(model.constraints) == n_constraints # needed? # Test desired_objective_value # desired_objective = 0.8 # pfba(model, solver=solver, # desired_objective_value=desired_objective) # abs_x = [abs(i) for i in model.solution.x] # assert model.solution.status == "optimal" # assert abs(model.solution.f - desired_objective) < 0.001 # assert abs(sum(abs_x) - 476.1594) < 0.001 # TODO: parametrize fraction (DRY it up) # Test fraction_of_optimum solution = pfba(model, fraction_of_optimum=0.95) assert solution.status == "optimal" assert solution.fluxes["Biomass_Ecoli_core"] == pytest.approx( 0.95 * 0.8739, abs=1e-4, rel=0.0 ) abs_x = [abs(i) for i in solution.fluxes.values] assert sum(abs_x) == pytest.approx(493.4400, abs=1e-4, rel=0.0) # Infeasible solution model.reactions.ATPM.lower_bound = 500 with warnings.catch_warnings(): warnings.simplefilter("error", UserWarning) with pytest.raises((UserWarning, Infeasible, ValueError)): pfba(model) cobrapy-0.29.1/tests/test_flux_analysis/test_phenotype_phase_plane.py000066400000000000000000000027031467301562300263200ustar00rootroot00000000000000"""Test functionalities of Phenotype Phase Plane Analysis.""" import numpy as np import pytest from cobra import Model from cobra.flux_analysis import production_envelope def test_envelope_one(model: Model) -> None: """Test flux of production envelope.""" df = production_envelope(model, ["EX_o2_e"]) assert np.isclose(df["flux_maximum"].sum(), 9.342, atol=1e-3) def test_envelope_multi_reaction_objective(model: Model) -> None: """Test production of multiple objectives.""" obj = {model.reactions.EX_ac_e: 1, model.reactions.EX_co2_e: 1} with pytest.raises(ValueError): production_envelope(model, "EX_o2_e", obj) @pytest.mark.parametrize( "variables, num", [ (["EX_glc__D_e"], 30), (["EX_glc__D_e", "EX_o2_e"], 20), (["EX_glc__D_e", "EX_o2_e", "EX_ac_e"], 10), ], ) def test_multi_variable_envelope(model: Model, variables: str, num: int) -> None: """Test production of envelope (multiple variable).""" df = production_envelope(model, variables, points=num) assert len(df) == num ** len(variables) def test_envelope_two(model: Model) -> None: """Test production of envelope.""" df = production_envelope(model, ["EX_glc__D_e", "EX_o2_e"], objective="EX_ac_e") assert np.isclose(df["flux_maximum"].sum(), 1737.466, atol=1e-3) assert np.isclose(df["carbon_yield_maximum"].sum(), 83.579, atol=1e-3) assert np.isclose(df["mass_yield_maximum"].sum(), 82.176, atol=1e-3) cobrapy-0.29.1/tests/test_flux_analysis/test_reaction.py000066400000000000000000000016061467301562300235530ustar00rootroot00000000000000"""Test assessing functions in flux_analysis.reaction.""" from typing import List from cobra.core import Metabolite, Model, Reaction from cobra.flux_analysis.reaction import assess def test_assess(model: Model, all_solvers: List[str]) -> None: """Test assess functions.""" with model: assert assess(model, model.reactions.GLCpts, solver=all_solvers) is True pyr = model.metabolites.pyr_c a = Metabolite("a") b = Metabolite("b") model.add_metabolites([a, b]) pyr_a2b = Reaction("pyr_a2b") pyr_a2b.add_metabolites({pyr: -1, a: -1, b: 1}) model.add_reactions([pyr_a2b]) res = assess(model, pyr_a2b, 0.01, solver=all_solvers) expected = { "precursors": {a: {"required": 0.01, "produced": 0.0}}, "products": {b: {"required": 0.01, "capacity": 0.0}}, } assert res == expected cobrapy-0.29.1/tests/test_flux_analysis/test_room.py000066400000000000000000000032501467301562300227200ustar00rootroot00000000000000"""Test functionalities of ROOM.""" from typing import List import pytest from cobra import Model from cobra.flux_analysis.room import add_room @pytest.mark.parametrize( "linear, delta, eps", [(True, 0.03, 1e-3), (False, 0.03, 1e-3), (True, 0.1, 1e-2), (False, 0.1, 1e-2)], ) def test_room_sanity( model: Model, all_solvers: List[str], linear: bool, delta: float, eps: float ) -> None: """Test optimization criterion and optimality for ROOM.""" model.solver = all_solvers sol = model.optimize() with model: model.reactions.PYK.knock_out() knock_sol = model.optimize() with model: # let it calculate its own reference solution (pFBA) add_room(model, linear=linear, delta=delta, epsilon=eps) model.reactions.PYK.knock_out() room_sol = model.optimize() with model: # use the more distant FBA reference add_room(model, solution=sol, linear=linear, delta=delta, epsilon=eps) model.reactions.PYK.knock_out() room_sol_ref = model.optimize() # The flux_change = (sol.fluxes - knock_sol.fluxes).abs() flux_change_room = (sol.fluxes - room_sol.fluxes).abs() flux_change_room_ref = (sol.fluxes - room_sol_ref.fluxes).abs() rxn_count_naive = (flux_change > delta * sol.fluxes.abs() + eps + 1e-6).sum() rxn_count_room = (flux_change_room > delta * sol.fluxes.abs() + eps + 1e-6).sum() rxn_count_room_ref = ( flux_change_room_ref > delta * sol.fluxes.abs() + eps + 1e-6 ).sum() # Expect the ROOM solution to have less changed reactions then a pFBA or FBA assert rxn_count_room <= rxn_count_naive assert rxn_count_room_ref <= rxn_count_naive cobrapy-0.29.1/tests/test_flux_analysis/test_variability.py000066400000000000000000000142721467301562300242710ustar00rootroot00000000000000"""Test functionalities of Flux Variability Analysis.""" import os from typing import Callable, List import numpy as np import pandas as pd import pytest from cobra import Model from cobra.exceptions import Infeasible from cobra.flux_analysis.variability import ( find_blocked_reactions, find_essential_genes, find_essential_reactions, flux_variability_analysis, ) # FVA def test_flux_variability_benchmark( large_model: Model, benchmark: Callable, all_solvers: List[str] ) -> None: """Benchmark FVA.""" large_model.solver = all_solvers benchmark( flux_variability_analysis, large_model, reaction_list=large_model.reactions[1::100], processes=1, ) def test_pfba_flux_variability( model: Model, pfba_fva_results: pd.DataFrame, fva_results: pd.DataFrame, all_solvers: List[str], ) -> None: """Test FVA using pFBA.""" model.solver = all_solvers with pytest.warns(UserWarning): flux_variability_analysis( model, pfba_factor=0.1, reaction_list=model.reactions[1::3], processes=1, ) fva_out = flux_variability_analysis( model, pfba_factor=1.1, reaction_list=model.reactions, processes=1 ) fva_out.sort_index(inplace=True) assert np.allclose(fva_out, pfba_fva_results) abs_fva_out = fva_out.dropna().abs() abs_fva_results = fva_results.dropna().abs() comparison = np.isclose(abs_fva_out, abs_fva_results) | ( abs_fva_out < abs_fva_results ) assert comparison["minimum"].all() assert comparison["maximum"].all() def test_loopless_pfba_fva(model: Model) -> None: """Test loopless FVA using pFBA.""" loop_reactions = [model.reactions.get_by_id(rid) for rid in ("FRD7", "SUCDi")] fva_loopless = flux_variability_analysis( model, pfba_factor=1.1, reaction_list=loop_reactions, loopless=True, processes=1, ) assert np.allclose(fva_loopless["maximum"], fva_loopless["minimum"]) def test_flux_variability( model: Model, fva_results: pd.DataFrame, all_solvers: List[str] ) -> None: """Test FVA.""" model.solver = all_solvers fva_out = flux_variability_analysis( model, reaction_list=model.reactions, processes=1 ) fva_out.sort_index(inplace=True) assert np.allclose(fva_out, fva_results) @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_parallel_flux_variability( model: Model, fva_results: pd.DataFrame, all_solvers: List[str] ) -> None: """Test parallel FVA.""" model.solver = all_solvers fva_out = flux_variability_analysis(model, processes=2) fva_out.sort_index(inplace=True) assert np.allclose(fva_out, fva_results) # Loopless FVA def test_flux_variability_loopless_benchmark( model: Model, benchmark: Callable, all_solvers: List[str] ) -> None: """Benchmark loopless FVA.""" model.solver = all_solvers benchmark( flux_variability_analysis, model, loopless=True, reaction_list=model.reactions[1::50], ) def test_flux_variability_loopless(model: Model, all_solvers: List[str]) -> None: """Test loopless FVA.""" model.solver = all_solvers loop_reactions = [model.reactions.get_by_id(rid) for rid in ("FRD7", "SUCDi")] fva_normal = flux_variability_analysis(model, reaction_list=loop_reactions) fva_loopless = flux_variability_analysis( model, reaction_list=loop_reactions, loopless=True ) assert not np.allclose(fva_normal["maximum"], fva_normal["minimum"]) assert np.allclose(fva_loopless["maximum"], fva_loopless["minimum"]) # Internals (essentiality, blocked reactions) def test_fva_data_frame(model: Model) -> None: """Test DataFrame obtained from FVA.""" df = flux_variability_analysis(model) assert np.all([df.columns.values == ["minimum", "maximum"]]) def test_fva_infeasible(model: Model) -> None: """Test FVA infeasibility.""" infeasible_model = model.copy() infeasible_model.reactions.get_by_id("EX_glc__D_e").lower_bound = 0 # ensure that an infeasible model does not run FVA with pytest.raises(Infeasible): flux_variability_analysis(infeasible_model) def test_fva_minimization(model: Model) -> None: """Test minimization using FVA.""" model.objective = model.reactions.EX_glc__D_e model.objective_direction = "min" solution = flux_variability_analysis(model, fraction_of_optimum=0.95) assert solution.at["EX_glc__D_e", "minimum"] == -10.0 assert solution.at["EX_glc__D_e", "maximum"] == -9.5 def test_find_blocked_reactions_solver_none(model: Model) -> None: """Test find_blocked_reactions() [no specific solver].""" result = find_blocked_reactions(model, model.reactions[40:46]) assert result == ["FRUpts2"] def test_essential_genes(model: Model) -> None: """Test find_essential_genes().""" essential_genes = { "b2779", "b1779", "b0720", "b2416", "b2926", "b1136", "b2415", } observed_essential_genes = {g.id for g in find_essential_genes(model)} assert observed_essential_genes == essential_genes def test_essential_reactions(model: Model) -> None: """Test find_blocked_reactions().""" essential_reactions = { "GLNS", "Biomass_Ecoli_core", "PIt2r", "GAPD", "ACONTb", "EX_nh4_e", "ENO", "EX_h_e", "EX_glc__D_e", "ICDHyr", "CS", "NH4t", "GLCpts", "PGM", "EX_pi_e", "PGK", "RPI", "ACONTa", } observed_essential_reactions = {r.id for r in find_essential_reactions(model)} assert observed_essential_reactions == essential_reactions def test_find_blocked_reactions(model: Model, all_solvers: List[str]) -> None: """Test find_blocked_reactions().""" model.solver = all_solvers result = find_blocked_reactions(model, model.reactions[40:46]) assert result == ["FRUpts2"] result = find_blocked_reactions(model, model.reactions[42:48]) assert set(result) == {"FUMt2_2", "FRUpts2"} result = find_blocked_reactions(model, model.reactions[30:50], open_exchanges=True) assert result == [] cobrapy-0.29.1/tests/test_io/000077500000000000000000000000001467301562300160615ustar00rootroot00000000000000cobrapy-0.29.1/tests/test_io/conftest.py000066400000000000000000000114561467301562300202670ustar00rootroot00000000000000"""Contains module level fixtures and utility functions.""" from pathlib import Path from pickle import load from typing import Callable import pytest from optlang.interface import OPTIMAL from cobra import Model @pytest.fixture(scope="module") def mini_model(data_directory: Path) -> Model: """Fixture for mini model.""" with (data_directory / "mini.pickle").open(mode="rb") as infile: return load(infile) @pytest.fixture def compare_models() -> Callable: """Compare models as a fixture. Returns ------- _compare_models: Callable A function that will assert that the models are identical. """ def _compare_models(model_1: Model, model_2: Model) -> None: """Compare two models (only for testing purposes). Parameters ---------- model_1: Model First model to compare. model_2: Model Second model to compare. This function does not return anything, and does a series of asserts on the two models. If the models are not identical, the asserts, and the test that called them will fail. """ assert len(model_1.reactions) == len(model_2.reactions) assert len(model_1.metabolites) == len(model_2.metabolites) assert len(model_1.genes) == len(model_2.genes) assert model_1.objective.direction == model_2.objective.direction # check Reaction attributes for attr in ( "id", "name", "lower_bound", "upper_bound", "objective_coefficient", "gene_reaction_rule", ): assert getattr(model_1.reactions[0], attr) == getattr( model_2.reactions[0], attr ) assert getattr(model_1.reactions[5], attr) == getattr( model_2.reactions[5], attr ) assert getattr(model_1.reactions[-1], attr) == getattr( model_2.reactions[-1], attr ) # check Metabolite attributes for attr in ("id", "name", "compartment", "formula", "charge"): assert getattr(model_1.metabolites[0], attr) == getattr( model_2.metabolites[0], attr ) assert getattr(model_1.metabolites[5], attr) == getattr( model_2.metabolites[5], attr ) assert getattr(model_1.metabolites[-1], attr) == getattr( model_2.metabolites[-1], attr ) assert len(model_1.reactions[0].metabolites) == len( model_2.reactions[0].metabolites ) # TODO: either relax gene attribute checking or fix models for testing. # check Gene attributes # for attr in ("id", "name"): # assert getattr(model_1.genes[0], attr) == getattr(model_2.genes[0], # attr) # assert getattr(model_1.genes[10], attr) == getattr(model_2.genes[10], # attr) # assert getattr(model_1.genes[-1], attr) == getattr(model_2.genes[-1], # attr) assert len(model_1.reactions[8].metabolites) == len( model_2.reactions[8].metabolites ) assert len(model_1.reactions[-1].metabolites) == len( model_2.reactions[-1].metabolites ) assert len(model_1.genes) == len(model_2.genes) # ensure they have the same solution max solution_1 = model_1.optimize() solution_2 = model_2.optimize() if solution_1.status == OPTIMAL and solution_2.status == OPTIMAL: assert abs( solution_1.objective_value - solution_2.objective_value ) == pytest.approx(0.0) else: assert solution_1.status == solution_2.status # ensure the references are correct # metabolite -> model reference assert model_1.metabolites[0]._model is model_1 assert model_2.metabolites[0]._model is model_2 # reaction -> model reference assert model_1.reactions[0]._model is model_1 assert model_2.reactions[0]._model is model_2 # gene -> model reference if model_1.genes: assert model_1.genes[0]._model is model_1 assert model_2.genes[0]._model is model_2 # extra comparisons # assert model_1.compartments == model_2.compartments # assert dict(model_1.metabolites[4].annotation) == dict( # model_2.metabolites[4].annotation) # assert dict(model_1.reactions[4].annotation) == dict( # model_2.reactions[4].annotation) # assert dict(model_1.genes[5].annotation) == dict( # model_2.genes[5].annotation) return _compare_models cobrapy-0.29.1/tests/test_io/test_annotation.py000066400000000000000000000101041467301562300216400ustar00rootroot00000000000000"""Test model annotations in SBML format.""" from pathlib import Path from typing import TYPE_CHECKING from cobra.io import read_sbml_model, write_sbml_model if TYPE_CHECKING: from cobra import Model def _check_sbml_annotations(model: "Model") -> None: """Check the annotations from the annotation.xml. Parameters ---------- model : cobra.Model The model to check annotations for. """ assert model is not None # model annotation # {'bigg.model': 'e_coli_core', 'doi': '10.1128/ecosalplus.10.2.1', # 'taxonomy': '511145'} annotation = model.annotation assert annotation is not None assert len(annotation) == 3 for key in ["bigg.model", "doi", "taxonomy"]: assert key in annotation assert annotation["bigg.model"] == "e_coli_core" assert annotation["doi"] == "10.1128/ecosalplus.10.2.1" assert annotation["taxonomy"] == "511145" # gene annotation # {'asap': 'ABE-0006162', 'ncbigene': '946368', 'uniprot': 'P33221', # 'ncbiprotein': 'gi:16129802', 'ecogene': 'EG11809'} annotation = model.genes.G1.annotation assert len(annotation) == 5 for key in ["asap", "ncbigene", "uniprot", "ncbiprotein", "ecogene"]: assert key in annotation assert annotation["asap"] == "ABE-0006162" assert annotation["ncbigene"] == "946368" assert annotation["uniprot"] == "P33221" assert annotation["ncbiprotein"] == "16129802" assert annotation["ecogene"] == "EG11809" # compartment annotation # FIXME: add tests with first class compartment model # annotation = model.compartments.c.annotation # assert len(annotation) == 1 # for key in ["bigg.compartment"]: # assert key in annotation # assert annotation["bigg.compartment"] == "c" # metabolite/species annotation # {'inchi': 'InChI=1S/C3H8O2/c1-3(5)2-4/h3-5H,2H2,1H3/t3-/m0/s1', # 'bigg.metabolite': '13dpg', 'chebi': ['CHEBI:11881', 'CHEBI:16001', # 'CHEBI:1658', 'CHEBI:20189', 'CHEBI:57604'], # 'metanetx.chemical': 'MNXM261', # 'kegg.compound': ['C00236', 'C02917'], # 'seed.compound': 'cpd00203', 'hmdb': ['HMDB62758', 'HMDB06213'], # 'biocyc': 'META:DPG'} annotation = model.metabolites.A.annotation for key in [ "inchi", "bigg.metabolite", "chebi", "metanetx.chemical", "kegg.compound", "seed.compound", "hmdb", "biocyc", ]: assert key in annotation assert ( annotation["inchi"] == "InChI=1S/C3H8O2/c1-3(5)2-4/h3-5H,2H2,1H3/t3-/m0/s1" ) # noqa: E501 # reaction annotation # {'kegg.reaction': 'R00228', 'sbo': 'SBO:0000375', # 'ec-code': '1.2.1.10', 'rhea': ['23288', '23289', '23290', '23291'], # 'metanetx.reaction': 'MNXR95210', 'bigg.reaction': 'ACALD', # 'biocyc': 'META:ACETALD-DEHYDROG-RXN'} annotation = model.reactions.R1.annotation for key in [ "kegg.reaction", "sbo", "ec-code", "rhea", "metanetx.reaction", "bigg.reaction", "biocyc", ]: assert key in annotation assert annotation["biocyc"] == "META:ACETALD-DEHYDROG-RXN" def test_read_sbml_annotations(data_directory: Path) -> None: """Test reading and writing annotations. data_directory : pathlib.Path The path to the test data directory. """ with open(data_directory / "annotation.xml", "r") as f_in: model1 = read_sbml_model(f_in) _check_sbml_annotations(model1) def test_read_write_sbml_annotations(data_directory: Path, tmp_path: Path) -> None: """Test reading and writing annotations. data_directory : pathlib.Path The path to the test data directory. tmp_path : pathlib.Path The path to the temporary test assets store. """ with open(data_directory / "annotation.xml", "r") as f_in: model1 = read_sbml_model(f_in) sbml_path = tmp_path / "test.xml" with open(sbml_path, "w") as f_out: write_sbml_model(model1, f_out) with open(sbml_path, "r") as f_in: model2 = read_sbml_model(f_in) _check_sbml_annotations(model2) cobrapy-0.29.1/tests/test_io/test_annotation_format.py000066400000000000000000000023611467301562300232160ustar00rootroot00000000000000"""Test model annotations in JSON format.""" from pathlib import Path import pytest from cobra.io import load_json_model, write_sbml_model def test_load_json_model_valid(data_directory: Path, tmp_path: Path) -> None: """Test loading a valid annotation from JSON. data_directory : pathlib.Path The path to the test data directory. tmp_path : pathlib.Path The path to the temporary test assets store. """ path_to_file = data_directory / "valid_annotation_format.json" model = load_json_model(path_to_file) expected = { "bigg.reaction": [["is", "PFK26"]], "kegg.reaction": [["is", "R02732"]], "rhea": [["is", "15656"]], } for metabolite in model.metabolites: assert metabolite.annotation == expected path_to_output = tmp_path / "valid_annotation_output.xml" write_sbml_model(model, str(path_to_output.resolve())) def test_load_json_model_invalid(data_directory: Path) -> None: """Test that loading an invalid annotation from JSON raises TypeError. data_directory : pathlib.Path The path to the test data directory. """ path = data_directory / "invalid_annotation_format.json" with pytest.raises(TypeError): load_json_model(path) cobrapy-0.29.1/tests/test_io/test_io_order.py000066400000000000000000000100451467301562300212740ustar00rootroot00000000000000"""Test functionalities of I/O in an usage order.""" import logging from operator import attrgetter from pathlib import Path from random import sample from typing import Iterable, List import pytest from cobra import DictList, Model from cobra import io as cio logger = logging.getLogger(__name__) @pytest.fixture(scope="module") def tmp_path_order(tmp_path_factory: Path) -> Path: """Temporary path for I/O order tests.""" return tmp_path_factory.mktemp("model_order") @pytest.fixture(scope="module") def minimized_shuffle(small_model: Model) -> Model: """Generate a minimal shuffled model for I/O order tests.""" model = small_model.copy() chosen = sample(list(set(model.reactions) - set(model.exchanges)), 10) new = Model("minimized_shuffle") new.add_reactions(chosen) logger.debug( f"'{new.id}' has {new.metabolites} metabolites, {new.reactions} reactions, and " f"{new.genes} genes." ) return new @pytest.fixture(scope="module") def minimized_sorted(minimized_shuffle: Model) -> Model: """Generate a minimal sorted model for I/O order tests.""" model = minimized_shuffle.copy() model.id = "minimized_sorted" model.metabolites = DictList(sorted(model.metabolites, key=attrgetter("id"))) model.genes = DictList(sorted(model.genes, key=attrgetter("id"))) model.reactions = DictList(sorted(model.reactions, key=attrgetter("id"))) return model @pytest.fixture(scope="module") def minimized_reverse(minimized_shuffle: Model) -> Model: """Generate a minimal reversed model for I/O order tests.""" model = minimized_shuffle.copy() model.id = "minimized_reverse" model.metabolites = DictList( sorted(model.metabolites, key=attrgetter("id"), reverse=True) ) model.genes = DictList(sorted(model.genes, key=attrgetter("id"), reverse=True)) model.reactions = DictList( sorted(model.reactions, key=attrgetter("id"), reverse=True) ) return model @pytest.fixture( scope="module", params=["minimized_shuffle", "minimized_reverse", "minimized_sorted"], ) def template( request: pytest.FixtureRequest, minimized_shuffle: Model, minimized_reverse: Model, minimized_sorted: Model, ) -> Model: """Return the cobra Model instances found in the current local symbol table.""" return locals()[request.param] @pytest.fixture(scope="module", params=["metabolites", "reactions", "genes"]) def attribute(request: pytest.FixtureRequest) -> str: """Return the parameter passed.""" return request.param def _get_ids(iterable: Iterable) -> List[str]: """Get IDs for elements in `iterable`.""" return [x.id for x in iterable] @pytest.mark.parametrize( "read, write, ext", [ ("read_sbml_model", "write_sbml_model", ".xml"), ("load_json_model", "save_json_model", ".json"), ("load_yaml_model", "save_yaml_model", ".yml"), ], ) def test_io_order( attribute: str, read: str, write: str, ext: str, template: Model, tmp_path_order: Path, ) -> None: """Test loading and saving of models in order. Parameters ---------- attribute : str The attribute of cobra Model to access. read : str The function name for loading model, defined as string. write : str The function name for saving model, defined as string. ext : str The extension of the file format for loading and saving model. template : cobra.Model The cobra Model instance to load and save. tmp_path_order : pathlib.Path The folder path for storing I/O order test files. """ read = getattr(cio, read) write = getattr(cio, write) file_path = tmp_path_order / f"template{ext}" write(template, str(file_path.resolve())) model = read(str(file_path.resolve())) model_elements = _get_ids(getattr(model, attribute)) template_elements = _get_ids(getattr(template, attribute)) assert len(model_elements) == len(template_elements) assert set(model_elements) == set(template_elements) assert model_elements == template_elements cobrapy-0.29.1/tests/test_io/test_json.py000066400000000000000000000064531467301562300204530ustar00rootroot00000000000000"""Test functionalities of I/O in JSON format.""" import json from pathlib import Path from typing import Any, Callable, Dict, Union import pytest from importlib_resources import files from cobra import Model from cobra import io as cio @pytest.fixture(scope="module") def json_schema_v1() -> Dict[str, Union[str, bool, Any]]: """Fixture for cobra JSON-schema.""" with files(cio).joinpath("schema_v1.json").open("r") as handle: schema_v1 = json.load(handle) return schema_v1 def test_validate_json( cobra_data_directory: Path, json_schema_v1: Dict[str, Union[str, bool, Any]] ) -> None: """Validate file according to JSON-schema.""" jsonschema = pytest.importorskip("jsonschema") with open( cobra_data_directory.joinpath("mini.json"), "r", encoding="utf-8" ) as infile: loaded = json.load(infile) assert jsonschema.validate(loaded, json_schema_v1) is None def test_load_json_model( compare_models: Callable, cobra_data_directory: Path, mini_model: Model ) -> None: """Test the reading of JSON model.""" json_model = cio.load_json_model(cobra_data_directory / "mini.json") assert compare_models(mini_model, json_model) is None json_model = cio.load_json_model(str(cobra_data_directory / "mini.json")) assert compare_models(mini_model, json_model) is None with open(cobra_data_directory / "mini.json", "r") as json_handle: json_model = cio.load_json_model(json_handle) assert compare_models(mini_model, json_model) is None def test_save_json_model( tmp_path: Path, mini_model: Model, json_schema_v1: Dict[str, Union[str, bool, Any]], ) -> None: """Test the writing of JSON model.""" jsonschema = pytest.importorskip("jsonschema") output_file = tmp_path.joinpath("mini.json") cio.save_json_model(mini_model, output_file, pretty=True) # validate against JSONSchema with open(output_file, "r") as infile: loaded = json.load(infile) assert jsonschema.validate(loaded, json_schema_v1) is None output_file.unlink() cio.save_json_model(mini_model, str(output_file), pretty=True) # validate against JSONSchema with open(output_file, "r") as infile: loaded = json.load(infile) assert jsonschema.validate(loaded, json_schema_v1) is None output_file.unlink() with output_file.open("w+") as json_outfile: cio.save_json_model(mini_model, json_outfile, pretty=True) # validate against JSONSchema json_outfile.seek(0, 0) loaded = json.load(json_outfile) assert jsonschema.validate(loaded, json_schema_v1) is None def test_reaction_bounds_json(data_directory: Path, tmp_path: Path) -> None: """Test reading and writing of model with inf bounds in JSON.""" # Path to XML file with INF bounds path_to_xml_inf_file = data_directory / "fbc_ex1.xml" model_xml_inf = cio.read_sbml_model(path_to_xml_inf_file) path_to_output = tmp_path.joinpath("fbc_ex1_json.json") # Saving model with inf bounds in json form without error cio.save_json_model(model_xml_inf, path_to_output) # Path to JSON file with INF bounds path_to_JSON_inf_file = data_directory.joinpath("JSON_with_inf_bounds.json") model_json_inf = cio.load_json_model(path_to_JSON_inf_file) assert model_json_inf.reactions[0].upper_bound == float("inf") cobrapy-0.29.1/tests/test_io/test_mat.py000066400000000000000000000272221467301562300202600ustar00rootroot00000000000000"""Test functionalities of I/O in MATLAB (.mat) format.""" from pathlib import Path from pickle import load from typing import TYPE_CHECKING, Callable import pytest from cobra.io import load_matlab_model, read_sbml_model, save_matlab_model try: import scipy except ImportError: scipy = None if TYPE_CHECKING: from cobra import Model @pytest.fixture(scope="function") def raven_model(data_directory: Path) -> "Model": """Fixture for RAVEN model.""" with open(data_directory / "raven.pickle", "rb") as infile: return load(infile) @pytest.mark.skipif(scipy is None, reason="scipy unavailable") @pytest.mark.parametrize( "fix_ref_model, fix_directory, filename", [ ("mini_model", "cobra_data_directory", "mini.mat"), ("raven_model", "data_directory", "raven.mat"), ], ) def test_load_matlab_model( compare_models: Callable, fix_ref_model: str, fix_directory: str, filename: str, request: pytest.FixtureRequest, ) -> None: """Test the reading of MAT model. Will check Path and str for each model. Parameters ---------- compare_models : Callable A callable function to compare models. fix_ref_model: str Name of reference model fixture which will be requested. fix_directory: str Name of directory fixture which will be requested. filename: str Filename to use as a parameter request: FixtureRequest Will be used to request fixtures. """ current_model = load_matlab_model( (request.getfixturevalue(fix_directory) / filename).resolve() ) assert compare_models(request.getfixturevalue(fix_ref_model), current_model) is None current_model = load_matlab_model( str((request.getfixturevalue(fix_directory) / filename).resolve()) ) assert compare_models(request.getfixturevalue(fix_ref_model), current_model) is None with request.getfixturevalue(fix_directory).joinpath(filename).open("rb") as file_h: current_model = load_matlab_model(file_h) assert ( compare_models(request.getfixturevalue(fix_ref_model), current_model) is None ) # @pytest.mark.xfail(reason="localPath not supported yet") @pytest.mark.skipif(scipy is None, reason="scipy unavailable") @pytest.mark.parametrize( "fix_ref_model, filename", [("mini_model", "mini.mat"), ("raven_model", "raven.mat")], ) def test_save_matlab_model(tmp_path: Path, fix_ref_model, filename, request) -> None: """Test the writing of MAT model. Parameters ---------- tmp_path : pathlib.Path The path to the temporary test assets store. fix_ref_model: str Name of reference model fixture which will be requested. filename: str Filename to use as a parameter request: FixtureRequest Will be used to request fixtures. """ output_file = tmp_path / filename save_matlab_model( request.getfixturevalue(fix_ref_model), str(output_file.resolve()) ) assert output_file.exists() output_file.unlink() save_matlab_model(request.getfixturevalue(fix_ref_model), output_file.resolve()) assert output_file.exists() output_file.unlink() with output_file.open("wb") as file_h: save_matlab_model(request.getfixturevalue(fix_ref_model), file_h) assert output_file.exists() @pytest.mark.skipif(scipy is None, reason="scipy unavailable") def test_large_bounds(tmp_path: Path, model: "Model") -> None: """Verify that mat bounds don't get broken by the config defaults. Parameters ---------- tmp_path : pathlib.Path The path to the temporary test assets store. model : cobra.Model The "textbook" model. """ model.reactions[0].bounds = -1e6, 1e6 filepath = tmp_path / "model.mat" save_matlab_model(model, filepath.resolve()) read = load_matlab_model(filepath.resolve()) assert read.reactions[0].bounds == (-1e6, 1e6) @pytest.mark.skipif(scipy is None, reason="scipy unavailable") @pytest.mark.parametrize( "fix_directory, filename", [("cobra_data_directory", "mini.mat"), ("data_directory", "raven.mat")], ) def test_read_rewrite_matlab_model( compare_models: Callable, tmp_path: Path, fix_directory: str, filename: str, request: pytest.FixtureRequest, ) -> None: """Verify that rewritten matlab model is identical to original. Parameters ---------- compare_models : Callable A callable to compare models. tmp_path : pathlib.Path The path to the temporary test assets store. fix_directory: str Name of directory fixture which will be requested. filename: str Filename to use as a parameter request: FixtureRequest Will be used to request fixtures. """ current_model = load_matlab_model(request.getfixturevalue(fix_directory) / filename) output_file = tmp_path.joinpath(filename) save_matlab_model(current_model, output_file) mat_model_reload = load_matlab_model(output_file) assert compare_models(current_model, mat_model_reload) is None @pytest.mark.skipif(scipy is None, reason="scipy unavailable") @pytest.mark.parametrize( "dirname, xml_file", [ ("data_directory", "e_coli_core.xml"), ("cobra_data_directory", "salmonella.xml.gz"), ("cobra_data_directory", "mini_cobra.xml"), ("data_directory", "mini_fbc2.xml"), ], ) # When using a better comparison function, can run test on # "annotation.xml", "example_notes.xml", "fbc_ex1.xml", "fbc_ex2.xml", "validation.xml" # "valid_annotation_output.xml" has reaction annotations in a metabolite, so they would # be thrown out by matlab def test_compare_xml_to_written_matlab_model( compare_models: Callable, tmp_path: Path, dirname: str, xml_file: str, request: pytest.FixtureRequest, ) -> None: """Verify that xml rewritten as mat file is written and read correctly. Parameters ---------- compare_models : Callable A callable to compare models. tmp_path : pathlib.Path The path to the temporary test assets store. xml_file : str The name of the XML file to compare against. """ xml_model = read_sbml_model(request.getfixturevalue(dirname) / xml_file) mat_output_file = tmp_path / xml_file.replace(".xml", ".mat") save_matlab_model(xml_model, mat_output_file) mat_model = load_matlab_model(mat_output_file) assert compare_models(xml_model, mat_model) is None @pytest.mark.skipif(scipy is None, reason="scipy unavailable") def test_fail_on_problematic_compartments(data_directory: Path) -> None: """Test that mat import will fail if there are problems in compartments. Parameters ---------- data_directory : pathlib.Path The path to the test data directory. """ with pytest.raises(IOError): # AntCore does not have defined compartments load_matlab_model(data_directory / "AntCore.mat") with pytest.raises(IOError): # Ec_iAF1260_flux1 has underscore in compartment names which is not allowed load_matlab_model(data_directory / "Ec_iAF1260_flux1.mat") @pytest.mark.skipif(scipy is None, reason="scipy unavailable") def test_mat_model_with_long_compartment_ids( compare_models: Callable, data_directory: Path, tmp_path: Path ) -> None: """Test that long compartment IDs like "luSI" are correctly loaded. Parameters ---------- compare_models : Callable A callable to compare models. data_directory : pathlib.Path The path to the test data directory. tmp_path : pathlib.Path The path to the temporary test assets store. """ model_compartments = load_matlab_model(data_directory / "compartments.mat") assert model_compartments.compartments == { "csf": "csf", "bcK": "bcK", "a": "a", "luSI": "luSI", "luLI": "luLI", "luP": "luP", "aL": "aL", "fe": "fe", } assert len(model_compartments.metabolites) == 8 assert len(model_compartments.reactions) == 15 for met in model_compartments.metabolites: assert met.annotation == { "bigg.metabolite": ["glc__D"], "cas": ["50-99-7"], "kegg.compound": ["C00031"], "pubchem.substance": ["3333"], } output_file = tmp_path / "compartments.mat" save_matlab_model(model_compartments, output_file.resolve()) model_compartments_reloaded = load_matlab_model(output_file.resolve()) assert compare_models(model_compartments, model_compartments_reloaded) is None @pytest.mark.skipif(scipy is None, reason="scipy unavailable") def test_mat_model_with_no_genes( compare_models: Callable, data_directory: Path, tmp_path: Path ) -> None: """Test that a model with no genes is loaded and reloaded correctly. Parameters ---------- compare_models : Callable A callable to compare models. data_directory : pathlib.Path The path to the test data directory. tmp_path : pathlib.Path The path to the temporary test assets store. """ model_no_genes = load_matlab_model( data_directory / "cardiac_mit_glcuptake_atpmax.mat" ) assert not len(model_no_genes.genes) output_file = tmp_path / "cardiac_mit_glcuptake_atpmax.mat" save_matlab_model(model_no_genes, output_file.resolve()) model_no_genes_reloaded = load_matlab_model(output_file.resolve()) assert compare_models(model_no_genes, model_no_genes_reloaded) is None @pytest.mark.skipif(scipy is None, reason="scipy unavailable") def test_mat_model_wrong_caps( compare_models: Callable, data_directory: Path, cobra_data_directory: Path ) -> None: """Check that wrong capitalization in matlab field names is processed correctly. See https://gist.github.com/akaviaLab/3dcb0eed6563a9d3d1e07198337300ac to create it again when needed. Parameters ---------- compare_models : Callable A callable to compare models. data_directory : pathlib.Path The path to the test data directory. """ mat_model = load_matlab_model((cobra_data_directory / "mini.mat").resolve()) mat_wrong_caps_model = load_matlab_model( (data_directory / "mini_wrong_key_caps.mat").resolve() ) assert compare_models(mat_model, mat_wrong_caps_model) is None assert mat_wrong_caps_model.reactions.get_by_id("LDH_D").annotation == { "rhea": ["16369", "16370", "16371", "16372"], "metanetx.reaction": ["MNXR101037"], "kegg.reaction": ["R00704"], "bigg.reaction": ["LDH_D"], "ec-code": ["1.1.1.28"], "biocyc": ["META:DLACTDEHYDROGNAD-RXN"], "sbo": ["SBO:0000375"], } for rxn in mat_model.reactions.list_attr("id"): assert ( mat_wrong_caps_model.reactions.get_by_id(rxn).annotation == mat_model.reactions.get_by_id(rxn).annotation ) assert mat_wrong_caps_model.metabolites.get_by_id("pyr_c").annotation == { "seed.compound": ["cpd00020"], "unipathway.compound": ["UPC00022"], "lipidmaps": ["LMFA01060077"], "reactome": ["REACT_113557", "REACT_389680", "REACT_29398"], "biocyc": ["PYRUVATE"], "chebi": [ "CHEBI:15361", "CHEBI:14987", "CHEBI:8685", "CHEBI:32816", "CHEBI:45253", "CHEBI:26466", "CHEBI:26462", ], "pubchem.substance": ["3324"], "bigg.metabolite": ["pyr"], "cas": ["127-17-3"], "hmdb": ["HMDB00243"], "kegg.compound": ["C00022"], } for met in mat_model.metabolites.list_attr("id"): assert ( mat_wrong_caps_model.metabolites.get_by_id(met).annotation == mat_model.metabolites.get_by_id(met).annotation ) cobrapy-0.29.1/tests/test_io/test_notes.py000066400000000000000000000027071467301562300206300ustar00rootroot00000000000000"""Test proper reading of SBML notes.""" from pathlib import Path from cobra import Metabolite, Model, Reaction from cobra.io import read_sbml_model, write_sbml_model def test_notes(tmp_path: Path) -> None: """Test if model notes are written in SBML. Parameters ---------- tmp_path : pathlib.Path The path to the temporary test assets store. """ path_to_file = tmp_path / "model_notes.xml" # making a minimal cobra model to test notes model = Model("test_notes_model") model.notes["Remark"] = "...Model Notes..." met = Metabolite("pyr_c", compartment="c") model.add_metabolites([met]) met.notes["Remark"] = "Note with \n newline" rxn = Reaction("R_ATPM") model.add_reactions([rxn]) rxn.notes["Remark"] = "What about me?" model.objective_direction = "max" model.objective = rxn write_sbml_model(model, str(path_to_file.resolve())) # reading the model back model_after_reading = read_sbml_model(str(path_to_file.resolve())) met_after_reading = model_after_reading.metabolites.get_by_id("pyr_c") reaction_after_reading = model_after_reading.reactions.get_by_id("R_ATPM") # checking if notes are written to model assert model_after_reading.notes["Remark"] == "...Model Notes..." # checking notes for metabolite and reaction assert met_after_reading.notes["Remark"] == "Note with \n newline" assert reaction_after_reading.notes["Remark"] == "What about me?" cobrapy-0.29.1/tests/test_io/test_pickle.py000066400000000000000000000020331467301562300207370ustar00rootroot00000000000000"""Test data storage and recovery using pickle.""" from pathlib import Path from pickle import dump, load from typing import Callable import pytest from cobra import Model @pytest.mark.parametrize("load_function", [load]) def test_read_pickle( compare_models: Callable, data_directory: Path, mini_model: Model, load_function: Callable, ): """Test the reading of model from pickle.""" if load_function is None: pytest.skip() with open(data_directory.joinpath("mini.pickle"), "rb") as infile: pickle_model = load_function(infile) assert compare_models(mini_model, pickle_model) is None @pytest.mark.parametrize("dump_function", [dump]) def test_write_pickle(tmp_path: Path, mini_model: Model, dump_function: Callable): """Test the writing of model to pickle.""" if dump_function is None: pytest.skip() output_file = tmp_path.joinpath("mini.pickle") with open(str(output_file), "wb") as outfile: dump_function(mini_model, outfile) assert output_file.exists() cobrapy-0.29.1/tests/test_io/test_sbml.py000066400000000000000000000613771467301562300204450ustar00rootroot00000000000000"""Testing SBML functionality based on libsbml.""" from collections import namedtuple from os import unlink from os.path import join, split from pathlib import Path from pickle import load from tempfile import gettempdir from typing import List, Tuple import pytest from _pytest.fixtures import SubRequest import cobra from cobra import Model from cobra.io import read_sbml_model, validate_sbml_model, write_sbml_model config = cobra.Configuration() # for default bounds try: import jsonschema except ImportError: jsonschema = None # ---------------------------------- # Definition of SBML files to test # ---------------------------------- IOTrial = namedtuple( "IOTrial", [ "name", "reference_file", "test_file", "read_function", "write_function", "validation_function", ], ) trials: List[IOTrial] = [ IOTrial( "fbc2", "mini.pickle", "mini_fbc2.xml", read_sbml_model, write_sbml_model, validate_sbml_model, ), IOTrial( "fbc2Gz", "mini.pickle", "mini_fbc2.xml.gz", read_sbml_model, write_sbml_model, None, ), IOTrial( "fbc2Bz2", "mini.pickle", "mini_fbc2.xml.bz2", read_sbml_model, write_sbml_model, None, ), IOTrial( "fbc1", "mini.pickle", "mini_fbc1.xml", read_sbml_model, write_sbml_model, None ), IOTrial("cobra", None, "mini_cobra.xml", read_sbml_model, write_sbml_model, None), ] trial_names: list = [node.name for node in trials] @pytest.mark.parametrize("trial", trials) def test_validate(trial: IOTrial, data_directory: Path) -> None: """Test validation function. Parameters ---------- trial: IOTrial Which model trial to check. data_directory: Path Directory where the data is. """ if trial.validation_function is None: pytest.skip("not implemented") test_file = data_directory / trial.test_file trial.validation_function(test_file) class TestCobraIO: """Tests the read and write functions.""" @classmethod def compare_models(cls, name: str, model1: Model, model2: Model) -> None: """Compare two models. name, str name of models to compare. model1: Model First model to compare. model2: Model Second model to compare. """ print(name) assert len(model1.reactions) == len(model2.reactions) assert len(model1.metabolites) == len(model2.metabolites) assert model1.objective.direction == model2.objective.direction for attr in ( "id", "name", "lower_bound", "upper_bound", "objective_coefficient", "gene_reaction_rule", ): assert getattr(model1.reactions[0], attr) == getattr( model2.reactions[0], attr ) assert getattr(model1.reactions[5], attr) == getattr( model2.reactions[5], attr ) assert getattr(model1.reactions[-1], attr) == getattr( model2.reactions[-1], attr ) for attr in ("id", "name", "compartment", "formula", "charge"): assert getattr(model1.metabolites[0], attr) == getattr( model2.metabolites[0], attr ) assert getattr(model1.metabolites[5], attr) == getattr( model2.metabolites[5], attr ) assert getattr(model1.metabolites[-1], attr) == getattr( model2.metabolites[-1], attr ) assert len(model1.reactions[0].metabolites) == len( model2.reactions[0].metabolites ) assert len(model1.reactions[8].metabolites) == len( model2.reactions[8].metabolites ) assert len(model1.reactions[-1].metabolites) == len( model2.reactions[-1].metabolites ) assert len(model1.genes) == len(model2.genes) # ensure they have the same solution max solution1 = model1.optimize() solution2 = model2.optimize() assert ( solution1.status == "infeasible" and solution2.status == "infeasible" ) or abs(solution1.objective_value - solution2.objective_value) < 0.001 # ensure the references are correct assert model2.metabolites[0]._model is model2 assert model2.reactions[0]._model is model2 assert model2.genes[0]._model is model2 @classmethod def extra_comparisons(cls, name: str, model1: Model, model2: Model) -> None: """Compare additional features of the model. name, str name of models to compare. model1: Model First model to compare. model2: Model Second model to compare. """ print(name) assert model1.compartments == model2.compartments # FIXME: problems of duplicate annotations in test data # ('cas': ['56-65-5', '56-65-5']) # assert dict(model1.metabolites[4].annotation) == dict( # model2.metabolites[4].annotation) d1 = model1.reactions[4].annotation d2 = model2.reactions[4].annotation assert list(d1.keys()) == list(d2.keys()) for k in d1: assert set(d1[k]) == set(d2[k]) assert dict(model1.reactions[4].annotation) == dict( model2.reactions[4].annotation ) assert dict(model1.genes[5].annotation) == dict(model2.genes[5].annotation) for attr in ("id", "name"): assert getattr(model1.genes[0], attr) == getattr(model2.genes[0], attr) assert getattr(model1.genes[10], attr) == getattr(model2.genes[10], attr) assert getattr(model1.genes[-1], attr) == getattr(model2.genes[-1], attr) def test_read_1(self, io_trial: Tuple[str, Model, Model, Model]) -> None: """Read the first model from a processed IOTrial via io_trial(). io_trial: [str, Model, Model, Model] A tuple containing name, reference_model, test_model, rewritten_model. """ name, reference_model, test_model, _ = io_trial if name in ["fbc1"]: pytest.xfail("not supported") if reference_model: self.compare_models(name, reference_model, test_model) def test_read_2(self, io_trial: Tuple[str, Model, Model, Model]) -> None: """Read the second model from a processed IOTrial via io_trial(). io_trial: [str, Model, Model, Model] A tuple containing name, reference_model, test_model, rewritten_model. """ name, reference_model, test_model, _ = io_trial if name in ["fbc1", "mat", "cobra", "raven-mat"]: pytest.xfail("not supported") if reference_model: self.extra_comparisons(name, reference_model, test_model) def test_write_1(self, io_trial: Tuple[str, Model, Model, Model]) -> None: """Test writing the first model from a processed IOTrial via io_trial(). io_trial: [str, Model, Model, Model] A tuple containing name, reference_model, test_model, rewritten_model. """ name, _, test_model, reread_model = io_trial if name in ["fbc1", "raven-mat"]: pytest.xfail("not supported") self.compare_models(name, test_model, reread_model) def test_write_2(self, io_trial: Tuple[str, Model, Model, Model]) -> None: """Test writing the second model from a processed IOTrial via io_trial(). io_trial: [str, Model, Model, Model] A tuple containing name, reference_model, test_model, rewritten_model. """ name, _, test_model, reread_model = io_trial if name in ["fbc1", "mat", "cobra", "raven-mat"]: pytest.xfail("not supported") self.extra_comparisons(name, test_model, reread_model) @pytest.fixture(scope="module", params=trials, ids=trial_names) def io_trial( request: SubRequest, data_directory: Path ) -> Tuple[str, Model, Model, Model]: """Read reference model, test model, write test model and reread it. Parameters ---------- request: IOTrail data_directory: Path Directory where the data is. This function will read the reference model, the test model. It will then write the test model based on the write_function() in IOTrial, and read the written test model using read_function in IOTrail. This can be used to compare the original model, test model and the written and reread model. Returns ------- Tuple: str, Model, Model, Model Name, original model as read by read_function(), test model as read by read_function(), test model as written by write_function() and reread by read_function(). """ reference_model = None if request.param.reference_file: with open( data_directory.joinpath(request.param.reference_file), "rb" ) as infile: reference_model = load(infile) test_model = request.param.read_function(data_directory / request.param.test_file) test_output_filename = join(gettempdir(), split(request.param.test_file)[-1]) # test writing the model within a context with a non-empty stack with test_model: test_model.objective = test_model.objective request.param.write_function(test_model, test_output_filename) reread_model = request.param.read_function(test_output_filename) unlink(test_output_filename) return request.param.name, reference_model, test_model, reread_model def test_filehandle(data_directory: Path, tmp_path: Path) -> None: """Test reading and writing to file handle. Parameters ---------- data_directory: Path Directory where the data is. tmp_path: Path Directory to use for temporary data. """ with data_directory.joinpath("mini_fbc2.xml").open("r") as f_in: model1 = read_sbml_model(f_in) assert model1 is not None sbml_path = tmp_path / "test.xml" with sbml_path.open("w") as f_out: write_sbml_model(model1, f_out) with sbml_path.open("r") as f_in: model2 = read_sbml_model(f_in) TestCobraIO.compare_models(name="filehandle", model1=model1, model2=model2) def test_from_sbml_string(data_directory: Path) -> None: """Test reading from SBML string. Parameters ---------- data_directory: Path Directory where the data is. """ sbml_path = data_directory / "mini_fbc2.xml" model1 = read_sbml_model(sbml_path.read_text()) model2 = read_sbml_model(sbml_path) TestCobraIO.compare_models(name="read from string", model1=model1, model2=model2) @pytest.mark.skip(reason="Model history currently not written") def test_model_history(tmp_path: Path) -> None: """Testing reading and writing of ModelHistory. Parameters ---------- tmp_path: Path Directory to use for temporary data. """ model = Model("test") model._sbml = { "creators": [ { "familyName": "Mustermann", "givenName": "Max", "organisation": "Muster University", "email": "muster@university.com", } ] } sbml_path = tmp_path / "test.xml" with open(sbml_path, "w") as f_out: write_sbml_model(model, f_out) with open(sbml_path, "r") as f_in: model2 = read_sbml_model(f_in) assert "creators" in model2._sbml assert len(model2._sbml["creators"]) == 1 c = model2._sbml["creators"][0] assert c["familyName"] == "Mustermann" assert c["givenName"] == "Max" assert c["organisation"] == "Muster University" assert c["email"] == "muster@university.com" def test_groups(data_directory: Path, tmp_path: Path) -> None: """Testing reading and writing of groups. Parameters ---------- data_directory: Path Directory where the data is. tmp_path: Path Directory to use for temporary data. """ sbml_path = data_directory / "e_coli_core.xml" model = read_sbml_model(sbml_path) assert model.groups is not None assert len(model.groups) == 10 g1 = model.groups[0] assert len(g1.members) == 6 temp_path = tmp_path / "test.xml" with open(temp_path, "w") as f_out: write_sbml_model(model, f_out) with open(temp_path, "r") as f_in: model2 = read_sbml_model(f_in) assert model2.groups is not None assert len(model2.groups) == 10 g1 = model2.groups[0] assert len(g1.members) == 6 def test_missing_flux_bounds1(data_directory: Path) -> None: """Test missing flux bounds in an incorrect model. Parameters ---------- data_directory: Path Directory where the data is. """ sbml_path = data_directory / "annotation.xml" with open(sbml_path, "r") as f_in: # missing flux bounds are set to cobra.configuration.bounds # noinspection PyTupleAssignmentBalance model, errors = validate_sbml_model(f_in, set_missing_bounds=True) r1 = model.reactions.R1 assert r1.lower_bound == config.lower_bound assert r1.upper_bound == config.upper_bound def test_missing_flux_bounds2(data_directory: Path) -> None: """Test missing flux bounds set to [-INF, INF]. Parameters ---------- data_directory: Path Directory where the data is. """ sbml_path = data_directory / "annotation.xml" with open(sbml_path, "r") as f_in: # missing flux bounds are set to [-INF, INF] # noinspection PyTupleAssignmentBalance model, errors = validate_sbml_model(f_in, set_missing_bounds=False) r1 = model.reactions.R1 assert r1.lower_bound == config.lower_bound assert r1.upper_bound == config.upper_bound def test_validate2(data_directory: Path) -> None: """Test the validation code. Parameters ---------- data_directory: Path Directory where the data is. """ sbml_path = data_directory / "mini_fbc2.xml" with open(sbml_path, "r") as f_in: # noinspection PyTupleAssignmentBalance model1, errors = validate_sbml_model(f_in, check_modeling_practice=True) assert model1 assert errors assert len(errors["SBML_WARNING"]) == 0 def test_validation_warnings(data_directory: Path) -> None: """Test the validation warnings. Parameters ---------- data_directory: Path Directory where the data is. """ sbml_path = data_directory / "validation.xml" with open(sbml_path, "r") as f_in: # noinspection PyTupleAssignmentBalance model1, errors = validate_sbml_model(f_in, check_modeling_practice=True) assert model1 assert errors assert len(errors["COBRA_WARNING"]) == 3 assert "No objective in listOfObjectives" in errors["COBRA_WARNING"] def test_infinity_bounds(data_directory: Path, tmp_path: Path) -> None: """Test infinity bound example. Parameters ---------- data_directory: Path Directory where the data is. tmp_path: Path Directory to use for temporary data. """ sbml_path = data_directory / "fbc_ex1.xml" model = read_sbml_model(sbml_path) # check that simulation works solution = model.optimize() assert solution is not None # check that values are set r = model.reactions.get_by_id("EX_X") assert r.lower_bound == -float("Inf") assert r.upper_bound == float("Inf") temp_path = tmp_path / "test.xml" with open(temp_path, "w") as f_out: write_sbml_model(model, f_out) with open(temp_path, "r") as f_in: model2 = read_sbml_model(f_in) r = model2.reactions.get_by_id("EX_X") assert r.lower_bound == -float("Inf") assert r.upper_bound == float("Inf") def test_boundary_conditions(data_directory: Path) -> None: """Test infinity bound example. Parameters ---------- data_directory: Path Directory where the data is. """ sbml_path1 = data_directory / "fbc_ex1.xml" model1 = read_sbml_model(sbml_path1) sol1 = model1.optimize() # model with species boundaryCondition==True sbml_path2 = data_directory / "fbc_ex2.xml" model2 = read_sbml_model(sbml_path2) sol2 = model2.optimize() r = model2.reactions.get_by_id("EX_X") assert r.lower_bound == config.lower_bound assert r.upper_bound == config.upper_bound assert sol1.objective_value == sol2.objective_value def test_bounds_on_write(data_directory: Path, tmp_path: Path) -> None: """Test infinity bound example. Parameters ---------- data_directory: Path Directory where the data is. """ sbml_path1 = data_directory / "fbc_ex1.xml" model1 = read_sbml_model(sbml_path1) r_x = model1.reactions.get_by_id("EX_X") r_y = model1.reactions.get_by_id("EX_Ac") r_x.bounds = (config.lower_bound - 1000, config.upper_bound + 1000) assert r_x.lower_bound == config.lower_bound - 1000 assert r_x.upper_bound == config.upper_bound + 1000 # Global min/max bounds for other reactions should not change before & after write! r_y.bounds = (config.lower_bound, config.upper_bound) assert r_y.lower_bound == config.lower_bound assert r_y.upper_bound == config.upper_bound sbml_path = tmp_path / "test.xml" with open(sbml_path, "w") as f_out: write_sbml_model(model1, f_out) with open(sbml_path, "r") as f_in: model2 = read_sbml_model(f_in) r2_x = model2.reactions.get_by_id("EX_X") r2_y = model2.reactions.get_by_id("EX_Ac") assert r2_x.lower_bound == r_x.lower_bound assert r2_x.upper_bound == r_x.upper_bound assert r2_y.lower_bound == r_y.lower_bound # before fix #1300, this would fail assert r2_y.upper_bound == r_y.upper_bound # before fix #1300, this would fail def test_gprs(large_model: Model, tmp_path: Path) -> None: """Test that GPRs are written and read correctly. Parameters ---------- large_model: Model Model to test gprs on. tmp_path: Path Directory to use for temporary data. """ model1 = large_model sbml_path = tmp_path / "test.xml" with open(sbml_path, "w") as f_out: write_sbml_model(model1, f_out) with open(sbml_path, "r") as f_in: model2 = read_sbml_model(f_in) for r1 in model1.reactions: rid = r1.id r2 = model2.reactions.get_by_id(rid) gpr1 = r1.gene_reaction_rule gpr2 = r2.gene_reaction_rule assert gpr1 == gpr2 def test_identifiers_annotation() -> None: """Test annotation with identifiers.""" from cobra.io.sbml import _parse_annotation_info for uri in [ "http://identifiers.org/chebi/CHEBI:000123", "https://identifiers.org/chebi/CHEBI:000123", "http://identifiers.org/CHEBI:000123", "https://identifiers.org/CHEBI:000123", ]: data = _parse_annotation_info(uri) assert data assert data[0] == "chebi" assert data[1] == "CHEBI:000123" for uri in [ "http://identifiers.org/taxonomy/9602", "https://identifiers.org/taxonomy/9602", "http://identifiers.org/taxonomy:9602", "https://identifiers.org/taxonomy:9602", ]: data = _parse_annotation_info(uri) assert data assert data[0] == "taxonomy" assert data[1] == "9602" for uri in [ "http://identifier.org/taxonomy/9602", "https://test.com", ]: data = _parse_annotation_info(uri) assert data is None def test_smbl_with_notes(data_directory: Path, tmp_path: Path) -> None: """Test that NOTES in the RECON 2.2 style are written and read correctly. Parameters ---------- data_directory: Path Directory where the data is. tmp_path: Path Directory to use for temporary data. """ sbml_path = join(data_directory, "example_notes.xml") model = read_sbml_model(sbml_path) assert model.metabolites is not None metabolite_notes = { "2hb_e": {"CHARGE": "-1", "FORMULA": "C4H7O3", "SMILES": "CCC(O)C(O)=O"}, "nad_e": { "CHARGE": "-1", "FORMULA": "C21H26N7O14P2", "SMILES": "NC(=O)c1ccc[n+](c1)[C@@H]1O[C@H](COP([O-])(" "=O)OP([O-])(=O)OC[C@H]2O[C@H]([C@H](O)[" "C@@H]2O)n2cnc3c(N)ncnc23)[C@@H](O)[C@H]1O", }, "h_e": {"CHARGE": "1", "FORMULA": "H", "SMILES": "[1H+]"}, "2obut_e": {"CHARGE": "-1", "FORMULA": "C4H5O3", "SMILES": "CCC(=O)C([O-])=O"}, "nadh_e": { "CHARGE": "-2", "FORMULA": "C21H27N7O14P2", "SMILES": "NC(=O)C1=CN(C=CC1)[C@@H]1O[C@H](COP([O-])(" "=O)OP([O-])(=O)OC[C@H]2O[C@H]([C@H](O)[" "C@@H]2O)n2cnc3c(N)ncnc23)[C@@H](O)[C@H]1O", }, } metabolite_annotations = { "2hb_e": { "sbo": "SBO:0000247", "inchi": "InChI=1S/C4H8O3/c1-2-3(5)4(6)7/h3,5H,2H2,1H3,(H,6,7)", "chebi": "CHEBI:1148", }, "nad_e": { "sbo": "SBO:0000247", "inchi": "InChI=1S/C21H27N7O14P2/c22-17-12-19(" "25-7-24-17)28(8-26-12)21-16(32)14(30)11(" "41-21)6-39-44(36,37)42-43(34,35)38-5-10-13(29)15(" "31)20(40-10)27-3-1-2-9(4-27)18(" "23)33/h1-4,7-8,10-11,13-16,20-21,29-32H,5-6H2," "(H5-,22,23,24,25,33,34,35,36,37)/p-1/t10-," "11-,13-,14-,15-,16-,20-,21-/m1/s1", "chebi": "CHEBI:57540", }, "h_e": { "sbo": "SBO:0000247", "inchi": "InChI=1S/p+1/i/hH", "chebi": "CHEBI:24636", }, "2obut_e": { "sbo": "SBO:0000247", "inchi": "InChI=1S/C4H6O3/c1-2-3(5)4(6)7/h2H2,1H3,(H,6,7)/p-1", "chebi": "CHEBI:16763", }, "nadh_e": { "sbo": "SBO:0000247", "inchi": "InChI=1S/C21H29N7O14P2/c22-17-12-19(" "25-7-24-17)28(8-26-12)21-16(32)14(30)11(" "41-21)6-39-44(36,37)42-43(34,35)38-5-10-13(" "29)15(31)20(40-10)27-3-1-2-9(4-27)18(" "23)33/h1,3-4,7-8,10-11,13-16,20-21,29-32H,2," "5-6H2,(H2,23,33)(H,34,35)(H,36,37)(H2,22,24," "25)/p-2/t10-,11-,13-,14-,15-,16-,20-,21-/m1/s1", "chebi": "CHEBI:57945", }, } reaction_notes = { "CONFIDENCE_LEVEL": "4", "NOTES": "NCD", "SUBSYSTEM": "Propanoate metabolism", "GENE_ASSOCIATION": "(HGNC:8546 and HGNC:8548) or (HGNC:8547 and HGNC:8548)", } reaction_annotations = { "sbo": "SBO:0000176", "ec-code": "1.1.1.27", "pubmed": ["10108", "21765"], } for met_id in metabolite_notes: assert model.metabolites.has_id(met_id) for note_key in metabolite_notes[met_id].keys(): assert note_key in model.metabolites.get_by_id(met_id).notes assert ( metabolite_notes[met_id][note_key] == model.metabolites.get_by_id(met_id).notes[note_key] ) for annotation_key in metabolite_annotations[met_id].keys(): assert annotation_key in model.metabolites.get_by_id(met_id).annotation assert ( metabolite_annotations[met_id][annotation_key] == model.metabolites.get_by_id(met_id).annotation[annotation_key] ) for note_key in reaction_notes.keys(): assert note_key in model.reactions[0].notes.keys() assert reaction_notes[note_key] == model.reactions[0].notes[note_key] assert ( model.reactions[0].gene_reaction_rule == "(HGNC:8546 and HGNC:8548) or " "(HGNC:8547 and HGNC:8548)" ) assert len(model.groups) == 1 for annotation_key in reaction_annotations.keys(): assert annotation_key in model.reactions[0].annotation.keys() assert ( reaction_annotations[annotation_key] == model.reactions[0].annotation[annotation_key] ) def test_stable_gprs(data_directory: Path, tmp_path: Path) -> None: """Test that GPRs are written correctly after manual changes. Parameters ---------- data_directory: Path Directory where the data is. tmp_path: Path Directory to use for temporary data. """ mini = read_sbml_model(join(data_directory, "mini_fbc2.xml")) mini.reactions.GLCpts.gene_reaction_rule = "((b2415 and b2417)or (b2416))" fixed = join(str(tmp_path), "fixed_gpr.xml") write_sbml_model(mini, fixed) fixed_model = read_sbml_model(fixed) assert ( fixed_model.reactions.GLCpts.gene_reaction_rule == "(b2415 and b2417) or b2416" ) def test_history(data_directory: Path) -> None: """Test that the history is read from the model.""" mini = read_sbml_model(join(data_directory, "mini_history.xml")) assert "creators" in mini._sbml assert "organisation" in mini._sbml["creators"][0] assert "created" in mini._sbml assert isinstance(mini._sbml["created"], str) assert "2022" in mini._sbml["created"] cobrapy-0.29.1/tests/test_io/test_web/000077500000000000000000000000001467301562300176755ustar00rootroot00000000000000cobrapy-0.29.1/tests/test_io/test_web/test_load.py000066400000000000000000000102231467301562300222230ustar00rootroot00000000000000"""Test model loading from local and remote model repositores.""" import gzip from pathlib import Path from typing import TYPE_CHECKING, Callable from unittest.mock import Mock import pytest from cobra import Configuration from cobra.io import BiGGModels, BioModels, load_model if TYPE_CHECKING: from pytest_mock import MockerFixture from cobra import Model @pytest.fixture(scope="module") def mini_sbml(cobra_data_directory: Path) -> bytes: """Provide a gzip-compressed SBML document.""" with (cobra_data_directory / "mini_cobra.xml").open(mode="rb") as handle: return gzip.compress(handle.read()) @pytest.fixture def bigg_models(mini_sbml: bytes, mocker: "MockerFixture") -> Mock: """Provide a mocked BiGG Models repository interface.""" result = mocker.Mock(spec_set=BiGGModels) result.get_sbml.return_value = mini_sbml return result @pytest.fixture def biomodels(mini_sbml: bytes, mocker: "MockerFixture") -> Mock: """Provide a mocked BioModels repository interface.""" result = mocker.Mock(spec_set=BioModels) result.get_sbml.return_value = mini_sbml return result def test_bigg_access(bigg_models: Mock) -> None: """Test that SBML would be retrieved from the BiGG Models repository. Parameters ---------- bigg_models : unittest.mock.Mock The mocked object for BiGG model respository. """ load_model("e_coli_core", cache=False, repositories=[bigg_models]) bigg_models.get_sbml.assert_called_once_with(model_id="e_coli_core") def test_biomodels_access(biomodels: Mock) -> None: """Test that SBML would be retrieved from the BioModels repository. Parameters ---------- biomodels : unittest.mock.Mock The mocked object for BioModels model respository. """ load_model("BIOMD0000000633", cache=False, repositories=[biomodels]) biomodels.get_sbml.assert_called_once_with(model_id="BIOMD0000000633") def test_unknown_model() -> None: """Expect that a not found error is raised (e2e).""" with pytest.raises(RuntimeError): load_model("MODELWHO?", cache=False) @pytest.mark.parametrize( "model_id, num_metabolites, num_reactions", [("e_coli_core", 72, 95), ("BIOMD0000000633", 50, 35)], ) def test_remote_load(model_id: str, num_metabolites: int, num_reactions: int) -> None: """Test that sample models can be loaded from remote repositories (e2e). Parameters ---------- model_id : str The ID of the model. num_metabolites : int The total number of metabolites in the model having ID `model_id`. num_reactions : int The total number of reactions in the model having ID `model_id`. """ model = load_model(model_id, cache=False) assert len(model.metabolites) == num_metabolites assert len(model.reactions) == num_reactions def test_cache( monkeypatch: pytest.MonkeyPatch, tmp_path: Path, bigg_models: Mock, biomodels: Mock ) -> None: """Test that remote models are properly cached. Parameters ---------- monkeypatch : pytest.MonkeyPatch The monkeypatch-ing object. tmp_path : pathlib.Path The path to the temporary test assets store. bigg_models : Mock The mocked object for BiGG model respository. biomodels : unittest.mock.Mock The mocked object for BioModels model respository. """ config = Configuration() monkeypatch.setattr(config, "cache_directory", tmp_path) remote_model = load_model("e_coli_core") cached_model = load_model("e_coli_core", repositories=[bigg_models, biomodels]) bigg_models.get_sbml.assert_not_called() biomodels.get_sbml.assert_not_called() assert len(cached_model.metabolites) == len(remote_model.metabolites) assert len(cached_model.reactions) == len(remote_model.reactions) def test_local_load(model: "Model", compare_models: Callable) -> None: """Test model loading from local repository. Parameters ---------- model : cobra.Model The model to compare local loading against. compare_models : Callable A callable object to compare local loading. """ model_local = load_model("textbook") compare_models(model, model_local) cobrapy-0.29.1/tests/test_io/test_yaml.py000066400000000000000000000021411467301562300204320ustar00rootroot00000000000000"""Test functionalities of I/O in YAML format.""" import json from pathlib import Path from typing import Callable import pytest from ruamel.yaml import YAML from cobra import Model from cobra import io as cio def test_load_yaml_model( compare_models: Callable, cobra_data_directory: Path, mini_model: Model ) -> None: """Test the reading of YAML model.""" yaml_model = cio.load_yaml_model(cobra_data_directory.joinpath("mini.yml")) assert compare_models(mini_model, yaml_model) is None @pytest.mark.xfail(reason="schema outdated") def test_save_yaml_model(tmp_path: Path, mini_model: Model) -> None: """Test the writing of YAML model.""" jsonschema = pytest.importorskip("jsonschema") output_file = tmp_path.joinpath("mini.yml") cio.save_yaml_model(mini_model, str(output_file), sort=True) # validate against JSONSchema yaml = YAML(typ="unsafe") with open(output_file, "r") as infile: yaml_to_dict = yaml.load(infile) dict_to_json = json.dumps(yaml_to_dict) loaded = json.loads(dict_to_json) assert jsonschema.validate(loaded, cio.json.json_schema) cobrapy-0.29.1/tests/test_manipulation/000077500000000000000000000000001467301562300201525ustar00rootroot00000000000000cobrapy-0.29.1/tests/test_manipulation/test_annotate.py000066400000000000000000000014401467301562300233730ustar00rootroot00000000000000"""Test functionalities of model component annotation functions.""" from cobra.core import Model, Reaction from cobra.manipulation import add_SBO def test_sbo_annotation(model: Model) -> None: """Test SBO annotation function.""" rxns = model.reactions rxns.EX_o2_e.annotation.clear() fake_DM = Reaction("DM_h_c") model.add_reactions([fake_DM]) fake_DM.add_metabolites({model.metabolites.get_by_id("h_c"): -1}) # this exchange will be set wrong. The function should not overwrite # an existing SBO annotation rxns.get_by_id("EX_h_e").annotation["sbo"] = "SBO:0000628" add_SBO(model) assert rxns.EX_o2_e.annotation["sbo"] == "SBO:0000627" assert rxns.DM_h_c.annotation["sbo"] == "SBO:0000628" assert rxns.EX_h_e.annotation["sbo"] == "SBO:0000628" cobrapy-0.29.1/tests/test_manipulation/test_delete.py000066400000000000000000000275131467301562300230350ustar00rootroot00000000000000"""Test functionalities of model component pruning functions.""" from itertools import chain from cobra.core import GPR, Metabolite, Model, Reaction from cobra.manipulation import ( knock_out_model_genes, prune_unused_metabolites, prune_unused_reactions, remove_genes, ) def test_prune_unused_metabolites_output_type(model: Model) -> None: """Test the output type of unused metabolites pruning.""" [model.reactions.get_by_id(x).remove_from_model() for x in ["RPI", "RPE", "GND"]] model_pruned, unused = prune_unused_metabolites(model) assert isinstance(model_pruned, Model) # test that the output contains metabolite objects assert isinstance(unused[0], Metabolite) def test_prune_unused_metabolites_sanity(model: Model) -> None: """Test the sanity of unused metabolites pruning.""" metabolite1 = model.metabolites.ru5p__D_c metabolite2 = model.metabolites.akg_e metabolite3 = model.metabolites.akg_c reactions = set( chain(metabolite1.reactions, metabolite2.reactions, metabolite3.reactions) ) model.remove_reactions(reactions) model_pruned, unused = prune_unused_metabolites(model) assert metabolite1 in model.metabolites assert metabolite2 in model.metabolites assert metabolite3 in model.metabolites # test that the unused metabolites are not used in the model assert metabolite1 not in model_pruned.metabolites assert metabolite2 not in model_pruned.metabolites assert metabolite3 not in model_pruned.metabolites def test_prune_unused_reactions_output_type(model: Model) -> None: """Test the output type of unused reactions pruning.""" reaction = Reaction("foo") model.add_reactions([reaction]) model_pruned, unused = prune_unused_reactions(model) assert isinstance(model_pruned, Model) # test that the output contains reaction objects assert isinstance(unused[0], Reaction) def test_prune_unused_rxns_functionality(model: Model) -> None: """Test the sanity of unused reactions pruning.""" for x in ["foo1", "foo2", "foo3"]: model.add_reactions([Reaction(x)]) model_pruned, unused = prune_unused_reactions(model) assert "foo1" in model.reactions assert "foo2" in model.reactions assert "foo3" in model.reactions # test that the unused reactions are not used in the model assert "foo1" not in model_pruned.reactions assert "foo2" not in model_pruned.reactions assert "foo3" not in model_pruned.reactions def test_gene_knockout(salmonella: Model) -> None: """Test gene knockout with and without context.""" gene_list = ["STM1067", "STM0227"] dependent_reactions = [ "3HAD100", "3HAD120", "3HAD121", "3HAD140", "3HAD141", "3HAD160", "3HAD161", "3HAD180", "3HAD181", "3HAD40", "3HAD60", "3HAD80", "T2DECAI", ] orig_gene_len = len(salmonella.genes) orig_bounds = salmonella.reactions.list_attr("bounds") with salmonella: expected_reactions = [ salmonella.reactions.get_by_id(r) for r in dependent_reactions ] knocked_out_reactions = knock_out_model_genes(salmonella, gene_list) assert set(expected_reactions) == set(knocked_out_reactions) assert len(salmonella.genes) == orig_gene_len assert salmonella.reactions.list_attr("bounds") == orig_bounds with salmonella: expected_reactions = [salmonella.reactions.get_by_id("PGI")] knocked_out_reactions = knock_out_model_genes(salmonella, ["STM4221"]) assert expected_reactions == knocked_out_reactions with salmonella: expected_reactions = [salmonella.reactions.get_by_id("PGI")] knocked_out_reactions = knock_out_model_genes( salmonella, [salmonella.genes.get_by_id("STM4221")] ) assert expected_reactions == knocked_out_reactions with salmonella: expected_reactions = [salmonella.reactions.get_by_id("PGI")] knocked_out_reactions = knock_out_model_genes( salmonella, [salmonella.genes.index("STM4221")] ) assert expected_reactions == knocked_out_reactions with salmonella: expected_reactions = [salmonella.reactions.get_by_id("4PEPTabcpp")] knocked_out_reactions = knock_out_model_genes(salmonella, ["STM1746.S"]) assert expected_reactions == knocked_out_reactions knocked_out_reactions = knock_out_model_genes(salmonella, gene_list) assert len(knocked_out_reactions) == 13 expected_reactions = [ salmonella.reactions.get_by_id(r) for r in dependent_reactions ] assert set(knocked_out_reactions) == set(expected_reactions) knocked_out_reactions.extend(knock_out_model_genes(salmonella, ["STM4221"])) expected_reactions.append(salmonella.reactions.get_by_id("PGI")) assert set(knocked_out_reactions) == set(expected_reactions) # test computation when gene name is a subset of another test_model = Model() test_reaction_1 = Reaction("test1") test_reaction_1.gene_reaction_rule = "eggs or (spam and eggspam)" test_model.add_reactions([test_reaction_1]) with test_model: knocked_out_reactions = knock_out_model_genes(test_model, ["eggs"]) assert knocked_out_reactions == list() with test_model: knocked_out_reactions = knock_out_model_genes(test_model, ["eggs", "spam"]) expected_reactions = [test_model.reactions.get_by_id("test1")] assert set(knocked_out_reactions) == set(expected_reactions) # test computation with nested boolean expression test_reaction_1.gene_reaction_rule = "g1 and g2 and (g3 or g4 or (g5 and g6))" with test_model: knocked_out_reactions = knock_out_model_genes(test_model, ["g3"]) assert knocked_out_reactions == list() with test_model: knocked_out_reactions = knock_out_model_genes(test_model, ["g1"]) assert knocked_out_reactions == [test_reaction_1] with test_model: knocked_out_reactions = knock_out_model_genes(test_model, ["g5"]) assert knocked_out_reactions == list() with test_model: knocked_out_reactions = knock_out_model_genes(test_model, ["g3", "g4", "g5"]) assert knocked_out_reactions == [test_reaction_1] # test computation when gene names are python expressions test_reaction_1.gene_reaction_rule = "g1 and (for or in)" with test_model: knocked_out_reactions = knock_out_model_genes(test_model, ["for", "in"]) assert knocked_out_reactions == [test_reaction_1] with test_model: knocked_out_reactions = knock_out_model_genes(test_model, ["for"]) assert knocked_out_reactions == list() test_reaction_1.gene_reaction_rule = "g1 and g2 and g2.conjugate" with test_model: knocked_out_reactions = knock_out_model_genes(test_model, ["g2"]) assert knocked_out_reactions == [test_reaction_1] with test_model: knocked_out_reactions = knock_out_model_genes(test_model, ["g2.conjugate"]) assert knocked_out_reactions == [test_reaction_1] test_reaction_1.gene_reaction_rule = "g1 and (try:' or 'except:1)" with test_model: knocked_out_reactions = knock_out_model_genes(test_model, ["try:'"]) assert knocked_out_reactions == [] with test_model: knocked_out_reactions = knock_out_model_genes( test_model, ["try:'", "'except:1"] ) assert knocked_out_reactions == [test_reaction_1] def test_remove_genes() -> None: """Test gene removal.""" m = Model("test") m.add_reactions([Reaction("r" + str(i + 1)) for i in range(8)]) assert len(m.reactions) == 8 rxns = m.reactions rxns.r1.gene_reaction_rule = "(a and b) or (c and a)" rxns.r2.gene_reaction_rule = "(a and b and d and e)" rxns.r3.gene_reaction_rule = "(a and b) or (b and c)" rxns.r4.gene_reaction_rule = "(f and b) or (b and c)" rxns.r5.gene_reaction_rule = "x" rxns.r6.gene_reaction_rule = "y" rxns.r7.gene_reaction_rule = "x or z" rxns.r8.gene_reaction_rule = "" assert "a" in m.genes assert "x" in m.genes remove_genes(m, ["a"], remove_reactions=False) assert "a" not in m.genes assert "x" in m.genes assert rxns.r1.gene_reaction_rule == "" assert rxns.r2.gene_reaction_rule == "" assert rxns.r3.gene_reaction_rule == "b and c" assert rxns.r4.gene_reaction_rule == "(f and b) or (b and c)" assert rxns.r5.gene_reaction_rule == "x" assert rxns.r6.gene_reaction_rule == "y" assert rxns.r7.genes == {m.genes.x, m.genes.z} assert rxns.r8.gene_reaction_rule == "" remove_genes(m, ["x"], remove_reactions=True) assert len(m.reactions) == 7 assert "r5" not in m.reactions assert "x" not in m.genes assert rxns.r1.gene_reaction_rule == "" assert rxns.r2.gene_reaction_rule == "" assert rxns.r3.gene_reaction_rule == "b and c" assert rxns.r4.gene_reaction_rule == "(f and b) or (b and c)" assert rxns.r6.gene_reaction_rule == "y" assert rxns.r7.gene_reaction_rule == "z" assert rxns.r7.genes == {m.genes.z} assert rxns.r8.gene_reaction_rule == "" def test_remove_genes_with_context() -> None: """Test gene removal is reversed in context.""" m = Model("test") m.add_reactions([Reaction("r" + str(i + 1)) for i in range(8)]) assert len(m.reactions) == 8 rxns = m.reactions rxns.r1.gene_reaction_rule = "(a and b) or (c and a)" rxns.r2.gene_reaction_rule = "(a and b and d and e)" rxns.r3.gene_reaction_rule = "(a and b) or (b and c)" rxns.r4.gene_reaction_rule = "(f and b) or (b and c)" rxns.r5.gene_reaction_rule = "x" rxns.r6.gene_reaction_rule = "y" rxns.r7.gene_reaction_rule = "x or z" rxns.r8.gene_reaction_rule = "" with m: remove_genes(m, ["a"], remove_reactions=False) assert "a" not in m.genes assert "x" in m.genes assert rxns.r1.gene_reaction_rule == "" assert rxns.r2.gene_reaction_rule == "" assert rxns.r3.gene_reaction_rule == "b and c" assert rxns.r4.gene_reaction_rule == "(f and b) or (b and c)" assert rxns.r5.gene_reaction_rule == "x" assert rxns.r6.gene_reaction_rule == "y" assert rxns.r7.genes == {m.genes.x, m.genes.z} assert rxns.r8.gene_reaction_rule == "" assert "a" in m.genes assert "x" in m.genes assert rxns.r1.gene_reaction_rule == "(a and b) or (c and a)" assert rxns.r2.gpr == GPR.from_string("(a and b and d and e)") assert rxns.r3.gene_reaction_rule == "(a and b) or (b and c)" assert rxns.r4.gene_reaction_rule == "(f and b) or (b and c)" assert rxns.r5.gene_reaction_rule == "x" assert rxns.r6.gene_reaction_rule == "y" assert rxns.r7.genes == {m.genes.x, m.genes.z} assert rxns.r8.gene_reaction_rule == "" with m: remove_genes(m, ["a"], remove_reactions=False) assert "a" not in m.genes remove_genes(m, ["x"], remove_reactions=True) assert len(m.reactions) == 7 assert "r5" not in m.reactions assert "x" not in m.genes assert rxns.r1.gene_reaction_rule == "" assert rxns.r2.gene_reaction_rule == "" assert rxns.r3.gene_reaction_rule == "b and c" assert rxns.r4.gene_reaction_rule == "(f and b) or (b and c)" assert rxns.r6.gene_reaction_rule == "y" assert rxns.r7.gene_reaction_rule == "z" assert rxns.r7.genes == {m.genes.z} assert rxns.r8.gene_reaction_rule == "" assert "a" in m.genes assert "x" in m.genes assert len(m.reactions) == 8 assert rxns.r1.gene_reaction_rule == "(a and b) or (c and a)" assert rxns.r2.gpr == GPR.from_string("(a and b and d and e)") assert rxns.r3.gene_reaction_rule == "(a and b) or (b and c)" assert rxns.r4.gene_reaction_rule == "(f and b) or (b and c)" assert rxns.r5.gene_reaction_rule == "x" assert rxns.r6.gene_reaction_rule == "y" assert rxns.r7.genes == {m.genes.x, m.genes.z} assert rxns.r8.gene_reaction_rule == "" cobrapy-0.29.1/tests/test_manipulation/test_modify.py000066400000000000000000000100671467301562300230560ustar00rootroot00000000000000"""Test functionalities of model component modifcations.""" from cobra.core import Model from cobra.manipulation import escape_ID, rename_genes def test_escape_ids(model: Model) -> None: """Test model component IDs' SBML compliance.""" model.reactions.PGI.gene_reaction_rule = "a.b or c" assert "a.b" in model.genes escape_ID(model) assert "a.b" not in model.genes def test_rename_genes(model: Model) -> None: """Test gene renaming functionality.""" original_name = model.genes.b1241.name rename_dict = { "b1241": "foo", "hello": "world", "b3115": "b3115", "b2465": "b3919", "bar": "2935", } rename_genes(model, rename_dict) for i in rename_dict.keys(): if i not in rename_dict.values(): assert i not in model.genes assert "b3115" in model.genes assert "foo" in model.genes assert "world" not in model.genes # make sure the object name was preserved assert model.genes.foo.name == original_name # make sure the reactions are correct assert len(model.genes.foo.reactions) == 2 assert model.reactions.ACALD.gene_reaction_rule == "b0351 or foo" assert model.reactions.TPI.gene_reaction_rule == "b3919" assert model.reactions.TPI.genes == {model.genes.b3919} assert model.reactions.TKT1.gene_reaction_rule == "b2935 or b3919" assert model.reactions.TKT1.genes == {model.genes.b2935, model.genes.b3919} assert model.genes.b3919.reactions == { model.reactions.get_by_id(i) for i in ("TKT1", "TKT2", "TPI") } def test_rename_genes_with_context(model: Model) -> None: """Test gene renaming functionality is reversed with context.""" original_name = model.genes.b1241.name rename_dict = { "b1241": "foo", "hello": "world", "b3115": "b3115", "b2465": "b3919", "bar": "2935", } genes_in_old_model = set() print(model.genes.b1241.reactions) for i in rename_dict.keys(): if i not in rename_dict.values() and i in model.genes: genes_in_old_model.add(i) with model: rename_genes(model, rename_dict) for i in rename_dict.keys(): if i not in rename_dict.values(): assert i not in model.genes assert "b3115" in model.genes assert "foo" in model.genes assert "world" not in model.genes # make sure the object name was preserved assert model.genes.foo.name == original_name # make sure the reactions are correct assert len(model.genes.foo.reactions) == 2 assert model.reactions.ACALD.gene_reaction_rule == "b0351 or foo" assert model.reactions.TPI.gene_reaction_rule == "b3919" assert model.reactions.TPI.genes == {model.genes.b3919} assert model.reactions.TKT1.gene_reaction_rule == "b2935 or b3919" assert model.reactions.TKT1.genes == {model.genes.b2935, model.genes.b3919} assert model.genes.b3919.reactions == { model.reactions.get_by_id(i) for i in ("TKT1", "TKT2", "TPI") } print(model.genes._dict) print(model.genes) for i in genes_in_old_model: if i not in rename_dict.values(): assert i in model.genes print(model.genes._dict) print(model.genes) assert "b3115" in model.genes assert "foo" not in model.genes assert "world" not in model.genes # make sure the object name was preserved assert model.genes.b1241.name == original_name # make sure the reactions are correct assert len(model.genes.b1241.reactions) == 2 assert model.reactions.ACALD.gene_reaction_rule == "b0351 or b1241" assert model.reactions.TPI.gene_reaction_rule == "b3919" assert model.reactions.TPI.genes == {model.genes.b3919} assert model.reactions.TKT1.gene_reaction_rule == "b2935 or b2465" assert model.reactions.TKT1.genes == {model.genes.b2935, model.genes.b2465} assert model.genes.b2465.reactions == { model.reactions.get_by_id(i) for i in ("TKT1", "TKT2") } assert model.genes.b3919.reactions == {model.reactions.get_by_id("TPI")} cobrapy-0.29.1/tests/test_manipulation/test_validate.py000066400000000000000000000021761467301562300233620ustar00rootroot00000000000000"""Test functionalities of cobra component validation functions.""" import pytest from cobra.core import Metabolite, Model, Reaction from cobra.manipulation import check_mass_balance, check_metabolite_compartment_formula def test_validate_mass_balance(model: Model) -> None: """Test reaction mass balance validation.""" assert len(check_mass_balance(model)) == 0 # if we remove the SBO term which marks the reaction as # mass balanced, then the reaction should be detected as # no longer mass balanced EX_rxn = model.reactions.query(lambda r: r.boundary)[0] EX_rxn.annotation.pop("sbo") balance = check_mass_balance(model) assert len(balance) == 1 assert EX_rxn in balance m1 = Metabolite("m1", formula="()") r1 = Reaction("r1") r1.add_metabolites({m1: 1}) with pytest.raises(ValueError), pytest.warns(UserWarning): r1.check_mass_balance() def test_validate_formula_compartment(model: Model) -> None: """Test metabolite formulae validation.""" model.metabolites[1].formula = "(a*.bcde)" errors = check_metabolite_compartment_formula(model) assert len(errors) == 1 cobrapy-0.29.1/tests/test_medium/000077500000000000000000000000001467301562300167325ustar00rootroot00000000000000cobrapy-0.29.1/tests/test_medium/test_boundary_types.py000066400000000000000000000077321467301562300234230ustar00rootroot00000000000000"""Test functionalities of boundary type detection functions.""" import logging import pytest from cobra.core import Metabolite, Model, Reaction from cobra.medium import ( find_boundary_types, find_external_compartment, is_boundary_type, ) def test_find_external_compartment_single(model: Model) -> None: """Test external compartment identification.""" # by name assert find_external_compartment(model) == "e" # from boundary counts for m in model.metabolites: if m.compartment == "e": m.compartment = "outside" for r in model.reactions: r._compartments = None assert find_external_compartment(model) == "outside" # names are always right model.exchanges[0].reactants[0].compartment = "extracellular" assert find_external_compartment(model) == "extracellular" def test_find_external_compartment_multi(model: Model) -> None: """Test multiple external compartment identification.""" for r in model.reactions: r._compartments = None model.exchanges[0].reactants[0].compartment = "extracellular" # still works due to different boundary numbers assert find_external_compartment(model) == "e" model.exchanges[1].reactants[0].compartment = "extra cellular" model.remove_reactions(model.exchanges) # Now fails because same boundary count with pytest.raises(RuntimeError): find_external_compartment(model) @pytest.mark.parametrize("compartment", ["C_e", "e0"]) def test_find_external_popular_reconstructions( model: Model, compartment, caplog ) -> None: """Test some additional id formats.""" for ex in model.exchanges: ex.reactants[0].compartment = compartment with caplog.at_level(logging.WARNING): external = find_external_compartment(model) assert external == compartment assert "complete nonsense" not in caplog.text def test_no_names_or_boundary_reactions(empty_model: Model) -> None: """Test absence of name or boundary reactions.""" with pytest.raises(RuntimeError): find_external_compartment(empty_model) def test_find_boundary_types_exchange(model: Model) -> None: """Test boundary type identification for exchanges.""" ex = model.exchanges assert all(r.id.startswith("EX_") for r in ex) ex = find_boundary_types(model, "exchange", "e") assert all(r.id.startswith("EX_") for r in ex) def test_find_boundary_types_demand(model: Model) -> None: """Test boundary type identification for demands.""" dm = Reaction("demand") model.add_reactions([dm]) dm.build_reaction_from_string("atp_c ->") dm = model.demands assert len(dm) == 1 assert "demand" in [r.id for r in dm] def test_find_boundary_types_sink(model: Model) -> None: """Test boundary type identification for sinks.""" sn = Reaction("sink") model.add_reactions([sn]) sn.build_reaction_from_string("atp_c <->") sn.bounds = -1000, 1000 sn = model.sinks assert len(sn) == 1 assert "sink" in [r.id for r in sn] def test_no_boundary_reactions(empty_model: Model) -> None: """Test proper identification of no boundary reactions.""" assert find_boundary_types(empty_model, "e", None) == [] def test_is_boundary_type(model: Model) -> None: """Test correct identification of boundary types for reactions.""" assert not is_boundary_type(model.reactions.ATPM, "exchange", "e") model.reactions.ATPM.annotation["sbo"] = "SBO:0000627" assert is_boundary_type(model.reactions.ATPM, "exchange", "bla") model.reactions.ATPM.annotation["sbo"] = "SBO:0000632" assert not is_boundary_type(model.reactions.ATPM, "exchange", "e") def test_bad_exchange(model: Model) -> None: """Test bad exchange reaction identification.""" with pytest.raises(ValueError): m = Metabolite("baddy", compartment="nonsense") model.add_boundary(m, type="exchange") m = Metabolite("goody", compartment="e") rxn = model.add_boundary(m, type="exchange") assert isinstance(rxn, Reaction) cobrapy-0.29.1/tests/test_medium/test_minimal_medium.py000066400000000000000000000112061467301562300233310ustar00rootroot00000000000000"""Test functionalities of minimal medium creation and analysis.""" from typing import Callable import pandas as pd import pytest from cobra.core import Model, Reaction from cobra.medium import minimal_medium def test_minimal_medium_linear(model: Model) -> None: """Test linear minimal medium.""" med = minimal_medium(model, 0.8) assert len(med) <= 4 assert all(med > 1e-6) def test_minimal_medium_linear_benchmark(model: Model, benchmark: Callable) -> None: """Benchmark linear minimal medium.""" benchmark(minimal_medium, model, 0.8) def test_minimal_medium_mip(model: Model) -> None: """Test mixed-integer minimal medium.""" med = minimal_medium(model, 0.8, minimize_components=True) assert len(med) <= 4 assert all(med > 1e-6) # Anaerobic growth med = minimal_medium(model, 0.1, minimize_components=True) assert len(med) <= 3 assert all(med > 1e-6) def test_minimal_medium_mip_benchmark(model: Model, benchmark: Callable) -> None: """Benchmark mixed-integer minimal medium.""" benchmark(minimal_medium, model, 0.8, True) def test_minimal_medium_alternative_mip(model: Model) -> None: """Test alternative mixed-integer minimal medium.""" med = minimal_medium(model, 0.8, minimize_components=5, open_exchanges=True) assert isinstance(med, pd.DataFrame) assert med.shape[0] >= 5 assert med.shape[1] == 5 assert all((med > 0).sum() == 3) assert all(med.sum(axis=1) > 1e-6) def test_minimal_medium_exports(model: Model) -> None: """Test exports of a minimal medium.""" med = minimal_medium(model, 0.8, exports=True, minimize_components=True) assert len(med) > 4 assert any(med < -1e-6) def test_minimal_medium_open_exchanges(model: Model) -> None: """Test open exchanges of a minimal medium.""" model.reactions.EX_glc__D_e.bounds = 0, 0 med = minimal_medium(model, 0.8) assert med is None med = minimal_medium(model, 0.8, minimize_components=True) assert med is None med = minimal_medium(model, 0.8, open_exchanges=True) assert len(med) >= 3 med = minimal_medium(model, 0.8, open_exchanges=100) assert len(med) >= 3 def test_model_medium(model: Model) -> None: """Test proper functioning of model medium manipulations.""" # Add a dummy 'malformed' import reaction bad_import = Reaction("bad_import") bad_import.add_metabolites({model.metabolites.pyr_c: 1}) bad_import.bounds = (0, 42) model.add_reactions([bad_import]) # Test basic setting and getting methods medium = model.medium model.medium = medium assert model.medium == medium # Test context management with model: # Ensure the bounds are correct beforehand assert model.reactions.EX_glc__D_e.lower_bound == -10 assert model.reactions.bad_import.upper_bound == 42 assert model.reactions.EX_co2_e.lower_bound == -1000 # Make changes to the media new_medium = model.medium new_medium["EX_glc__D_e"] = 20 new_medium["bad_import"] = 24 del new_medium["EX_co2_e"] # Change the medium, make sure changes work model.medium = new_medium assert model.reactions.EX_glc__D_e.lower_bound == -20 assert model.reactions.bad_import.upper_bound == 24 assert model.reactions.EX_co2_e.lower_bound == 0 # Make sure changes revert after the contex assert model.reactions.EX_glc__D_e.lower_bound == -10 assert model.reactions.bad_import.upper_bound == 42 assert model.reactions.EX_co2_e.lower_bound == -1000 new_medium["bogus_rxn"] = 0 with pytest.raises(KeyError): model.medium = new_medium def test_medium_does_not_affect_reactant_exports(model: Model) -> None: """Test that the medium setter does not overwrite exports defined as reactants.""" med = model.medium # Set a fixed export rate model.reactions.EX_ac_e.lower_bound = 0.1 model.medium = med assert model.reactions.EX_ac_e.lower_bound == 0.1 # should be overwritten if actually in the medium med["EX_ac_e"] = 1 model.medium = med assert model.reactions.EX_ac_e.lower_bound == -1 def test_medium_does_not_affect_product_exports(model: Model) -> None: """Test that the medium setter does not overwrite exports defined as products.""" med = model.medium # Reverse reaction definition model.reactions.EX_ac_e *= -1 # Set a fixed export rate model.reactions.EX_ac_e.bounds = -1, -0.1 model.medium = med assert model.reactions.EX_ac_e.bounds == (-1, -0.1) # should be overwritten if actually in the medium med["EX_ac_e"] = 1 model.medium = med assert model.reactions.EX_ac_e.upper_bound == 1 cobrapy-0.29.1/tests/test_sampling/000077500000000000000000000000001467301562300172645ustar00rootroot00000000000000cobrapy-0.29.1/tests/test_sampling/conftest.py000066400000000000000000000007711467301562300214700ustar00rootroot00000000000000"""Define module level fixtures.""" from typing import TYPE_CHECKING import pytest from cobra.sampling import ACHRSampler if TYPE_CHECKING: from cobra import Model @pytest.fixture(scope="function") def achr(model: "Model") -> ACHRSampler: """Return ACHRSampler instance for tests.""" sampler = ACHRSampler(model, thinning=1) assert (sampler.n_warmup > 0) and (sampler.n_warmup <= 2 * len(model.variables)) assert all(sampler.validate(sampler.warmup) == "v") return sampler cobrapy-0.29.1/tests/test_sampling/test_achr.py000066400000000000000000000025101467301562300216100ustar00rootroot00000000000000"""Test functionalities of ACHRSampler.""" from typing import TYPE_CHECKING, Callable import numpy as np import pytest from cobra.sampling import ACHRSampler if TYPE_CHECKING: from cobra import Model def test_achr_init_benchmark(model: "Model", benchmark: Callable) -> None: """Benchmark inital ACHR sampling.""" benchmark(lambda: ACHRSampler(model)) def test_achr_sample_benchmark(achr: ACHRSampler, benchmark: Callable) -> None: """Benchmark ACHR sampling.""" benchmark(achr.sample, 1) def test_validate_wrong_sample(achr: ACHRSampler, model: "Model") -> None: """Test sample correctness.""" s = achr.sample(10) s["hello"] = 1 with pytest.raises(ValueError): achr.validate(s) def test_sampling(achr: ACHRSampler) -> None: """Test sampling.""" s = achr.sample(10) assert all(achr.validate(s) == "v") def test_batch_sampling(achr: ACHRSampler) -> None: """Test batch sampling.""" for b in achr.batch(5, 4): assert all(achr.validate(b) == "v") def test_variables_samples(achr: ACHRSampler) -> None: """Test variable samples.""" vnames = np.array([v.name for v in achr.model.variables]) s = achr.sample(10, fluxes=False) assert s.shape == (10, achr.warmup.shape[1]) assert (s.columns == vnames).all() assert (achr.validate(s) == "v").all() cobrapy-0.29.1/tests/test_sampling/test_optgp.py000066400000000000000000000040151467301562300220260ustar00rootroot00000000000000"""Test functionalities of OptGPSampler.""" import os from typing import TYPE_CHECKING, Callable import numpy as np import pytest from cobra.sampling import OptGPSampler if TYPE_CHECKING: from cobra import Model from cobra.sampling import ACHRSampler @pytest.fixture(scope="function") def optgp(model: "Model") -> OptGPSampler: """Return OptGPSampler instance for tests.""" sampler = OptGPSampler(model, processes=1, thinning=1) assert (sampler.n_warmup > 0) and (sampler.n_warmup <= 2 * len(model.variables)) assert all(sampler.validate(sampler.warmup) == "v") return sampler @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_optgp_init_benchmark(model: "Model", benchmark: Callable) -> None: """Benchmark inital OptGP sampling.""" benchmark(lambda: OptGPSampler(model, processes=2)) def test_optgp_sample_benchmark(optgp: "Model", benchmark: Callable) -> None: """Benchmark OptGP sampling.""" benchmark(optgp.sample, 1) def test_sampling(optgp: OptGPSampler) -> None: """Test sampling.""" s = optgp.sample(10) assert all(optgp.validate(s) == "v") def test_batch_sampling(optgp: OptGPSampler) -> None: """Test batch sampling.""" for b in optgp.batch(5, 4): assert all(optgp.validate(b) == "v") def test_variables_samples(achr: "ACHRSampler", optgp: OptGPSampler) -> None: """Test variable samples.""" vnames = np.array([v.name for v in achr.model.variables]) s = optgp.sample(10, fluxes=False) assert s.shape == (10, optgp.warmup.shape[1]) assert (s.columns == vnames).all() assert (optgp.validate(s) == "v").all() def test_reproject(optgp: OptGPSampler) -> None: """Test reprojection of sampling.""" s = optgp.sample(10, fluxes=False).values proj = np.apply_along_axis(optgp._reproject, 1, s) assert all(optgp.validate(proj) == "v") s = np.random.rand(10, optgp.warmup.shape[1]) proj = np.apply_along_axis(optgp._reproject, 1, s) assert all(optgp.validate(proj) == "v") cobrapy-0.29.1/tests/test_sampling/test_sampling.py000066400000000000000000000101151467301562300225050ustar00rootroot00000000000000"""Test functionalities of flux sampling methods.""" import os import numpy as np import pytest from cobra.core import Metabolite, Model, Reaction from cobra.flux_analysis.parsimonious import pfba from cobra.sampling import ACHRSampler, OptGPSampler, sample def test_single_achr(model: Model) -> None: """Test ACHR sampling (one sample).""" s = sample(model, 10, method="achr") assert s.shape == (10, len(model.reactions)) def test_single_optgp(model: Model) -> None: """Test OptGP sampling (one sample).""" s = sample(model, 10, processes=1) assert s.shape == (10, len(model.reactions)) @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_multi_optgp(model: Model) -> None: # pragma: no cover """Test OptGP sampling (multi sample).""" s = sample(model, 10, processes=2) assert s.shape == (10, len(model.reactions)) def test_wrong_method(model: Model) -> None: """Test method intake sanity.""" with pytest.raises(ValueError): sample(model, 1, method="schwupdiwupp") def test_fixed_seed(model: Model) -> None: """Test result of fixed seed for sampling.""" s1 = sample(model, 1, seed=42) s2 = sample(model, 1, seed=42) assert np.isclose(s1.TPI[0], s2.TPI[0]) def test_equality_constraint(model: Model) -> None: """Test equality constraint.""" model.reactions.ACALD.bounds = (-1.5, -1.5) s = sample(model, 10) assert np.allclose(s.ACALD, -1.5, atol=1e-6, rtol=0) s = sample(model, 10, method="achr") assert np.allclose(s.ACALD, -1.5, atol=1e-6, rtol=0) def test_inequality_constraint(model: Model) -> None: """Test inequality constraint.""" co = model.problem.Constraint(model.reactions.ACALD.flux_expression, lb=-0.5) model.add_cons_vars(co) s = sample(model, 10) assert all(s.ACALD > -0.5 - 1e-6) s = sample(model, 10, method="achr") assert all(s.ACALD > -0.5 - 1e-6) def test_inhomogeneous_sanity(model: Model) -> None: """Test standard deviation between inhomogeneous and homogeneous sampling.""" model.reactions.ACALD.bounds = (-1.5, -1.5) s_inhom = sample(model, 64) model.reactions.ACALD.bounds = (-1.5 - 1e-3, -1.5 + 1e-3) s_hom = sample(model, 64) relative_diff = (s_inhom.std() + 1e-12) / (s_hom.std() + 1e-12) assert 0.5 < relative_diff.abs().mean() < 2 model.reactions.ACALD.bounds = (-1.5, -1.5) s_inhom = sample(model, 64, method="achr") model.reactions.ACALD.bounds = (-1.5 - 1e-3, -1.5 + 1e-3) s_hom = sample(model, 64, method="achr") relative_diff = (s_inhom.std() + 1e-12) / (s_hom.std() + 1e-12) assert 0.5 < relative_diff.abs().mean() < 2 def test_complicated_model() -> None: """Test a complicated model. Difficult model since the online mean calculation is numerically unstable, so many samples weakly violate the equality constraints. """ model = Model("flux_split") reaction1 = Reaction("V1") reaction2 = Reaction("V2") reaction3 = Reaction("V3") reaction1.bounds = (0, 6) reaction2.bounds = (0, 8) reaction3.bounds = (0, 10) A = Metabolite("A") reaction1.add_metabolites({A: -1}) reaction2.add_metabolites({A: -1}) reaction3.add_metabolites({A: 1}) model.add_reactions([reaction1, reaction2, reaction3]) optgp = OptGPSampler(model, 1, seed=42) achr = ACHRSampler(model, seed=42) optgp_samples = optgp.sample(100) achr_samples = achr.sample(100) assert any(optgp_samples.corr().abs() < 1.0) assert any(achr_samples.corr().abs() < 1.0) # > 95% are valid assert sum(optgp.validate(optgp_samples) == "v") > 95 assert sum(achr.validate(achr_samples) == "v") > 95 def test_single_point_space(model: Model) -> None: """Test the reduction of the sampling space to one point.""" pfba_sol = pfba(model) pfba_const = model.problem.Constraint( sum(model.variables), ub=pfba_sol.objective_value ) model.add_cons_vars(pfba_const) model.reactions.Biomass_Ecoli_core.lower_bound = pfba_sol.fluxes.Biomass_Ecoli_core with pytest.raises(ValueError): sample(model, 1) cobrapy-0.29.1/tests/test_summary/000077500000000000000000000000001467301562300171475ustar00rootroot00000000000000cobrapy-0.29.1/tests/test_summary/test_metabolite_summary.py000066400000000000000000000076601467301562300244730ustar00rootroot00000000000000"""Unit test the MetaboliteSummary class.""" import pytest from cobra.flux_analysis import flux_variability_analysis, pfba from cobra.summary import MetaboliteSummary def test_metabolite_summary_interface(model, opt_solver): """Test that a summary can be created successfully.""" model.solver = opt_solver metabolite = model.metabolites.get_by_id("q8_c") MetaboliteSummary( metabolite=metabolite, model=model, ) MetaboliteSummary( metabolite=metabolite, model=model, solution=pfba(model), ) MetaboliteSummary( metabolite=metabolite, model=model, fva=0.95, ) MetaboliteSummary( metabolite=metabolite, model=model, fva=flux_variability_analysis( model, reaction_list=["CYTBD", "NADH16", "SUCDi"] ), ) def test_metabolite_summary_to_frame(model, opt_solver): """Test that the summary's method ``to_frame`` can be called.""" model.solver = opt_solver summary = model.metabolites.get_by_id("atp_c").summary() summary.to_frame() @pytest.mark.parametrize( "kwargs", [ {}, {"names": True}, {"float_format": ".1f"}, {"threshold": 1e-2}, {"column_width": 20}, ], ) def test_metabolite_summary_to_string(model, opt_solver, kwargs): """Test that the summary's method ``to_string`` can be called.""" model.solver = opt_solver summary = model.metabolites.get_by_id("atp_c").summary() summary.to_string(**kwargs) @pytest.mark.parametrize( "kwargs", [{}, {"names": True}, {"float_format": ".1f"}, {"threshold": 1e-2}] ) def test_metabolite_summary_to_html(model, opt_solver, kwargs): """Test that the summary's method ``to_html`` can be called.""" model.solver = opt_solver summary = model.metabolites.get_by_id("atp_c").summary() summary.to_html(**kwargs) def test_q8_producing_summary(model, opt_solver): """Test that the production summary of q8 is accurate.""" model.solver = opt_solver summary = model.metabolites.get_by_id("q8_c").summary() assert summary.producing_flux.at["CYTBD", "percent"] == 1 assert summary.producing_flux.at["CYTBD", "flux"] == pytest.approx(43.6, abs=1e-2) def test_q8_consuming_summary(model, opt_solver): """Test that the consumption summary of q8 is accurate.""" model.solver = opt_solver summary = model.metabolites.get_by_id("q8_c").summary() assert summary.consuming_flux.at["NADH16", "percent"] == pytest.approx( 0.8838, abs=1e-4 ) assert summary.consuming_flux.at["NADH16", "flux"] == pytest.approx( -38.54, abs=1e-2 ) assert summary.consuming_flux.at["SUCDi", "percent"] == pytest.approx( 0.1162, abs=1e-4 ) assert summary.consuming_flux.at["SUCDi", "flux"] == pytest.approx(-5.06, abs=1e-2) def test_fdp_production_with_fva(model, opt_solver): """Test that the production summary of fdp is within expected bounds.""" model.solver = opt_solver summary = model.metabolites.get_by_id("fdp_c").summary(fva=0.99) assert summary.producing_flux.at["PFK", "percent"] == 1 assert summary.producing_flux.at["PFK", "flux"] == pytest.approx(7.48, abs=1e-2) assert summary.producing_flux.at["PFK", "minimum"] == pytest.approx(6.17, abs=1e-2) assert summary.producing_flux.at["PFK", "maximum"] == pytest.approx(9.26, abs=1e-2) @pytest.mark.parametrize("metabolite_id", ["q8_c", "fdp_c", "atp_c"]) def test_metabolite_summary_flux_in_context(model, opt_solver, metabolite_id: str): """Test that the metabolite summary inside and outside of a context are equal.""" model.solver = opt_solver with model: context_summary = model.metabolites.get_by_id(metabolite_id).summary() outside_summary = model.metabolites.get_by_id(metabolite_id).summary() assert context_summary.to_frame()["flux"].values == pytest.approx( outside_summary.to_frame()["flux"].values, abs=model.tolerance ) cobrapy-0.29.1/tests/test_summary/test_model_summary.py000066400000000000000000000071751467301562300234470ustar00rootroot00000000000000"""Unit test the model summary.""" import pytest from cobra.flux_analysis import flux_variability_analysis, pfba from cobra.summary import ModelSummary def test_model_summary_interface(model, opt_solver): """Test that a summary can be created successfully.""" model.solver = opt_solver ModelSummary( model=model, ) ModelSummary( model=model, solution=pfba(model), ) ModelSummary( model=model, fva=0.95, ) ModelSummary( model=model, fva=flux_variability_analysis( model, reaction_list=["CYTBD", "NADH16", "SUCDi"] ), ) def test_model_summary_to_frame(model, opt_solver): """Test that the summary's method ``to_frame`` can be called.""" model.solver = opt_solver summary = model.summary() summary.to_frame() @pytest.mark.parametrize( "kwargs", [ {}, {"names": True}, {"float_format": ".1f"}, {"threshold": 1e-2}, {"column_width": 20}, ], ) def test_model_summary_to_string(model, opt_solver, kwargs): """Test that the summary's method ``to_string`` can be called.""" model.solver = opt_solver summary = model.summary() summary.to_string(**kwargs) @pytest.mark.parametrize( "kwargs", [{}, {"names": True}, {"float_format": ".1f"}, {"threshold": 1e-2}] ) def test_model_summary_to_html(model, opt_solver, kwargs): """Test that the summary's method ``to_html`` can be called.""" model.solver = opt_solver summary = model.summary() summary.to_html(**kwargs) def test_model_summary_to_frame_previous_solution(model, opt_solver): """Test that the summary correctly uses an existing solution.""" model.solver = opt_solver solution = pfba(model) rxn = model.reactions.EX_glc__D_e solution.fluxes[rxn.id] = -123 summary = model.summary(solution=solution) assert summary.to_frame().at[rxn.id, "flux"] == 123 def test_model_summary_flux(model, opt_solver): """Test that the summary has expected fluxes.""" model.solver = opt_solver summary = model.summary() assert summary.uptake_flux.at["EX_o2_e", "flux"] == pytest.approx(21.8, abs=1e-02) assert summary.secretion_flux.at["EX_h2o_e", "flux"] == pytest.approx( -29.18, abs=1e-02 ) def test_model_summary_fva(model, opt_solver): """Test that the exchange summary is within expected bounds.""" model.solver = opt_solver summary = model.summary(fva=0.95) assert summary.to_frame().at["EX_o2_e", "flux"] == pytest.approx(21.8, abs=1e-02) assert summary.to_frame().at["EX_o2_e", "minimum"] == pytest.approx(19.9, abs=1e-02) assert summary.to_frame().at["EX_o2_e", "maximum"] == pytest.approx( 23.71, abs=1e-02 ) assert summary.to_frame().at["EX_h2o_e", "flux"] == pytest.approx(-29.18, abs=1e-02) assert summary.to_frame().at["EX_h2o_e", "minimum"] == pytest.approx( -30.72, abs=1e-02 ) assert summary.to_frame().at["EX_h2o_e", "maximum"] == pytest.approx(-25, abs=1e-02) def test_model_summary_flux_in_context(model, opt_solver): """Test that the model summary inside and outside of a context are equal.""" model.solver = opt_solver copy = model.copy() with model: model.reactions.EX_glc__D_e.bounds = (0, 1000) model.reactions.EX_ac_e.bounds = (-10, 1000) context_summary = model.summary() copy.reactions.EX_glc__D_e.bounds = (0, 1000) copy.reactions.EX_ac_e.bounds = (-10, 1000) outside_summary = copy.summary() assert context_summary.to_frame()["flux"].values == pytest.approx( outside_summary.to_frame()["flux"].values, abs=model.tolerance ) cobrapy-0.29.1/tests/test_summary/test_reaction_summary.py000066400000000000000000000066151467301562300241510ustar00rootroot00000000000000"""Unit test the ReactionSummary class.""" import pytest from cobra.flux_analysis import flux_variability_analysis, pfba from cobra.summary import ReactionSummary def test_reaction_summary_interface(model, opt_solver): """Test that a summary can be created successfully.""" model.solver = opt_solver reaction = model.reactions.get_by_id("FUM") ReactionSummary( reaction=reaction, model=model, ) ReactionSummary( reaction=reaction, model=model, solution=pfba(model), ) ReactionSummary( reaction=reaction, model=model, fva=0.95, ) ReactionSummary( reaction=reaction, model=model, fva=flux_variability_analysis(model, reaction_list=["FUM"]), ) def test_reaction_summary_to_frame(model, opt_solver): """Test that the summary's method ``to_frame`` can be called.""" model.solver = opt_solver summary = model.reactions.get_by_id("FUM").summary() summary.to_frame() @pytest.mark.parametrize( "kwargs", [ {}, {"names": True}, {"float_format": ".1f"}, {"threshold": 1e-2}, {"column_width": 20}, ], ) def test_reaction_summary_to_string(model, opt_solver, kwargs): """Test that the summary's method ``to_string`` can be called.""" model.solver = opt_solver summary = model.reactions.get_by_id("FUM").summary() summary.to_string(**kwargs) @pytest.mark.parametrize( "kwargs", [{}, {"names": True}, {"float_format": ".1f"}, {"threshold": 1e-2}] ) def test_reaction_summary_to_html(model, opt_solver, kwargs): """Test that the summary's method ``to_html`` can be called.""" model.solver = opt_solver summary = model.reactions.get_by_id("FUM").summary() summary.to_html(**kwargs) @pytest.mark.parametrize( "reaction_id, expected", [("ACALD", 0.0), ("FUM", 5.06), ("PFK", 7.48)] ) def test_reaction_summary_flux(model, reaction_id: str, expected: float) -> None: """Test that the reported flux in the summary is reasonable.""" result = ReactionSummary( reaction=model.reactions.get_by_id(reaction_id), model=model ) assert result.to_frame().at[reaction_id, "flux"] == pytest.approx( expected, abs=1e-2 ) @pytest.mark.parametrize( "reaction_id, min_flux, max_flux", [("ACALD", -1.27, 0.0), ("FUM", 0.79, 7.38), ("PFK", 2.58, 16.38)], ) def test_reaction_summary_flux_fva( model, reaction_id: str, min_flux: float, max_flux: float ) -> None: """Test that the reported flux ranges in the summary are reasonable.""" result = ReactionSummary( reaction=model.reactions.get_by_id(reaction_id), model=model, fva=0.95 ) assert result.to_frame().at[reaction_id, "minimum"] == pytest.approx( min_flux, abs=1e-2 ) assert result.to_frame().at[reaction_id, "maximum"] == pytest.approx( max_flux, abs=1e-2 ) @pytest.mark.parametrize("reaction_id", ["ACALD", "FUM", "PFK"]) def test_reaction_summary_flux_in_context(model, reaction_id: str) -> None: """Test that the reaction summary inside and outside of a context are equal.""" with model: context_summary = model.reactions.get_by_id(reaction_id).summary() outside_summary = model.reactions.get_by_id(reaction_id).summary() assert context_summary.to_frame()["flux"].values == pytest.approx( outside_summary.to_frame()["flux"].values, abs=model.tolerance ) cobrapy-0.29.1/tests/test_util/000077500000000000000000000000001467301562300164275ustar00rootroot00000000000000cobrapy-0.29.1/tests/test_util/test_array.py000066400000000000000000000024631467301562300211630ustar00rootroot00000000000000"""Test functions of array.py.""" from typing import TYPE_CHECKING import numpy as np import pytest from cobra.util import create_stoichiometric_matrix if TYPE_CHECKING: from cobra import Model def test_dense_matrix(model: "Model") -> None: """Test dense stoichiometric matrix creation.""" S = create_stoichiometric_matrix(model, array_type="dense", dtype=int) assert S.dtype == int assert np.allclose(S.max(), [59]) S_df = create_stoichiometric_matrix(model, array_type="DataFrame", dtype=int) assert S_df.values.dtype == int assert np.all(S_df.columns == [r.id for r in model.reactions]) assert np.all(S_df.index == [m.id for m in model.metabolites]) assert np.allclose(S_df.values, S) S = create_stoichiometric_matrix(model, array_type="dense", dtype=float) solution = model.optimize() mass_balance = S.dot(solution.fluxes) assert np.allclose(mass_balance, 0) def test_sparse_matrix(model: "Model") -> None: """Test sparse stoichiometric matrix creation.""" pytest.importorskip("scipy") sparse_types = ["dok", "lil"] solution = model.optimize() for sparse_type in sparse_types: S = create_stoichiometric_matrix(model, array_type=sparse_type) mass_balance = S.dot(solution.fluxes) assert np.allclose(mass_balance, 0) cobrapy-0.29.1/tests/test_util/test_context.py000066400000000000000000000025011467301562300215220ustar00rootroot00000000000000"""Test functions of context.py.""" from typing import TYPE_CHECKING from cobra.util import HistoryManager, get_context, resettable if TYPE_CHECKING: from cobra import Model def test_history_manager() -> None: """Test initialization and resetting of HistoryManager.""" # initialize HistoryManager history_manager = HistoryManager() # add non-functioning operation history_manager(lambda: 1) # check size of the stack assert history_manager.size() == 1 # reset operations history_manager.reset() def test_get_context(model: "Model") -> None: """Test if context retrieval is working.""" with model as model: # reverse optimization direcion model.objective_direction = "min" # retrieve context context = get_context(model) # check size of the context if context: assert context.size() == 1 # there shouldn't be any history if context: assert context.size() == 0 def test_resettable() -> None: """Test if resettable decorator is functional.""" # decorate a dummy function @resettable def change_my_name(old_name, new_name): """Change old name to new name.""" if old_name != new_name: old_name = new_name # call the dummy function change_my_name("hmm", "hmmm") cobrapy-0.29.1/tests/test_util/test_process_pool.py000066400000000000000000000102401467301562300225440ustar00rootroot00000000000000"""Test the behaviour of the ProcessPool class.""" import os from typing import Iterable, Tuple import pytest from pytest_mock import MockerFixture from cobra.util import ProcessPool def dummy_initializer(*args: Iterable) -> Tuple: """Implement a 'do nothing' function that accepts initialization arguments.""" return args def square(num: int) -> int: """Return the square of an integer.""" return num * num def summation(*args: Iterable[int]) -> int: """Return the sum of all integer arguments.""" return sum(args) @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") @pytest.mark.parametrize( "attributes", [ {}, {"processes": 2}, {"initializer": dummy_initializer}, {"initializer": dummy_initializer, "initargs": (1, "2", [3], {"a": 4})}, {"maxtasksperchild": 1}, ], ) def test_init(attributes: dict) -> None: """Test that a process pool can be initialized with each of its arguments.""" ProcessPool(**attributes) @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_close(mocker: MockerFixture) -> None: """Test that the composed pool is closed as well.""" pool = ProcessPool(processes=3) mock = mocker.patch.object(pool, "_pool", autospec=True) pool.close() mock.close.assert_called_once() @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_with_context(mocker: MockerFixture) -> None: """Test that the composed pool's context is managed as well.""" pool = ProcessPool(processes=3) mock = mocker.patch.object(pool, "_pool", autospec=True) with pool: pass mock.__enter__.assert_called_once() mock.__exit__.assert_called_once() @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_apply() -> None: """Test that a function can be applied.""" with ProcessPool(processes=3) as pool: assert pool.apply(square, (3,)) == 9 @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_apply_async() -> None: """Test that a function can be applied asynchronously.""" with ProcessPool(processes=3) as pool: assert pool.apply_async(square, (3,)).get() == 9 @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_map() -> None: """Test that a function can be mapped over an iterable of values.""" with ProcessPool(processes=3) as pool: assert sum(pool.map(square, [2] * 6)) == 24 @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_map_async() -> None: """Test that a function can be mapped over an iterable of values asynchronously.""" with ProcessPool(processes=3) as pool: assert sum(pool.map_async(square, [2] * 6).get()) == 24 @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_imap() -> None: """Test that mapped function results can be iterated.""" with ProcessPool(processes=3) as pool: total = 0 for result in pool.imap(square, [2] * 6): total += result assert total == 24 @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_imap_unordered() -> None: """Test that mapped function results can be iterated in any order.""" with ProcessPool(processes=3) as pool: assert sum(pool.imap_unordered(square, [2] * 6)) == 24 @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_starmap() -> None: """Test that a function can be starmapped over many iterables.""" with ProcessPool(processes=3) as pool: assert sum(pool.starmap(summation, [range(10), range(10), range(10)])) == 135 @pytest.mark.skipif("SKIP_MP" in os.environ, reason="unsafe for parallel execution") def test_starmap_async() -> None: """Test that a function can be starmapped over many iterables asynchronously.""" with ProcessPool(processes=3) as pool: assert ( sum(pool.starmap_async(summation, [range(10), range(10), range(10)]).get()) == 135 ) cobrapy-0.29.1/tests/test_util/test_solver.py000066400000000000000000000173641467301562300213650ustar00rootroot00000000000000"""Test functions of solver.py.""" import logging from typing import TYPE_CHECKING, Optional import numpy as np import pytest from cobra.exceptions import OptimizationError from cobra.util import solver as su if TYPE_CHECKING: from cobra import Model stable_optlang = ["glpk", "cplex", "gurobi"] optlang_solvers = [f"optlang-{s}" for s in stable_optlang if s in su.solvers] def test_solver_list() -> None: """Expect that at least the GLPK solver is found.""" assert len(su.solvers) >= 1 assert "glpk" in su.solvers def test_interface_str() -> None: """Test the string representation of solver interfaces.""" assert su.interface_to_str("nonsense") == "nonsense" assert su.interface_to_str("optlang.glpk_interface") == "glpk" assert su.interface_to_str("optlang-cplex") == "cplex" def test_solver_name() -> None: """Test that the default LP solver name is GLPK.""" assert su.get_solver_name() == "glpk" def test_choose_solver(model: "Model") -> Optional[su.SolverNotFound]: """Test that solver switching is working.""" so = su.choose_solver(model, "glpk") assert su.interface_to_str(so) == "glpk" if any(s in su.solvers for s in su.qp_solvers): qp_choice = su.choose_solver(model, qp=True) assert su.interface_to_str(qp_choice) in su.qp_solvers else: with pytest.raises(su.SolverNotFound): su.choose_solver(model, qp=True) def test_linear_reaction_coefficients(model: "Model") -> None: """Test that linear coefficients are identifiable in objective.""" coefficients = su.linear_reaction_coefficients(model) assert coefficients == {model.reactions.Biomass_Ecoli_core: 1} def test_fail_non_linear_reaction_coefficients(model: "Model") -> None: """Test failure of non-linear coefficient identification in reaction.""" model.solver = "optlang-glpk" with pytest.raises(ValueError) as error: model.objective = model.problem.Objective( model.reactions.ATPM.flux_expression**2 ) coefficients = su.linear_reaction_coefficients(model) assert coefficients == {} assert "GLPK only supports linear objectives." in str(error.value) def test_add_remove(model: "Model") -> None: """Test addition and removal of variables and constraints.""" v = model.variables new_var = model.problem.Variable("test_var", lb=-10, ub=-10) new_constraint = model.problem.Constraint( v.PGK - new_var, name="test_constraint", lb=0 ) su.add_cons_vars_to_problem(model, [new_var, new_constraint]) assert "test_var" in model.variables.keys() assert "test_constraint" in model.constraints.keys() su.remove_cons_vars_from_problem(model, [new_var, new_constraint]) assert "test_var" not in model.variables.keys() assert "test_constraint" not in model.constraints.keys() def test_add_remove_in_context(model: "Model") -> None: """Test addition and removal of variables and constraints within context.""" v = model.variables new_var = model.problem.Variable("test_var", lb=-10, ub=-10) with model: su.add_cons_vars_to_problem(model, [new_var]) su.remove_cons_vars_from_problem(model, [v.PGM]) assert "test_var" in model.variables.keys() assert "PGM" not in model.variables.keys() assert "test_var" not in model.variables.keys() assert "PGM" in model.variables.keys() def test_absolute_expression(model: "Model") -> None: """Test addition of an absolute expression.""" v = model.variables with model: parts = su.add_absolute_expression(model, 2 * v.PGM, name="test", ub=100) assert len(parts) == 3 assert "test" in model.variables.keys() assert "abs_pos_test" in model.constraints.keys() assert "abs_neg_test" in model.constraints.keys() assert "test" not in model.variables.keys() assert "abs_pos_test" not in model.constraints.keys() assert "abs_neg_test" not in model.constraints.keys() @pytest.mark.parametrize("solver", optlang_solvers) def test_fix_objective_as_constraint(solver: str, model: "Model") -> None: """Test fixing present objective as a constraint.""" model.solver = solver opt = model.slim_optimize() with model as m: su.fix_objective_as_constraint(model, 1.0, name="fixed") assert (m.constraints.fixed.expression - m.objective.expression).simplify() == 0 assert m.constraints.fixed.lb == pytest.approx(opt) assert "fixed" not in m.constraints su.fix_objective_as_constraint(model, name="fixed") assert ( model.constraints.fixed.expression - model.objective.expression ).simplify() == 0 assert m.constraints.fixed.lb == pytest.approx(opt) assert "fixed" in model.constraints @pytest.mark.parametrize("solver", optlang_solvers) def test_fix_objective_as_constraint_minimize(model: "Model", solver: str) -> None: """Test fixing present objective as a constraint but as a minimization.""" model.solver = solver model.reactions.Biomass_Ecoli_core.bounds = (0.1, 0.1) minimize_glucose = model.problem.Objective( model.reactions.EX_glc__D_e.flux_expression, direction="min" ) su.set_objective(model, minimize_glucose) su.fix_objective_as_constraint(model) fx_name = f"fixed_objective_{model.objective.name}" constr = model.constraints # Ensure that a solution exists on non-GLPK solvers. model.slim_optimize() assert (constr[fx_name].lb, constr[fx_name].ub) == ( None, model.solver.objective.value, ) @pytest.mark.parametrize("solver", optlang_solvers) def test_add_lp_feasibility(model: "Model", solver: str) -> None: """Test functionality to ensure LP feasibility.""" model.solver = solver with model: with model: su.add_lp_feasibility(model) assert "s_plus_succoa_c" in model.variables assert np.isclose(model.slim_optimize(), 0.0) model.reactions.EX_glc__D_e.lower_bound = 1 assert np.isnan(model.slim_optimize()) assert "s_plus_succoa_c" not in model.variables su.add_lp_feasibility(model) assert np.isclose(model.slim_optimize(), 1.3872307692307695) @pytest.mark.parametrize("solver", optlang_solvers) def test_add_lexicographic_constraints(model: "Model", solver: str) -> None: """Test addition of lexicographic constraints.""" model.solver = solver rxns = ["Biomass_Ecoli_core", "EX_glc__D_e", "EX_o2_e"] with model: out = su.add_lexicographic_constraints(model, rxns, ["max", "min", "max"]) print(model.reactions.Biomass_Ecoli_core.bounds) assert np.isclose(model.constraints[-3].lb, out[rxns[0]]) assert np.isclose(model.constraints[-2].ub, out[rxns[1]]) assert np.isclose(model.constraints[-1].lb, out[rxns[2]]) with model: su.add_lexicographic_constraints(model, rxns, "max") with model: su.add_lexicographic_constraints(model, rxns) def test_time_limit(large_model: "Model") -> None: """Test time limit while optimizing a model.""" if su.interface_to_str(large_model.problem) != "glpk": pytest.skip("requires GLPK") # It is done like this since optlang accepts inputs in seconds # whereas GLPK accepts milliseconds large_model.solver.configuration._smcp.tm_lim = 1 with pytest.warns(UserWarning): sol = large_model.optimize() assert sol.fluxes is not None with pytest.raises(OptimizationError): sol = large_model.optimize(raise_error=True) @pytest.mark.parametrize( "solver", [s for s in su.solvers if s in ["osqp", "coinor_cbc"]] ) def test_specialized_solver_warning(solver, caplog): """Test the warning for specialized solvers.""" with caplog.at_level(logging.WARNING): su.check_solver(solver) assert "are specialized solvers for" in caplog.text cobrapy-0.29.1/tests/test_util/test_util.py000066400000000000000000000027431467301562300210230ustar00rootroot00000000000000"""Test functions of util.py .""" import pytest from cobra.util import AutoVivification, format_long_string, show_versions @pytest.mark.parametrize( "input_string, expected_string", [ ( ( "This is a really long string, but is it long enough. " "I hope it is long enough so that format_long_string() function works." ), "This is a really long string, but is it long...", ), ("This is short string.", "This is short string."), ], ) def test_format_long_string(input_string: str, expected_string: str) -> None: """Test functionality of format long string.""" assert expected_string == format_long_string(input_string) def test_autovivification() -> None: """Test proper functionality of autovivification.""" test_data = AutoVivification() test_data["a"]["b"] = 1 test_data["c"]["d"] = 2 assert test_data["a"] == {"b": 1} assert test_data["c"] == {"d": 2} assert test_data["a"]["b"] == 1 assert test_data["c"]["d"] == 2 def test_show_versions(capsys) -> None: """Test output of dependency information.""" show_versions() captured = capsys.readouterr() lines = captured.out.split("\n") assert lines[1].startswith("Package Information") assert lines[2].startswith("------------------") assert lines[3].startswith("cobra") assert lines[5].startswith("Dependency Information") assert lines[6].startswith("------------------") cobrapy-0.29.1/tox.ini000066400000000000000000000046071467301562300145730ustar00rootroot00000000000000[tox] envlist = isort, black, flake8, safety, py3{8,9,10,11}, coverage [gh-actions] python = 3.8: install, safety, py38 3.9: install, py39 3.10: install, py310, coverage 3.11: install, py311 [testenv] extras = array deps= jsonschema osqp~=0.6 ; python_version < "3.10" pytest pytest-benchmark~=4.0 pytest-mock pytest-raises commands = pytest {posargs: --benchmark-skip} [testenv:coverage] setenv = SKIP_MP = 1 extras = array deps= jsonschema osqp~=0.6 ; python_version < "3.10" pytest pytest-benchmark~=4.0 pytest-cov pytest-mock pytest-raises commands = pytest --cov=cobra --cov-report=xml {posargs: --benchmark-skip} [testenv:isort] skip_install = True deps= isort commands= isort --check-only --diff {toxinidir}/src/cobra {toxinidir}/setup.py {toxinidir}/tests [testenv:black] skip_install = True deps= black commands= black --check --diff {toxinidir}/src/cobra {toxinidir}/setup.py {toxinidir}/tests [testenv:flake8] skip_install = True deps= flake8 flake8-docstrings flake8-bugbear commands= flake8 {toxinidir}/src/cobra {toxinidir}/setup.py {toxinidir}/tests [testenv:safety] deps= safety commands= safety check --full-report -i 70612 [testenv:install] skip_install = True deps= build twine commands= pip check {toxinidir} python -m build {toxinidir} twine check {toxinidir}/dist/* ################################################################################ # Testing tools configuration # ################################################################################ [pytest] testpaths = tests markers = raises [coverage:paths] source = src/cobra */site-packages/cobra [coverage:run] branch = true parallel = true [coverage:report] exclude_lines = # Have to re-enable the standard pragma pragma: no cover precision = 2 [isort] skip = __init__.py profile = black lines_after_imports = 2 known_first_party = cobra known_third_party = appdirs depinfo diskcache future httpx importlib_resources libsbml numpy optlang pandas pydantic pytest rich ruamel.yaml scipy swiglpk [flake8] exclude = __init__.py max-line-length = 88 # The following conflict with `black` which is the more pedantic. extend-ignore = E203 W503 D202