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A python implementation of flux balance analysis to model microbial metabolism

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PyFBA

A python implementation of flux balance analysis to model microbial metabolism. Read more ...

About PyFBA

PyFBA is a Python flux-balance-analysis package that allows you to build models from genomes, gapfill models, and run flux-balance-analysis on that model. The aim of PyFBA is to provide an extensible, python-based platform for FBA work.

PyFBA is being developed by Daniel Cuevas, Taylor O'Connell, and Rob Edwards in Rob's bioinformatics group at San Diego State University together with help from Janaka Edirisinghe, Chris Henry, Ross Overbeek and others at Argonne National Labs.

You can read more about PyFBA on our github.io pages.

Installing PyFBA

To use PyFBA you need Python 3.0 or greater, and you need to install the GNU GLPK and a Python wrapper for that program, pyGLPK available from github.

We also leverage the Model SEED GitHub repository with all the latest biochemistry tables.

Our installation page has detailed instructions on installing PyFBA and getting everything running.

Getting Started with PyFBA

Once you have installed GLPK, PyGLPK, and PyFBA, you will most likely want to build a model from a genome, gap fill that model, and test it for growth on different media. We have detailed instructions that walk you through the step-by-step procedures that you need to use to run flux balance analysis on your own genome.

Updating to Python 3

This branch of PyFBA is compatible with Python 3, however that currently breaks compatibility with Python 2 because we use an open syntax that is not supported in earlier Python versions.

To update to Python 3 from a working Python 2 installation, you will need to reinstall pyGLPK available from github. Checkout that code, cd into the directory, and then install it using python3 setup.py. Everything else should work just fine.

If you cd into the PyBFA directory, nosetests3 tests/ should successfully run 77 tests with no errors.

Copyright and License

PyFBA is copyright Daniel Cuevas, Taylor O'Connell, and Rob Edwards, and is released under the MIT license.

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