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About this fork

This is a modified version used in the paper "In silico modelling of liver metabolism in a human disease reveals a key enzyme for histidine and histamine homeostasis", Cell Reports, 2016.

This version mainly fixes a problem with importing metabolites from a "b" compartment (as opposed to consider them "boundary" metabolites).

The original documentation follows.

cobrapy

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COnstraint-Based Reconstruction and Analysis (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 accomodate 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.

To install, please follow the instructions.

The documentation is browseable online at readthedocs and can also be downloaded.

Please use the Google Group for help. 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

License

The cobrapy source is released under both the GPL and LGPL licenses. You may choose which license you choose to use the software under. However, please note that binary packages which include GLPK (such as the binary wheels for Windows and Mac OS X distributed at https://pypi.python.org/pypi/cobra) will be bound by its license as well.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License or the Lesser GNU 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.

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COBRApy is a package for constraints-based modeling of biological networks

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