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The default solver is glpk (for LP and MILP). You can install TOMLAB, IBM ILOG CPLEX, GUROBI, or MOSEK by following these detailed instructions.
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Download this repository (the folder
./cobratoolbox/will be created). You can clone the repository using:$ git clone https://github.com/opencobra/cobratoolbox.git cobratoolbox
Run this command in
Terminal(onand
) or in
Git Bash(on) - not in
. Although not recommended, you can download the repository as a compressed archive.
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Change to the folder
cobratoolbox/and run from>> initCobraToolbox -
You can test your installation by running from
>> testAll
All tutorials are included in the folder tutorials. More tutorials are currently being prepared.
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The documentation is available here. This version is in development, but the legacy version of the documentation is here.
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Answers to Frequently Asked Questions (FAQ) are here.
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If you need support, please feel free to post your question in our
.
- Please follow the Style Guide.
- More information on writing a test is here and a template is here.
- A guide for reporting an issue or submitting a pull request (PR) is here.
If you want to use git via the command line interface and need help, these training slides will get you started. This guide or the official GitHub guide also come in handy.
When citing The COBRA Toolbox, it is important to cite the original paper where an algorithm was first reported, as well as its implementation in The COBRA Toolbox. This is important, because the objective of The COBRA Toolbox is to amalgamate and integrate the functionality of a wide range of COBRA algorithms and this will be undermined if contributors of new algorithms do not get their fair share of citations. The following is one example how to approach this within the methods section of a paper (not the supplemental material please):
To generate a context-specific model the FASTCORE algorithm [1], implemented in The COBRA Toolbox [2], was employed.
[1] = Vlassis N, Pacheco MP, Sauter T (2014) Fast Reconstruction of Compact Context-Specific Metabolic Network Models. PLoS Comput Biol 10(1): e1003424.
[2] = Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ. 2011 Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox v2.0. Nature Protocols 6:1290-1307.
For convenience, we provide the SBMLToolbox 4.1.0, and glpk_mex in external/toolboxes, libSBML-5.13.0-matlab in src/io/utilities.
Binaries for these libraries are provided in a submodule for Mac OS X 10.6 or later (64-bit), GNU/Linux Ubuntu 10.0 (64-bit), and Microsoft Windows 7 (64-bit).
For unsupported OS, please refer to their respective building instructions (glpk_mex, libSBML).
Read more on the compatibility with SBML-FBCv2 here.
The software provided by the openCOBRA Project is distributed under the GNU GPLv3 or later. However, this software is designed for scientific research and as such may contain algorithms that are associated with patents in the U.S. and abroad. If the user so chooses to use the software provided by the openCOBRA project for commercial endeavors then it is solely the user’s responsibility to license any patents that may exist and respond in full to any legal actions taken by the patent holder.