|License| |PyPI version| |RTD version|
CoBAMP
============
*CoBAMP* (Constraint-Based Analysis of Metabolic Pathways) is a Python package containing pathway analysis methods
for use with constraint-based metabolic models. The main purpose is to provide a framework that is both modular and
flexible enough to be integrated in other packages (such as cobrapy, framed or cameo) that already implement generic
data structures for metabolic models.
CoBAMP depends on *optlang* (https://github.com/biosustain/optlang) for solving (mixed-integer) linear programming
problems, and thus, requires a compatible solver and Python dependency installed from the following list:
- `cplex <https://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/>`__ (preferred)
- `gurobi <http://www.gurobi.com>`__ (no explicit indicator variables)
- `glpk <https://www.gnu.org/software/glpk/>`__ (no explicit indicator variables or solution pools)
Current methods include:
- Elementary flux modes: K-Shortest algorithm
- Minimal cut sets: MCSEnumerator approach
- Elementary flux patterns: K-Shortest algorithm
Documentation
~~~~~~~~~~~~~
Documentation available at https://cobamp.readthedocs.io/
Instalation from PyPI (stable releases)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pip install cobamp
Credits and License
~~~~~~~~~~~~~~~~~~~
Developed at the Centre of Biological Engineering, University of Minho
Please refer to this work through this `publication <https://doi.org/10.1093/bioinformatics/btz598>`__ by Vieira and Rocha (2019):
- CoBAMP: a Python framework for metabolic pathway analysis in constraint-based models, *Bioinformatics*, btz598
Released under the GNU Public License (version 3.0).
.. |License| image:: https://img.shields.io/badge/license-GPL%20v3.0-blue.svg
:target: https://opensource.org/licenses/GPL-3.0
.. |PyPI version| image:: https://badge.fury.io/py/cobamp.svg
:target: https://badge.fury.io/py/cobamp
.. |RTD version| image:: https://readthedocs.org/projects/cobamp/badge/?version=latest&style=plastic
:target: https://cobamp.readthedocs.io/
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