cobamp - pathway analysis methods for genome-scale metabolic models
Project description
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 (preferred)
gurobi (no explicit indicator variables)
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 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).
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