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PyCoMo

What is PyCoMo?

PyCoMo is a python 3 package for the creation and analysis of community metabolic models. More specifically, PyCoMo generates compartmentalized community metabolic models with a structure allowing simulations under fixed growth rate, but variable abundance profile, or fixed abundance profile and variable community growth rate. The community metabolic models generated by PyCoMo can switch between these two structures and retain the original reaction bounds of the input member models. As also all metabolites, reactions, genes and compartments are directly attributable to their member of origin, PyCoMo community metabolic models are fully reusable.

The community models can be analysed with PyCoMo to predict all feasible exchange metabolites and cross-feeding interactions, for the whole space of growth rate and abundance profiles. The community models are COBRApy models and can therefor be directly used by other COBRA methods. It is also possible to save and load the community models in SBML format, allowing to share and reuse the models built with PyCoMo.

Installation

PyCoMo can be installed via pip:

pip install pycomo

Or via conda:

conda install -c conda-forge -c bioconda pycomo

Alternatively PyCoMo can be installed from the GitHub repository. First clone the repository to your machine.

git clone https://github.com/univieCUBE/PyCoMo

Next run pip install on the folder containing the PyCoMo repository.

pip install path/to/PyCoMo

Usage guide

PyCoMo can be imported in Python as any other package. Please look through the tutorial for a walkthrough of all the options generating and analysing community metabolic models (available as ipython notebook, python file and pdf).

PyCoMo can also be used via its command line interface. After installation, run pycomo -h or pycomo --help to see all options.

Citing PyCoMo

Michael Predl, Marianne Mießkes, Thomas Rattei, Jürgen Zanghellini, PyCoMo: a python package for community metabolic model creation and analysis, Bioinformatics, Volume 40, Issue 4, April 2024, btae153, https://doi.org/10.1093/bioinformatics/btae153