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Medusa: Software to build and analyze ensembles of genome-scale metabolic network reconstructions

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  • Gregory L Medlock
  • Thomas J Moutinho
  • Jason A Papin

Abstract

Uncertainty in the structure and parameters of networks is ubiquitous across computational biology. In constraint-based reconstruction and analysis of metabolic networks, this uncertainty is present both during the reconstruction of networks and in simulations performed with them. Here, we present Medusa, a Python package for the generation and analysis of ensembles of genome-scale metabolic network reconstructions. Medusa builds on the COBRApy package for constraint-based reconstruction and analysis by compressing a set of models into a compact ensemble object, providing functions for the generation of ensembles using experimental data, and extending constraint-based analyses to ensemble scale. We demonstrate how Medusa can be used to generate ensembles and perform ensemble simulations, and how machine learning can be used in conjunction with Medusa to guide the curation of genome-scale metabolic network reconstructions. Medusa is available under the permissive MIT license from the Python Packaging Index (https://pypi.org) and from github (https://github.com/opencobra/Medusa), and comprehensive documentation is available at https://medusa.readthedocs.io/en/latest.

Suggested Citation

  • Gregory L Medlock & Thomas J Moutinho & Jason A Papin, 2020. "Medusa: Software to build and analyze ensembles of genome-scale metabolic network reconstructions," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-11, April.
    • Handle: RePEc:plo:pcbi00:1007847
    DOI: 10.1371/journal.pcbi.1007847
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    1. Daniel Silk & Paul D W Kirk & Chris P Barnes & Tina Toni & Michael P H Stumpf, 2014. "Model Selection in Systems Biology Depends on Experimental Design," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-14, June.
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