A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Marko Budinich 1 Jérémie Bourdon 1 Abdelhalim Larhlimi 1 Damien Eveillard 1
1 COMBI - Combinatoire et Bioinformatique
LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.
Type de document :
Article dans une revue
PLoS ONE, Public Library of Science, 2017, 12 (2), pp.e0171744
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Contributeur : Damien Eveillard <>
Soumis le : mardi 28 février 2017 - 10:29:39
Dernière modification le : jeudi 19 avril 2018 - 11:46:05


  • HAL Id : hal-01478375, version 1
  • PUBMED : 28187207



Marko Budinich, Jérémie Bourdon, Abdelhalim Larhlimi, Damien Eveillard. A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.. PLoS ONE, Public Library of Science, 2017, 12 (2), pp.e0171744. 〈hal-01478375〉



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