Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome

Steven N. Steinway, Matthew B. Biggs, Thomas P. Loughran, Jason A. Papin, Reka Albert

Research output: Contribution to journalArticlepeer-review

68 Scopus citations

Abstract

We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions. We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic Clostridium difficile infection. Our model recapitulates known dynamics of clindamycin antibiotic treatment and C. difficile infection and predicts therapeutic probiotic interventions to suppress C. difficile infection. Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of our computational model, that B. intestinihominis can in fact slow C. difficile growth.

Original languageEnglish (US)
Article numbere1004338
JournalPLoS computational biology
Volume11
Issue number6
DOIs
StatePublished - Jun 23 2015

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

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