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• W816410171 startingPage "e1004338" @default.
• W816410171 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." @default.
• W816410171 created "2016-06-24" @default.
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• W816410171 date "2015-06-23" @default.
• W816410171 modified "2023-09-23" @default.
• W816410171 title "Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome" @default.
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• W816410171 doi "https://doi.org/10.1371/journal.pcbi.1004338" @default.
• W816410171 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4478025" @default.
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