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MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses
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Journal Article
MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses
2016
Overview
Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE’s utility on two new gnotobiotic mice datasets, investigating infection with
Clostridium difficile
and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations.
Publisher
BioMed Central,Springer Nature B.V
Subject
/ Animal Genetics and Genomics
/ Animals
/ Bacteria
/ Biomedical and Life Sciences
/ Clostridium difficile - genetics
/ Clostridium difficile - growth & development
/ Clostridium difficile - pathogenicity
/ E coli
/ Host-Pathogen Interactions - genetics
/ Method
/ Methods
/ Mice
/ Microbial Genetics and Genomics
