MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses

Vanni Bucci, Belinda Tzen, Ning Li, Matt Simmons, Takeshi Tanoue, Elijah Bogart, Luxue Deng, Vladimir Yeliseyev, Mary L. Delaney, Qing Liu, Bernat Olle, Richard R. Stein, Kenya Honda, Lynn Bry, Georg K. Gerber

Research output: Contribution to journalArticle

65 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number121
JournalGenome biology
Volume17
Issue number1
DOIs
Publication statusPublished - 2016 Jun 3
Externally publishedYes

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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    Bucci, V., Tzen, B., Li, N., Simmons, M., Tanoue, T., Bogart, E., Deng, L., Yeliseyev, V., Delaney, M. L., Liu, Q., Olle, B., Stein, R. R., Honda, K., Bry, L., & Gerber, G. K. (2016). MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses. Genome biology, 17(1), [121]. https://doi.org/10.1186/s13059-016-0980-6