Bucci, VanniTzen, BelindaLi, NingSimmons, MattTanoue, TakeshiBogart, ElijahDeng, LuxueYeliseyev, VladimirDelaney, MaryLiu, QingOlle, BernatStein, Richard R.Honda, KenyaBry, LynnGerber, Georg2016-07-142016Bucci, V., B. Tzen, N. Li, M. Simmons, T. Tanoue, E. Bogart, L. Deng, et al. 2016. “MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses.” Genome Biology 17 (1): 121. doi:10.1186/s13059-016-0980-6. http://dx.doi.org/10.1186/s13059-016-0980-6.1474-7596http://nrs.harvard.edu/urn-3:HUL.InstRepos:27662168Predicting 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. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0980-6) contains supplementary material, which is available to authorized users.en-USJournal Article2016-07-1410.1186/s13059-016-0980-6