Richard R. Stein

TL;DR: A novel method to infer microbial community ecology directly from time-resolved metagenomics is presented, extending generalized Lotka–Volterra dynamics to account for external perturbations and suggests a subnetwork of bacterial groups implicated in protection against C. difficile.

TL;DR: MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors, is presented, demonstrating 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.

TL;DR: This work reviews undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables, and presents recently developed inference methods from the field of protein contact prediction.

TL;DR: This particular application of perturbation biology demonstrates the potential impact of combining time-resolved data with modeling for the discovery of new combinations of cancer drugs.