Stein and colleagues describe in their study, published in PLOS computational biology, how time series can help to study dynamics of the microbiota. Moreover, unlike usual cross sectional studies which lack a mechanistic understanding of the ecosystem’s structure and its response to external perturbations, modelling dynamics can help to predict and recover the microbiota temporal dynamics. For example, they modeled how antibiotics can help Clostridium difficile to perpetuate the intestinal ecosystem.
Conceptual figure highlighting the difference between our approach and the currently available methods for microbiota analysis.
Used input data are the temporal records of microbial total abundances (colored bars on left) and the temporal signal of external perturbations (e.g. presence/absence or concentration). (A) Example and list of current computational approaches used to analyze community data for microbiota studies. (B) Our approach uses ecological modeling to infer a network of microbial interactions, susceptibilities to external perturbations and growth rates. The inferred parameters are used in an ecological community model which can then be used to predict ecosystem dynamics and to identify steady states.
Richard R. Stein , Vanni Bucci et al. Ecological Modeling from Time-Series Inference: Insight into Dynamics and Stability of Intestinal Microbiota. PLoS Comp. Biol. Dec 2013.
