Stochastic models allow improved inference of microbiome interactions from time series data.

ABSTRACT: How can we figure out how the different microbes interact within microbiomes? To combine theoretical models and experimental data, we often fit a deterministic model for the mean dynamics of a system to averaged data. However, in the averaging procedure a lot of information from the data is lost-and a deterministic model may be a poor representation of a stochastic reality. Here, we develop an inference method for microbiomes based on the idea that both the experiment and the model are stochastic. Starting from a stochastic model, we derive dynamical equations not only for the average, but also for higher statistical moments of the microbial abundances. We use these equations to infer distributions of the interaction parameters that best describe the biological experimental data-improving

SUBMITTER: Zapien-Campos R 

PROVIDER: S-EPMC11620570 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature