A computational framework has been developed to infer species dynamics and interactions, with potential applications in microbiota ecology. The framework appears to involve multi-omic network inference from time-series data and may utilize machine learning approaches to understand microbiome-host interactions. It has been applied to study metabolic interactions in the gut microbiota and the dynamics of microbial communities.