This digital microbial ecology PhD project aims at identifying - using artificial intelligence algorithms - robust, even universal, signatures associated with microbiota-diet-health interactions, with the goal of developing precision therapies and nutritional recommendations.
This project is divided into three aims: i) identifying and characterizing homogeneous strata of intestinal microbiota within different cohorts in the general population, including international cohorts, ii) highlighting existing guilds within these homogeneous strata through network inference approaches, and iii) using modeling approaches to elucidate the metabolism and functional niches of these ecological units as well as their potential interactions. The developed stratifications and associated mechanistic models aim to detect determinants of dysbiotic shift in disrupted ecosystems and to formulate hypotheses on the underlying metabolic mechanisms of these changes.
The deployment of these methods on large-scale sampling data would be necessary to explore the degree of modeling/simplification required to capture the natural heterogeneity of the intestinal microbiome. Thanks to an access to large cohorts like Lifelines-DEEP metagenomic samples, we are aiming at improving diagnostics, prognostics, and therapeutic interventions in stratified populations.