Exploratory action

SLIMMEST

Statistical Learning of the Intestinal Microbiota MEtabolism in Space and Time
Statistical Learning of the Intestinal Microbiota MEtabolism in Space and Time

Microbial communities are heterogenous collections of microorganisms interacting in spatially heterogenous environments. Understanding how they function is key to developing applications in health, ecology, and biotechnologies. SLIMMEST builds spatio-temporal models of microbial communities through a combination of metabolic modelling approaches: a community-scale qualitative modeling using logical methods, and a quantitative modeling using meta-models and numerical optimization. By weaving together these two traditionally orthogonal strategies, SLIMMEST will faithfully model the complex behavior of these communities while mitigating the numerical difficulties inherent in spatialized models. This methodology will be applied to build a numerical version of a well-established experimental model of the murine gut microbiota, with the long term goal of using upstream in silico exploration to optimize experimental planning, reduce costs and decrease animal use.

Inria teams involved
PLEIADE
In partnership with
INRAE

Members

Clemence Frioux

Scientific leader

Simon Labarthe

Scientific co-leader