Microbial communities are an essential component of plant health, helping in nutrient acquisition and defense against pathogens. Despite their importance, the mechanisms behind their assembly and regulation remain poorly understood. Advances in sequencing and measuring technologies have enabled the collection of multi-omics data, including population counts on the abundance of microorganisms, metabolomic data on metabolite consumption and production, and metatranscriptomic data on gene activity within these communities. In order to answer the question of how these microorganisms function in the community and interact with one another, our main objective is to develop a mathematical model of dynamic systems capable of integrating these time series of multi-omics data at a community scale. Such a model will help to better decipher the functioning of the microbial community and understand its composition, knowing what each individual consume and produces. To achieve this goal, we introduce the community-scale metabolic flux analysis (cMFA) method.In this poster, we introduced the cMFA method, that we assessed on synthetic data from a dynamic model of increasingly complex microbial communities, built upon metabolic models of microorganisms. The observed growth rates were obtained from the spline smoothing of several replicates of the community dynamics. Synthetic meta-transcriptomic data were produced from metabolic fluxes in the dynamic model. Different regularization terms were tested, including different levels of sparsity, for a cross-validated penalty weight. The cMFA method, implemented in Python with OSQP, a software package dedicated to quadratic programming problems, allows for the recovery of the functioning of microbial individuals from multi-omics data acquired at the community scale during growth experiments.Read less <