Understanding the functioning of microbial communities is challenging due to the complexity of their interactions and assembly mechanisms. However, advances in sequencing technologies have enabled the collection of multi-omics data, including population counts and metabolomic or metatranscriptomic profiles. Our main objective is to develop a mathematical model capable of integrating time series of multi-omics data at the community scale.We introduce the community Metabolic Flux Analysis (cMFA) method: a biology-informed inference approach that generalizes classical Metabolic Flux Analysis. This high-dimensional analytical framework aims to estimate metabolic fluxes by integrating multi-omics data. Specifically, we aim to (i) quantify, for each member of the microbial community, their individual contributions to overall community dynamics based on external measurements of metabolite dynamics, and (ii) infer their intracellular distribution of metabolic fluxes. The difficulty here is in accurately inferring latent internal rates from a few observations of community-scale consumption and production rates for extracellular metabolites.We evaluated the cMFA method using synthetic data generated from dynamic models of microbial communities of increasing complexity using dynamic flux balance analysis, based on metabolic models of different Escherichia coli mutants. Synthetic metatranscriptomic data were obtained from internal metabolic fluxes simulated in the dynamic model. To evaluate the robustness of the method, multiplebenchmarks were tested. These included assessments of the robustness of the method to data noise,incomplete meta-transcriptomic data, inaccurate prior knowledge of metabolic import rates, and larger of microbial communities. We are currently finalizing various benchmarks and working with real experimental data.< Réduire