Análisis in silico de la dinámica de comunidadesmicrobianas frente a perturbaciones usando modelosmetabólicos

Interest on microbial communities have grown in recent years due to their impact on human health, agricultural productivity, environmental bioremediation and industrial processes. Thanks to the advances in metagenomics, the understanding of the interactions and dynamics of these communities are increasing. Hence, data-driven approaches are applied to enable interpretation of metagenomics data, although the lack of metadata collection often hampers this kind of approaches. This work describes MMODES, an in silico approach, that was developed to simulate dynamics in multi-strain microbial communities influenced by perturbations, based on metabolic modeling and ordinary differential equations. In addition, it was combined with the existent microbiome data-driven framework MDPbiome. The whole workflow, called MDPbiomeGEM, was applied to suggest the best actions to move the microbial community to a better state in two particular cases of study: a human gut simplified microbiome, modeling how to recover from Crohn’s disease; and a soil microbiome where to improve the degradation of atrazine, a high-impact environmental herbicide.