SUMMARY It is now recognized that instability and dysbiosis of physiologically important communities, such as the gut microbiome, may contribute to a wide range of human disease, from immune disorders to psychiatric phenotypes to obesity. This is a problem of preeminent medical importance as dietary imbalances, shaping the identity and abundance of resident bacteria, affect the health of many millions of Americans on a daily basis. Despite the underlying complexity, our preliminary analyses revealed that the dynamics of gut bacteria can be in fact described by several robust statistical relationships. Moreover, the relationships characterizing microbiota fluctuations are strikingly similar to patterns previously observed across multiple other ecological and economic systems. We have also recently developed novel high-throughput experimental and computational approaches to characterize likely metabolic interactions at the micron scale and across different diets. We propose to use an integrated computational and experimental approach to comprehensively investigate diet-dependent dynamics and stability of gut microbiota: Aim1. Develop and implement a set of complementary computational approaches for probabilistic prediction of microbial metabolic phenotypes. Aim2. Collect temporal data on absolute bacterial abundances in the gut across several health-related diets and common prebiotic supplements in mice models. Apply a quantitative ecological framework to comprehensively investigate microbiota stability and dynamics on different diets. Aim 3. Collect spatial co-localization information on the micron scale and across multiple diets. Combine co-localization with probabilistic metabolic annotations to investigate the nature of potential cooperative and competitive metabolic interactions between microbial species in the gut. Investigate the diet- dependent stability of bacterial interactions in space and time. Close the experimental- computational loop by validating several dozens of high-confident interactions in vitro.!