The goal of this proposal is to build a novel, computational metabolomics platform enabling efficient exploration of bacterial metabolites in the gastrointestinal (GI) tract. It is becoming increasingly evident that microbiota- derived metabolites mediate important signals in the context of inflammation and immunomodulation in the human GI tract. Despite intense interest, only a handful of bioactive microbiota metabolites in the GI tract have been identified. One major challenge is that the spectrum of metabolites present in the GI tract is extremely complex, as the microbiota can carry out a diverse range of biotransformation reactions, including those that are not present in the mammalian host. Classical approaches such as isolating and culturing individual bacteria and identifying metabolites produced in these cultures has not yielded much success, as many bacterial species in the GI tract cannot be cultured under standard laboratory conditions. Moreover, this approach also does not account for community-level interactions between the bacteria nor the interactions between host and bacteria. Thus, alternate methods of discovery are needed. Our approach is to model the microbiota as a metabolic network, and employ a probabilistic search to identify possible biotransformation products of selected metabolites that can be unambiguously attributed to bacteria. A critical new development is to capture the contributions of the host organism through its array of xenobiotic transformation enzymes. Since many of these enzymes exhibit a high degree of substrate flexibility, an algorithm based on pattern matching will be developed to augment the probabilistic search based on reaction definitions. To establish proof-of-concept, we plan to validate the predicted metabolites by performing targeted mass spectrometry measurements on fecal culture samples and characterize the bioactivity of the confirmed metabolites. Our specific aims are as follows. In Aim 1, we will build a metabolic network model of GI tract microbiota to enable focused predictions on bacterial biotransformation products. We will analyze the network model by developing a pathway analysis algorithm to predict and rank bacterial metabolites based on the likelihood that the relevant enzymes are expressed in the GI tract microbiota. We will validate the model predictions by analyzing murine fecal cultures as a surrogate experimental system for the GI tract microbiota. In Aim 2, we will augment the search algorithm of Aim 1 with predictions on probable host modifications computed from pattern recognition analysis of known CYP biotransformations. As in Aim 1, we will perform experimental validation of the model predictions using cultured hepatocytes as a surrogate system for the liver. These studies are expected to demonstrate the significant benefits of computational metabolic pathway analysis for targeted metabolomics, and provide a generally applicable methodology for identifying bioactive microbiota metabolites that are beneficial to human health.