A gap-filling algorithm for prediction of metabolic interactions in microbial communities

摘要: The study of microbial communities and their interactions has attracted the interest scientific community, because potential for applications in biotechnology, ecology medicine. complexity interspecies interactions, which are key macroscopic behavior communities, cannot be studied easily experimentally. For this reason, modeling begun to leverage knowledge established constraint-based methods, have long been used studying analyzing metabolism individual species based on genome-scale metabolic reconstructions microorganisms. A main problem is that they usually contain gaps due genome misannotations unknown enzyme functions. This traditionally solved by using gap-filling algorithms add biochemical reactions from external databases reconstruction, order restore model growth. However, could evolve taking into account among coexist communities. In work, a method resolves at community level was developed. efficacy algorithm tested its ability resolve synthetic auxotrophic Escherichia coli strains. Subsequently, applied predict Bifidobacterium adolescentis Faecalibacterium prausnitzii, two present human gut microbiota, an experimentally Dehalobacter Bacteroidales ACT-3 community. can facilitate improvement models identification difficult identify Author summaryMicrobes compose most abundant form life our planet almost never found isolation, as live close association with one another other organisms. capacity dictates ways interaction well environment. microorganisms recognised driving force emergence properties understanding effect detrimental benefited insights offered methods developed interrogating models. paper, we predicts cooperative competitive between species, while it computationally efficient way. We use interesting environmental health-related applications.