Predictive Metabolic Network Modeling of Nitrogen- and Methane-Cycling Microorganisms

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Genome-scale metabolic network models (GEMs) are computerized representations of the complete set of chemical reactions that an organism uses to support its lifestyle. GEMs are constructed by layering data from growth, gene expression, metabolite production, and any number of other experiments from an organism onto its well-curated genome sequence. When iteratively refined with more data sets, GEMs can accurately predict the products of an organism's metabolism in silico. Our long-term objective is to construct, refine, and validate robust GEMs that predict when microbes involved in nitrogen and methane cycling produce and consume products of environmental and biotechnological interest, such as greenhouse gases, biopolymers, organic acids, and other useful metabolites. Ammonia- and methane-oxidizing bacteria are essential players in controlling emissions of the greenhouse gases, nitrous oxide and methane, and have extensive applications to bioindustry, renewable energy, and waste management. Objectives include: 1) constructing GEMs for ammonia- and methane-oxidizing bacteria originating from diverse ecosystems and with unique metabolic capabilities, 2) generating physiological and genome expression data to refine the GEMs, and 3) validating outcomes predicted from the GEMs with laboratory experiments to match the models to real-world biology. GEMs will be constructed using the publicly available constraint-based reconstruction and analysis (COBRA) package. We will refine the GEMs by incorporating newly generated data collected from our bacteria under a range of nutrient combinations and environmental parameters. Last, we will validate GEMs by performing in silico metabolic and gene deletion/addition experiments and matching predicted outcomes with real-world data. This work offers a unique cross-training experience for students at the cutting-edges of microbiology, bioinformatics, functional genomics and molecular biology.