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

基因组规模的代谢网络模型（GEM）是一套完整的化学反应的计算机化表示，生物体用于支持其生活方式。GEM是通过将来自生物体的生长、基因表达、代谢产物产生和任何数量的其他实验的数据分层到其精心策划的基因组序列上来构建的。当使用更多的数据集进行迭代优化时，GEM可以通过计算机准确预测生物体代谢的产物。我们的长期目标是构建，完善和验证强大的GEM，预测参与氮和甲烷循环的微生物何时产生和消耗具有环境和生物技术意义的产品，如温室气体，生物聚合物，有机酸和其他有用的代谢产物。氨氧化细菌和甲烷氧化细菌是控制温室气体、一氧化二氮和甲烷排放的重要参与者，并在生物工业、可再生能源和废物管理方面有着广泛的应用。目标包括：1）构建来自不同生态系统并具有独特代谢能力的氨和甲烷氧化细菌的GEM，2）生成生理和基因组表达数据以改进GEM，以及3）用实验室实验验证从GEM预测的结果，以将模型与现实世界的生物学相匹配。GEM将使用公开可用的基于约束的重建和分析（COBRA）包构建。我们将通过在一系列营养组合和环境参数下从我们的细菌中收集新生成的数据来完善GEM。最后，我们将通过计算机模拟代谢和基因删除/添加实验以及将预测结果与真实数据相匹配来验证GEM。这项工作为微生物学，生物信息学，功能基因组学和分子生物学前沿的学生提供了独特的交叉培训经验。

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.