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

氮循环和甲烷循环微生物的预测代谢网络模型

• 批准号: RGPIN-2019-04399
• 负责人: Stein, Lisa
• 金额: $ 3.64万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=744836
• 关键词: Predictive; Metabolic; Network; Modeling; Nitrogen

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.

基因组规模的代谢网络模型（GEM）是有机体用来支持其生活方式的完整化学反应集的计算机化表示。通过将生长，基因表达，代谢产物的代谢产生以及来自生物体的任何其他实验中的数据分层数据分层的数据来构建宝石。当迭代使用更多的数据集进行完善时，宝石可以准确地预测有机体在硅中的代谢产物。我们的长期目标是构建，完善和验证可靠的宝石，以预测何时参与环境和生物技术兴趣的氮气和甲烷循环生产和消费产品，例如温室气体，生物聚合物，有机酸和其他有用的代谢产物。氨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，并在生物业，可再生能源和废物管理中广泛应用。目标包括：1）为氨和甲烷氧化的细菌构建宝石，源自潜水员生态系统并具有独特的代谢能力，2）生成物理和基因组表达数据以完善宝石，以及3）验证与实验室实验相匹配的宝石的结果，以将模型与现实世界生物学匹配。将使用基于公开约束的重建和分析（COBRA）软件包构建宝石。我们将通过在一系列营养组合和环境参数的范围内纳入从细菌中收集的新生成的数据来完善宝石。最后，我们将通过在计算机代谢和基因缺失/添加实验中进行验证宝石，并与现实世界数据相匹配预测的结果。这项工作为微生物学，生物信息学，功能性基因组学和分子生物学的专家提供了独特的交叉训练经验。