RUI: Automated Metabolic Reconstruction for All Sequenced Microbial Genomes

RUI：所有测序微生物基因组的自动代谢重建

• 批准号: 0745100
• 负责人: Matthew DeJongh
• 金额: $ 23.5万
• 依托单位: Hope College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-15 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0745100&HistoricalAwards=false
• 关键词: RUI; Automated; Metabolic; Reconstruction; Sequenced

Computer models of microbial metabolism have proven useful for understanding the biochemical processes by which organisms transform substrates into biomass components and energy. These computer models consist in part of a network representing the biochemical reactions catalyzed by the enzymes encoded in an organism's genome. The challenge of metabolic reconstruction is to create a complete reaction network suitable for systems level analysis directly from an annotated genome. Metabolic reconstruction is still largely a manual process, and to date metabolic reconstructions have been published for only a handful of organisms. Thus, there is an urgent need for more substantial automation of this process. The research will take a breadth-first approach to generating substantially complete metabolic reconstructions for all sequenced microbial genomes. This approach involves an iterative process of identifying areas of metabolism encoded in microbial genomes that are not yet represented in the database, creating new reaction network components for them, and generating updated metabolic reconstructions. Each of these metabolic reconstructions will require some degree of manual refinement to reach completion. Therefore, software tools will be developed and validated by producing complete metabolic reconstructions for a small set of selected organisms. A web site will be created to make available to the scientific community all of the metabolic reconstructions and refinement tools. The database of metabolic components and software will be fully integrated with the SEED, a widely used comparative genome annotation and analysis environment, and the principal investigators will collaborate with the SEED community to disseminate the research results. The database and software developed will significantly reduce the amount of manual effort required to create complete metabolic reconstructions that are suitable for systems level analysis. This will provide high-quality starting points for modeling efforts by organism-specific scientific communities. Metabolic reconstructions for diverse microbes will lead to new scientific investigations into their phylogenetic relationships as well as new methods for analyzing genomic, metabolomic and proteomic data. Broader Impacts: Metabolic reconstructions for diverse microbes will lead to new applications in industrial, medical and environmental contexts. This project will provide undergraduate students in biology and computer science with hands-on experience in interdisciplinary research that will prepare them for graduate work or other scientific activity. The research activities will also feed back into courses in bioinformatics, scientific computing, and microbiology. The program will draw on Hope College's existing collaborations with area community colleges and the local public school system to provide research opportunities to students from underrepresented groups in the sciences.

微生物代谢的计算机模型已被证明有助于了解生物体将底物转化为生物质组分和能量的生化过程。这些计算机模型包括一个网络的一部分，该网络代表生物体基因组中编码的酶催化的生化反应。代谢重建的挑战是直接从注释的基因组创建适合于系统水平分析的完整反应网络。代谢重建在很大程度上仍然是一个手动的过程，迄今为止，代谢重建已被公布的只有少数生物体。因此，迫切需要对这一过程进行更大程度的自动化。该研究将采取广度优先的方法，为所有测序的微生物基因组生成基本完整的代谢重建。这种方法涉及一个迭代过程，即识别在数据库中尚未表示的微生物基因组中编码的代谢区域，为它们创建新的反应网络组件，并生成更新的代谢重建。这些代谢重建中的每一个都需要一定程度的手动细化才能完成。因此，将通过为一小组选定的生物体生成完整的代谢重建来开发和验证软件工具。将建立一个网站，向科学界提供所有代谢重建和改进工具。代谢组分和软件的数据库将与SEED（一个广泛使用的比较基因组注释和分析环境）完全集成，主要研究人员将与SEED社区合作传播研究成果。 开发的数据库和软件将显著减少创建适合系统级分析的完整代谢重建所需的手动工作量。这将为特定生物科学界的建模工作提供高质量的起点。不同微生物的代谢重建将导致对它们的系统发育关系进行新的科学研究，以及分析基因组学，代谢组学和蛋白质组学数据的新方法。更广泛的影响：不同微生物的代谢重建将在工业，医疗和环境方面产生新的应用。该项目将为生物学和计算机科学的本科生提供跨学科研究的实践经验，为他们的研究生工作或其他科学活动做好准备。研究活动还将反馈到生物信息学，科学计算和微生物学课程中。该计划将利用霍普学院与地区社区学院和当地公立学校系统的现有合作，为科学领域代表性不足的群体的学生提供研究机会。