ABI Innovation: Mining Metabolic and Enzyme Databases for the Composition of Non-Canonical Pathways

ABI 创新：挖掘代谢和酶数据库以组成非规范途径

• 批准号: 1262491
• 负责人: Lydia Kavraki
• 金额: $ 69.49万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262491&HistoricalAwards=false
• 关键词: ABI; Innovation; Mining; Metabolic; Enzyme

This project will develop and implement a set of algorithms for the discovery of complex metabolic pathways using information on metabolic pathways from different species available in databases such as KEGG and MetaCyc, and enzyme information available in databases such as BRENDA. The increasing availability of experimentally elucidated metabolic pathways together with the availability of enzyme functional data have ignited research in metabolic engineering and metabolomics. Recently, metabolic engineering has facilitated the production of complex pharmaceutical molecules by combining and manipulating metabolic pathways from multiple organisms. Metabolomic studies have revealed underlying causes of complex diseases and have helped diagnostic, prognostic, and treatment regimens as these evolve toward more individualized protocols. These spectacular advances have mostly relied upon laborious manual processes that are infeasible as more and more metabolic data are generated. The ability of the algorithms that will developed herein to discover known pathways will be investigated thoroughly and will include wetlab experimentation with two model studies. The studies involve niacin and shikonin, two therapeutic agents. Computational and wetlab experimentation will be conducted in parallel, with the computation feeding the experiment and the experiment providing valuable feedback for the tuning of the parameters of the corresponding algorithms. A web server will be built to facilitate the use of our tools and to widely disseminate our work in the scientific community. The research is timely and directed toward an important area of broad scientific interest with implications extending to fields such as health and energy. The project will provide training opportunities for graduate and undergraduate students. Participation in this type of research will provide graduate students with interdisciplinary training in metabolic engineering, metabolomics, systems biology, biochemistry, and molecular biology, as well as in mathematical modeling, algorithms, and computer science. The project will provide training opportunities and mentoring for women and minority graduate, undergraduate, and high school students. For more information, please visit: http://www.kavrakilab.org/projects/nsf_abi_1262491

该项目将使用来自数据库中不同物种（例如KEGG和Metacyc）的代谢途径的信息开发并实施一组算法，以发现复杂的代谢途径，以及Brenda等数据库中可用的酶信息。实验阐明的代谢途径的可用性增加，以及酶功能数据的可用性，引发了代谢工程和代谢组学的研究。最近，代谢工程通过结合和操纵多种生物的代谢途径来促进复杂的药物分子的产生。代谢组学研究揭示了复杂疾病的根本原因，并帮助诊断，预后和治疗方案有助于这些方案发展，这些方案朝着更加个性化的方案发展。这些壮观的进步主要依赖于艰苦的手动过程，这些过程是由于越来越多的代谢数据而变得不可行的。将对本文开发以发现已知途径的算法的能力进行彻底研究，并将包括通过两个模型研究进行Wetlab实验。这些研究涉及两种治疗剂烟酸和湿科蛋白。计算和湿lab实验将平行进行，计算供应实验，实验为调整相应算法的参数提供了宝贵的反馈。将构建Web服务器，以促进我们的工具使用并广泛地在科学界传播我们的工作。 这项研究是及时的，并针对广泛科学兴趣的重要领域，其影响延伸到健康和能源等领域。该项目将为研究生和本科生提供培训机会。参与此类研究将为研究生提供代谢工程，代谢组学，系统生物学，生物化学和分子生物学以及数学建模，算法和计算机科学的跨学科培训。该项目将为妇女和少数族裔，本科生和高中生提供培训机会和指导。 有关更多信息，请访问：http：//www.kavrakilab.org/projects/nsf_abi_1262491