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

该项目将开发和实施一套算法，利用 KEGG 和 MetaCyc 等数据库中提供的不同物种的代谢途径信息以及 BRENDA 等数据库中提供的酶信息来发现复杂的代谢途径。实验阐明的代谢途径的不断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，代谢工程通过组合和操纵多种生物体的代谢途径，促进了复杂药物分子的生产。代谢组学研究揭示了复杂疾病的根本原因，并有助于诊断、预后和治疗方案，因为这些方案正在朝着更加个体化的方案发展。这些惊人的进步主要依赖于费力的手动过程，随着越来越多的代谢数据的产生，这些过程是不可行的。本文开发的算法发现已知途径的能力将得到彻底研究，并将包括两个模型研究的湿实验室实验。这些研究涉及烟酸和紫草素这两种治疗剂。计算和湿实验室实验将并行进行，计算为实验提供支持，实验为相应算法参数的调整提供有价值的反馈。将建立一个网络服务器，以方便使用我们的工具并在科学界广泛传播我们的工作。 这项研究是及时的，针对具有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、算法和计算机科学方面的跨学科培训。该项目将为女性和少数族裔研究生、本科生和高中生提供培训机会和指导。 欲了解更多信息，请访问：http://www.kavrakilab.org/projects/nsf_abi_1262491