FET: Small: Tools and Experimental Validation for Predicting Enzymatic Promiscuity and its Products

FET：小型：预测酶乱性及其产物的工具和实验验证

• 批准号: 1909536
• 负责人: Soha Hassoun
• 金额: $ 51.58万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909536&HistoricalAwards=false
• 关键词: FET; Small; Tools; Experimental; Validation

The engineering of microbial cells through synthetic biology promises to advance the production of high-volume commodity chemicals such as biopolymers, fuels, nutraceuticals, therapeutics, and other specialty products. Increasing understanding of metabolic and regulatory networks underlying microbial physiology hinges on developing metabolic models that capture enzymatic cellular activity. Despite significant progress in sequencing technology and model reconstruction, there are many cellular enzymatic activities that remain unknown. The hypothesis underlying this project is that undocumented promiscuous activity of enzymes results in the formation of unexpected reaction byproducts. This phenomenon is frequently observed by synthetic biologists, and sometimes exploited during design through ad hoc experimental efforts. However, there are currently limited ways to predict, analyze, or mitigate the effects of enzyme promiscuity. This project develops tools to predict stoichiometrically balanced reactions that reflect undocumented promiscuous enzymatic cellular activities. These reactions can be utilized to augment existing metabolic models and improve design tools. Further, the project experimentally validates the augmented models by engineering a microbial host to synthesize desirable chemicals and then analyzing host-pathway interactions. The proposed work is at the intersection of computer science and biology and will advance the ability to predict and assess the impact of enzyme promiscuity on biological systems. Training will be provided to graduate and undergraduate students and the research will be incorporated into classroom teaching. Underrepresented minorities in computing will be recruited to participate in the research through various established national and local programs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

通过合成生物学对微生物细胞进行工程改造有望推进生物聚合物、燃料、营养药、治疗药和其他特种产品等大批量商品化学品的生产。增加对微生物生理学基础的代谢和调控网络的理解取决于开发捕获酶细胞活性的代谢模型。尽管在测序技术和模型重建方面取得了重大进展，但仍有许多细胞酶活性仍然未知。该项目的假设是，未记录的混杂酶活性导致形成意想不到的反应副产物。这种现象经常被合成生物学家观察到，有时在设计过程中通过特别的实验努力加以利用。然而，目前有有限的方法来预测，分析，或减轻酶混杂的影响。该项目开发工具来预测化学计量平衡反应，反映了未记录的混杂酶细胞活性。这些反应可用于增强现有的代谢模型和改进设计工具。此外，该项目通过工程改造微生物宿主来合成所需的化学物质，然后分析宿主-途径相互作用，从而实验验证了增强模型。拟议的工作是在计算机科学和生物学的交叉点，并将提高预测和评估酶混杂对生物系统的影响的能力。将向研究生和本科生提供培训，并将研究纳入课堂教学。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Stochastic Iterative Graph Matching

• 发表时间: 2021-06
• 期刊: ArXiv
• 作者: Linfeng Liu;M. Hughes;S. Hassoun;Liping Liu