Collaborative Research: Data-driven engineering of the thermotolerant yeast Kluyveromyces marxianus

合作研究：耐热酵母克鲁维酵母的数据驱动工程

• 批准号: 2225877
• 负责人: Nancy Da Silva
• 金额: $ 42.28万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225877&HistoricalAwards=false
• 关键词: Collaborative; Research; Data; driven; engineering

Metabolism involves a complex set of reactions and control mechanisms. This makes microbial behavior difficult to understand and engineer. Machine learning (ML) identifies patterns and relationships in complex sets of data. A yeast will be subject to varied efforts to increase its yield of biochemicals and biofuels. Machine learning will help identify synthetic biology approaches that maximize production. Also, educational and training resources will be expanded for students in K-6 afterschool programs, in K-12 summer camps, and in a Data Science Academy for high school students. The overall goal is to identify genes critical to driving high carbon flux to a desired central metabolite and product. A deep learning approach – DeepGuide – will be used to design optimized sgRNA libraries to generate genetic diversity. The build stage of the cycle will leverage efficient CRISPR-Cas9 technologies for gene disruption and regulation. A biosensor-driven approach to testing will enable high throughput analysis of the effect of host genetics on the production of malonyl-CoA, a key precursor to polyketides. The droplet microfluidics screening and analysis capabilities of the Agile BioFoundry (ABF) at Argonne National Laboratory will provide a wealth of additional information to link metabolite production with host genetics. The large datasets generated in the testing stage will be used as input for deep learning; a new algorithm linking genotypes to phenotypes – DeepMetabolism – will be used to predict a minimal set of genetic perturbations that maximize malonyl-CoA biosynthesis. This data-driven approach will advance both deep learning and high throughput approaches for microbial engineering and can be applied to other strategic metabolites.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.

代谢涉及一组复杂的反应和控制机制。这使微生物行为难以理解和设计。机器学习（ML）标识复杂数据集中的模式和关系。酵母将受到各种努力，以提高其生化物和生物燃料的产量。机器学习将有助于确定最大化生产的合成生物学方法。此外，在K-6课后课程，K-12夏令营和高中生数据科学学院的K-6课程计划中，教育和培训资源还将扩展。总体目标是确定对于将高碳通量驱动到所需的中央代谢产物和产品至关重要的基因。深度学习方法（深导）将用于设计优化的SGRNA库来产生遗传多样性。周期的构建阶段将利用有效的CRISPR-CAS9技术来进行基因破坏和调节。生物传感器驱动的测试方法将对宿主遗传学对丙酰coA的产生的影响进行高吞吐量分析，这是多酮化合物的关键先驱。 Argonne National Laboratory的敏捷生物基础（ABF）的液滴微流体筛查和分析能力将提供大量其他信息，以将代谢物生产与宿主遗传学联系起来。在测试阶段生成的大型数据集将用作深度学习的输入；将基因型与表型联系起来的一种新算法（深度代谢）将用于预测最小的遗传扰动集，从而最大程度地提高丙二酰coA生物合成。这种数据驱动的方法将推进微生物工程的深度学习和高通量方法，并可以应用于其他战略代谢物。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估值得支持。