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.

新陈代谢涉及一系列复杂的反应和控制机制，这使得机器学习（ML）难以识别和设计复杂数据中的模式和关系，以提高其生化物质的产量。机器学习将有助于确定最大限度提高产量的合成生物学方法。此外，还将为 K-6 课外项目、K-12 夏令营和高中生数据科学学院的学生提供教育和培训资源。 .总体目标是确定深度学习方法 DeepGuide 将用于设计优化的 sgRNA 文库，以产生遗传多样性，该循环的构建阶段将利用高效的 CRISPR-Cas9 基因技术。生物传感器驱动的测试方法将能够高通量分析宿主遗传学对丙二酰辅酶A（聚酮化合物的关键前体）产生的影响。阿贡国家实验室的 Agile BioFoundry (ABF) 将提供大量附加信息，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；一种将基因型与表型联系起来的新算法。 DeepMetabolism——将用于预测最大限度地提高丙二酰辅酶A生物合成的最小遗传扰动，这种数据驱动的方法将推动微生物工程和高通量方法的发展。可应用于其他战略代谢物。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。