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

代谢涉及一系列复杂的反应和控制机制。这使得微生物的行为难以理解和工程。机器学习（ML）识别复杂数据集中的模式和关系。酵母将受到各种努力，以增加其生物化学品和生物燃料的产量。 机器学习将有助于识别最大化生产的合成生物学方法。此外，教育和培训资源将扩大到K-6课后计划，K-12夏令营和高中生数据科学学院的学生。总的目标是确定基因的关键驱动高碳通量所需的中央代谢产物和产品。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。该周期的构建阶段将利用高效的CRISPR-Cas9技术进行基因破坏和调控。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生产的影响，丙二酰辅酶A是聚酮化合物的关键前体。阿贡国家实验室的Agile BioFoundry（ABF）的液滴微流体筛选和分析能力将提供丰富的额外信息，以将代谢产物与宿主遗传学联系起来。在测试阶段生成的大型数据集将被用作深度学习的输入;将基因型与表型联系起来的新算法- DeepMetabolism -将被用于预测最小的遗传扰动集，从而最大化丙二酰辅酶A的生物合成。该奖项反映了NSF的法定使命，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。

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.