Collaborative Research: Productivity Prediction of Microbial Cell Factories using Machine Learning and Knowledge Engineering

合作研究：利用机器学习和知识工程预测微生物细胞工厂的生产力

• 批准号: 1616216
• 负责人: Forrest Sheng Bao
• 金额: $ 23.25万
• 依托单位: University of Akron
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1616216&HistoricalAwards=false
• 关键词: Collaborative; Research; Productivity; Prediction; Microbial

Over the past decade, systems and synthetic biology approaches provided novel mechanism to enhance the production of diverse chemicals and biofuels from renewable resources in laboratory settings. However, it is still rare for synthetically modified strains to meet the production requirement for commercialization. Strain development falls into the tedious and costly design-build-test-learn cycle because existing modeling approaches failed to capture the complicated metabolic responses in such engineered cells. This proposal will explore an alternate, data-driven approach that has the potential to predict the productivity of synthetic organisms by leveraging the vast array of microbial cell factory publications. Using Artificial Intelligence approaches such as Machine Learning and Knowledge Representation, one can abstract "previous lessons'' hidden in published data to facilitate a priori estimations of the metabolic output by engineered hosts given a set of specific genetic instructions and fermentation growth conditions. The resulting platform can assist current constraint-based models to design the most effective strategies for producing value-added chemicals. On the educational front, this proposal will offer educational and research training opportunities in synthetic biology, computer programming, and artificial intelligence for graduate students to provide them with a non-conventional career pathway. Synthetic biology relies on extensive genetic modification and pathway engineering, which often result in unexpected physiological changes or metabolic shifts that reduce the productivity and stability of the hosts. The investigators conceived of a creative, multidisciplinary approach that relies on artificial intelligence-inspired methods for predicting the performance of two distinct unicellular cell factories (Escherichia coli and Saccharomyces cerevisiae). These platforms can be used to quantify the factors that govern microbial productivity (yield, titer, and growth rate), including the type and availability of metabolic precursors; the elements that constitute a biosynthetic pathway; fermentation conditions; and the specific genetic modification to optimize the system. By extracting and classifying information derived from referenced publications within the last 20 years, one can construct a ''knowledge base'' containing sufficient samples of bio-production assemblies. This information will then inform the building of cellular factories using supervised machine learning and non-monotonic logic programming to estimate the productivity of hosts. The data-driven platform will also be integrated into genome scale models to project physiological changes of specific mutant strains. This novel approach will reduce the need for costly design-build-test bench work. Key outcomes from this project include: (1) a database to standardize synthetic biology studies, (2) machine learning models to recognize lessons and patterns hidden in published data, and (3) integration of machine learning with flux balance models, leading to the design of strains with high chances of success in industry settings.

在过去的十年中，系统和合成生物学方法提供了新的机制，以提高在实验室环境中从可再生资源生产各种化学品和生物燃料。然而，目前人工合成的能够满足商业化生产要求的菌株还很少见。菌株开发福尔斯了繁琐而昂贵的设计-构建-测试-学习周期，因为现有的建模方法未能捕获此类工程细胞中复杂的代谢反应。该提案将探索一种替代的、数据驱动的方法，该方法有可能通过利用大量的微生物细胞工厂出版物来预测合成生物的生产力。使用人工智能方法，如机器学习和知识表示，人们可以提取隐藏在已发表数据中的“以前的经验教训”，以促进对给定一组特定遗传指令和发酵生长条件的工程宿主的代谢输出的先验估计。由此产生的平台可以帮助目前基于约束的模型设计最有效的战略，以生产增值化学品。在教育方面，该提案将为研究生提供合成生物学、计算机编程和人工智能方面的教育和研究培训机会，为他们提供非传统的职业途径。合成生物学依赖于广泛的遗传修饰和途径工程，这通常会导致意想不到的生理变化或代谢变化，从而降低宿主的生产力和稳定性。研究人员设想了一种创造性的多学科方法，该方法依赖于人工智能启发的方法来预测两种不同的单细胞细胞工厂（大肠杆菌和酿酒酵母）的性能。这些平台可用于量化控制微生物生产力（产量，滴度和生长速率）的因素，包括代谢前体的类型和可用性;构成生物合成途径的元素;发酵条件;以及优化系统的特定遗传修饰。通过提取和分类来自参考出版物在过去20年中的信息，可以构建一个“知识库”，包含足够的样本的生物生产组件。然后，这些信息将为使用监督机器学习和非单调逻辑编程来估计主机生产力的细胞工厂的构建提供信息。数据驱动的平台还将整合到基因组规模模型中，以预测特定突变菌株的生理变化。这种新颖的方法将减少对昂贵的设计-建造-测试工作台的需求。该项目的主要成果包括：（1）标准化合成生物学研究的数据库，（2）识别隐藏在已发布数据中的经验教训和模式的机器学习模型，以及（3）将机器学习与通量平衡模型相结合，从而设计出在工业环境中成功几率很高的菌株。