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

在过去的十年中，系统和合成生物学方法为在实验室环境下利用可再生资源生产多种化学品和生物燃料提供了新的机制。然而，合成改性菌株达到商业化生产要求的尚属罕见。由于现有的建模方法无法捕捉到这种工程细胞中复杂的代谢反应，菌株的开发陷入了冗长而昂贵的设计-构建-测试-学习周期。该提案将探索另一种数据驱动的方法，该方法有可能通过利用大量微生物细胞工厂出版物来预测合成生物体的生产力。利用机器学习和知识表示等人工智能方法，人们可以提取隐藏在已发表数据中的“以前的教训”，以便在给定一组特定遗传指令和发酵生长条件的情况下，对工程宿主的代谢输出进行先验估计。由此产生的平台可以帮助当前基于约束的模型设计最有效的生产增值化学品的策略。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道路。合成生物学依赖于广泛的基因改造和途径工程，这往往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、多学科的方法，依靠人工智能启发的方法来预测两种不同的单细胞工厂（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的因素（产量、滴度和生长速度），包括代谢前体的类型和可用性；构成生物合成途径的要素；发酵条件;并对该系统进行具体的基因改造优化。通过提取和分类来自近20年的参考出版物的信息，可以构建一个包含足够生物生产组件样本的“知识库”。然后，这些信息将通知使用监督机器学习和非单调逻辑编程来构建细胞工厂，以估计主机的生产率。数据驱动的平台还将集成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-构建-测试台架工作的需求。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)识别已发表数据中隐藏的经验教训和模式的机器学习模型，以及(3)机器学习与通量平衡模型的集成，从而设计出在工业环境中具有高成功机会的菌株。该奖项反映了美国国家科学基金会的法定使命，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。

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.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.