Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI, 53706, USA; ZS Associates, 1560 Sherman Ave, Evanston, IL, 60201, USA.
Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI, 53706, USA.
Metab Eng. 2021 Sep;67:216-226. doi: 10.1016/j.ymben.2021.06.009. Epub 2021 Jul 3.
In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic models (e.g., kinetic/stoichiometric models of metabolism)-requiring many experimental datasets for their parameterization-while experimental methods may require screening large mutant libraries to explore the design space for the few mutants with desired behaviors. To address these limitations, we developed an active and machine learning approach (ActiveOpt) to intelligently guide experiments to arrive at an optimal phenotype with minimal measured datasets. ActiveOpt was applied to two separate case studies to evaluate its potential to increase valine yields and neurosporene productivity in Escherichia coli. In both the cases, ActiveOpt identified the best performing strain in fewer experiments than the case studies used. This work demonstrates that machine and active learning approaches have the potential to greatly facilitate metabolic engineering efforts to rapidly achieve its objectives.
为了从微生物中制造可再生燃料和化学品，需要新的方法来更智能地设计微生物。用于工程菌株以增强化学品生产的计算方法通常依赖于详细的机理模型（例如，代谢的动力学/化学计量模型）-需要许多实验数据集进行参数化-而实验方法可能需要筛选大量突变体文库，以探索具有所需行为的少数突变体的设计空间。为了解决这些限制，我们开发了一种主动和机器学习方法（ActiveOpt），以智能地指导实验，用最少的测量数据集获得最佳表型。在两个独立的案例研究中应用了 ActiveOpt，以评估其在提高大肠杆菌中缬氨酸产量和神经鞘氨醇生产力方面的潜力。在这两种情况下，ActiveOpt 都比案例研究中使用的实验确定了表现最佳的菌株。这项工作表明，机器学习和主动学习方法具有极大地促进代谢工程努力的潜力，以快速实现其目标。
