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.

为了用微生物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。计算方法来设计提高化学产量的菌株通常依赖于详细的机械模型（例如，代谢的动力学/化学计量模型）——需要许多实验数据集来进行参数化——而实验方法可能需要筛选大型突变文库来探索设计空间少数具有所需行为的突变体。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) 来智能地指导实验，以使用最少的测量数据集得出最佳表型。ActiveOpt 被应用于两个独立的案例研究，以评估其提高缬氨酸产量和神经孢子素生产力的潜力大肠杆菌。在这两种情况下，ActiveOpt 在比使用的案例研究更少的实验中确定了性能最好的应变。这项工作表明，机器和主动学习方法有可能极大地促进代谢工程努力以快速实现其目标。