Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore.
Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585, Singapore.
Curr Opin Biotechnol. 2020 Dec;66:179-185. doi: 10.1016/j.copbio.2020.08.004. Epub 2020 Sep 4.
Machine learning is transforming many industries through self-improving models that are fueled by big data and high computing power. The field of metabolic engineering, which uses cellular biochemical network to manufacture useful small molecules, has also witnessed the first wave of machine learning applications in the past five years, covering reaction route design, enzyme selection, pathway engineering and process optimization. This review focuses on pathway engineering, and uses a few recent studies to illustrate (1) how machine learning models can be useful in overcoming an evident rate-limiting step, and (2) how the models may be used to exhaustively search - or guide optimization algorithms to search - a large design space when the cellular regulation of the reaction network is more convoluted.
机器学习正在通过由大数据和高计算能力驱动的自我改进模型，改变许多行业。代谢工程领域利用细胞生化网络来制造有用的小分子，在过去五年中也见证了机器学习应用的第一波浪潮，涵盖了反应路线设计、酶选择、途径工程和过程优化。本文重点介绍途径工程，并使用最近的一些研究来说明 (1) 机器学习模型如何在克服明显的限速步骤方面发挥作用，以及 (2) 当反应网络的细胞调节更为复杂时，模型如何用于广泛搜索 - 或指导优化算法搜索 - 大型设计空间。
