Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon 34141, Republic of Korea.
Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), KAIST, Daejeon 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea.
Curr Opin Biotechnol. 2020 Aug;64:1-9. doi: 10.1016/j.copbio.2019.08.010. Epub 2019 Sep 30.
Systems metabolic engineering allows efficient development of high performing microbial strains for the sustainable production of chemicals and materials. In recent years, increasing availability of bio big data, for example, omics data, has led to active application of machine learning techniques across various stages of systems metabolic engineering, including host strain selection, metabolic pathway reconstruction, metabolic flux optimization, and fermentation. In this paper, recent contributions of machine learning approaches to each major step of systems metabolic engineering are discussed. As the use of machine learning in systems metabolic engineering will become more widespread in accordance with the ever-increasing volume of bio big data, future prospects are also provided for the successful applications of machine learning.
系统代谢工程允许高效开发高性能微生物菌株，以可持续地生产化学品和材料。近年来，生物大数据（例如组学数据）的可用性不断增加，促使机器学习技术在系统代谢工程的各个阶段得到积极应用，包括宿主菌株选择、代谢途径重建、代谢通量优化和发酵。本文讨论了机器学习方法在系统代谢工程每个主要步骤中的最新贡献。随着生物大数据量的不断增加，机器学习在系统代谢工程中的应用将变得更加广泛，本文还为机器学习的成功应用提供了未来展望。
