Kong Dechun, Qian Jinyi, Gao Cong, Wang Yuetong, Shi Tianqiong, Ye Chao
School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, People's Republic of China.
Ministry of Education Key Laboratory of NSLSCS, Nanjing Normal University, Nanjing, 210023, People's Republic of China.
Appl Biochem Biotechnol. 2025 May 21. doi: 10.1007/s12010-025-05260-x.
The wide application of machine learning has provided more possibilities for biological manufacturing, and the combination of machine learning and synthetic biology technology has ignited even more brilliant sparks, which has created an unpredictable value for the upgrading of microbial cell factories. The review delves into the synergies between machine learning and synthetic biology to create research worth investigating in biotechnology. We explore relevant databases, toolboxes, and machine learning-derived models. Furthermore, we examine specific applications of this combined approach in chemical production, human health, and environmental remediation. By elucidating these successful integrations, this review aims to provide valuable guidance for future research at the intersection of biomanufacturing and artificial intelligence.
机器学习的广泛应用为生物制造提供了更多可能性，机器学习与合成生物学技术的结合更是点燃了更为璀璨的火花，为微生物细胞工厂的升级创造了不可估量的价值。本综述深入探讨了机器学习与合成生物学之间的协同作用，以开创值得在生物技术领域研究的课题。我们探索了相关数据库、工具箱以及源自机器学习的模型。此外，我们考察了这种组合方法在化学生产、人类健康和环境修复方面的具体应用。通过阐明这些成功的整合案例，本综述旨在为生物制造与人工智能交叉领域的未来研究提供有价值的指导。
