In silico, in vitro, and in vivo machine learning in synthetic biology and metabolic engineering. (December 2021)

• Record Type: Journal Article
• Authors: Faulon, Jean-Loup; Faure, Léon
• Is Part Of: Current opinion in chemical biology. Volume 65(2021)
• Page Start: 85
• Page End: 92
• Publication Date: 2021-12
• Subjects: Synthetic biology -- Metabolic engineering -- Machine learning -- Active learning -- Reinforcement learning -- Artificial neural networks -- Perceptron; Bioorganic chemistry -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Clinical biochemistry -- Periodicals; Biochemistry -- Periodicals; Chimie bio-organique -- Périodiques; Biologie -- Périodiques; 572.05
• DOI: 10.1016/j.cbpa.2021.06.002
• Languages: English
• ISSNs: 1367-5931

Abstract

Abstract: Among the main learning methods reviewed in this study and used in synthetic biology and metabolic engineering are supervised learning, reinforcement and active learning, and in vitro or in vivo learning. In the context of biosynthesis, supervised machine learning is being exploited to predict biological sequence activities, predict structures and engineer sequences, and optimize culture conditions. Active and reinforcement learning methods use training sets acquired through an iterative process generally involving experimental measurements. They are applied to design, engineer, and optimize metabolic pathways and bioprocesses. The nascent but promising developments with in vitro and in vivo learning comprise molecular circuits performing simple tasks such as pattern recognition and classification.