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. 2023:2553:21-39.
doi: 10.1007/978-1-0716-2617-7_2.

Synthetic Biology Meets Machine Learning

Brendan Fu-Long Sieow  1   2   3   4 , Ryan De Sotto  1   2   3 , Zhi Ren Darren Seet  1   2   3 , In Young Hwang  1   2   3 , Matthew Wook Chang  5   6   7
Affiliations

Synthetic Biology Meets Machine Learning

Brendan Fu-Long Sieow et al. Methods Mol Biol. 2023.

Abstract

This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.

Keywords: Machine learning; Metabolic engineering; Protein engineering; Synthetic biology.

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