Review Article | Open Access

Volume 2022 |Article ID 9898461 | https://doi.org/10.34133/2022/9898461

Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing

Zhenkun Shi,¹ Pi Liu,^1,2 Xiaoping Liao,^1,2 Zhitao Mao,^1,2 Jianqi Zhang,^1,2 Qinhong Wang,^1,2 Jibin Sun,^1,2 Hongwu Ma iD ,^1,2 Yanhe Ma iD ^1,2

¹Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
²National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China

Abstract

Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a wide range of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, we review the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. We first briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis for data-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are also presented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factory development using examples from recent studies, including the prediction of protein function, improvement of metabolic models, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization. In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methods should be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains for industrial biomanufacturing.