Cell factory design with advanced metabolic modelling empowered by artificial intelligence
Journal article, 2024

Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a bio-based product succeeds or fails in the fierce competition with petroleum-based products. To date, one of the greatest challenges in synthetic biology is the creation of high-performance cell factories in a consistent and efficient manner. As so-called white-box models, numerous metabolic network models have been developed and used in computational strain design. Moreover, great progress has been made in AI-powered strain engineering in recent years. Both approaches have advantages and disadvantages. Therefore, the deep integration of AI with metabolic models is crucial for the construction of superior cell factories with higher titres, yields and production rates. The detailed applications of the latest advanced metabolic models and AI in computational strain design are summarized in this review. Additionally, approaches for the deep integration of AI and metabolic models are discussed. It is anticipated that advanced mechanistic metabolic models powered by AI will pave the way for the efficient construction of powerful industrial chassis strains in the coming years.

In silico strain design

Artificial intelligence

Mechanistic metabolic models

Hybrid models

Author

Hongzhong Lu

State Key Laboratory of Microbial Metabolism

Luchi Xiao

State Key Laboratory of Microbial Metabolism

Wenbin Liao

State Key Laboratory of Microbial Metabolism

East China University of Science and Technology

Xuefeng Yan

East China University of Science and Technology

Jens B Nielsen

BioInnovation Institute

Chalmers, Life Sciences, Systems and Synthetic Biology

Metabolic Engineering

1096-7176 (ISSN) 1096-7184 (eISSN)

Vol. 85 61-72

Subject Categories

Medical Biotechnology

DOI

10.1016/j.ymben.2024.07.003

More information

Latest update

8/9/2024 8