期刊
METABOLIC ENGINEERING
卷 63, 期 -, 页码 34-60出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymben.2020.10.005
关键词
Machine Learning; Metabolic Engineering; Synthetic Biology; Deep Learning
资金
- U.S. Department of Energy, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office [DE-AC02-05CH11231]
- U.S. Department of Energy, Office of Science [DE-AC02-05CH11231]
- Basque Government through the BERC 2014-2017 program
- Spanish Ministry of Economy and Competitiveness MINECO: BCAM Severo Ochoa excellence accreditation [SEV-2013-0323]
The review introduces how machine learning can make metabolic engineering more predictable, provides examples and advice, discusses various applications, and examines future prospects.
Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.
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