Machine learning applications in systems metabolic engineering

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Systems metabolic engineering allows efficient development of high performing microbial strains for the sustainable production of chemicals and materials. In recent years, increasing availability of bio big data, for example, omics data, has led to active application of machine learning techniques across various stages of systems metabolic engineering, including host strain selection, metabolic pathway reconstruction, metabolic flux optimization, and fermentation. In this paper, recent contributions of machine learning approaches to each major step of systems metabolic engineering are discussed. As the use of machine learning in systems metabolic engineering will become more widespread in accordance with the ever-increasing volume of bio big data, future prospects are also provided for the successful applications of machine learning.
Publisher
CURRENT BIOLOGY LTD
Issue Date
2020-08
Language
English
Article Type
Review
Citation

CURRENT OPINION IN BIOTECHNOLOGY, v.64, pp.1 - 9

ISSN
0958-1669
DOI
10.1016/j.copbio.2019.08.010
URI
http://hdl.handle.net/10203/268741
Appears in Collection
CBE-Journal Papers(저널논문)
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