A machine learning Automated Recommendation Tool for synthetic biology

Radivojević, Tijana; Costello, Zak; Workman, Kenneth; Garcia Martin, Hector

doi:10.1038/s41467-020-18008-4

Title: A machine learning Automated Recommendation Tool for synthetic biology

Journal Article · Fri Sep 25 00:00:00 UTC 2020 · Nature Communications

DOI: https://doi.org/10.1038/s41467-020-18008-4 · OSTI ID:1665911

; Costello, Zak;

;

Abstract Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, fatty acids, and tryptophan. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: MINECO; USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1665911

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 11; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Title: A machine learning Automated Recommendation Tool for synthetic biology

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References (65)

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