Biosystems Design by Machine Learning

Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughpu...

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Bibliographic Details
Published in:ACS synthetic biology 2020-07, Vol.9 (7), p.1514-1533
Main Authors: Volk, Michael Jeffrey, Lourentzou, Ismini, Mishra, Shekhar, Vo, Lam Tung, Zhai, Chengxiang, Zhao, Huimin
Format: Article
Language:English
Subjects:
BASIC BIOLOGICAL SCIENCES
Biosystems design
Machine learning
Metabolic engineering
Synthetic biology
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Summary:Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.
ISSN:2161-5063
2161-5063
DOI:10.1021/acssynbio.0c00129