Biosystems Design by Machine Learning
Abstract
Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. Yet, the intricate connectivity and complexity of biosystems pose a major hurdle in designing biosystems with desired 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 non-obvious 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 bioprocess. Lastly, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.
- Authors:
-
- Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States); Univ. of Illinois at Urbana-Champaign, IL (United States). Carl R. Woese Inst. for Genomic Biology and Dept. of Chemical and Biomolecular Engineering
- Univ. of Illinois at Urbana-Champaign, IL (United States). Dept. of Computer Science; Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States)
- Univ. of Illinois at Urbana-Champaign, IL (United States). Dept. of Chemistry and Carl R. Woese Inst. for Genomic Biology; Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States)
- Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States); Univ. of Illinois at Urbana-Champaign, IL (United States). Carl R. Woese Inst. for Genomic Biology, Dept. of Chemical and Biomolecular Engineering, and Dept. of Chemistry
- Publication Date:
- Research Org.:
- Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States); Univ. of Illinois at Urbana-Champaign, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); National Institutes of Health (NIH)
- OSTI Identifier:
- 1632114
- Grant/Contract Number:
- SC0018420; SC0018260; 1UM1HG009402; 1U54DK107965; AI144967
- Resource Type:
- Accepted Manuscript
- Journal Name:
- ACS Synthetic Biology
- Additional Journal Information:
- Journal Volume: 9; Journal Issue: 7; Journal ID: ISSN 2161-5063
- Publisher:
- American Chemical Society (ACS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; Machine learning; Biosystems design; Synthetic biology; Metabolic engineering
Citation Formats
Volk, Michael Jeffrey, Lourentzou, Ismini, Mishra, Shekhar, Vo, Lam Tung, Zhai, Chengxiang, and Zhao, Huimin. Biosystems Design by Machine Learning. United States: N. p., 2020.
Web. doi:10.1021/acssynbio.0c00129.
Volk, Michael Jeffrey, Lourentzou, Ismini, Mishra, Shekhar, Vo, Lam Tung, Zhai, Chengxiang, & Zhao, Huimin. Biosystems Design by Machine Learning. United States. https://doi.org/10.1021/acssynbio.0c00129
Volk, Michael Jeffrey, Lourentzou, Ismini, Mishra, Shekhar, Vo, Lam Tung, Zhai, Chengxiang, and Zhao, Huimin. Tue .
"Biosystems Design by Machine Learning". United States. https://doi.org/10.1021/acssynbio.0c00129. https://www.osti.gov/servlets/purl/1632114.
@article{osti_1632114,
title = {Biosystems Design by Machine Learning},
author = {Volk, Michael Jeffrey and Lourentzou, Ismini and Mishra, Shekhar and Vo, Lam Tung and Zhai, Chengxiang and Zhao, Huimin},
abstractNote = {Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. Yet, the intricate connectivity and complexity of biosystems pose a major hurdle in designing biosystems with desired 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 non-obvious 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 bioprocess. Lastly, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.},
doi = {10.1021/acssynbio.0c00129},
journal = {ACS Synthetic Biology},
number = 7,
volume = 9,
place = {United States},
year = {Tue Jun 02 00:00:00 EDT 2020},
month = {Tue Jun 02 00:00:00 EDT 2020}
}
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