Towards a fully automated algorithm driven platform for biosystems design
Abstract
Large-scale data acquisition and analysis are often required in the successful implementation of the design, build, test, and learn (DBTL) cycle in biosystems design. However, it has long been hindered by experimental cost, variability, biases, and missed insights from traditional analysis methods. Here, we report the application of an integrated robotic system coupled with machine learning algorithms to fully automate the DBTL process for biosystems design. As proof of concept, we have demonstrated its capacity by optimizing the lycopene biosynthetic pathway. This fully-automated robotic platform, BioAutomata, evaluates less than 1% of possible variants while outperforming random screening by 77%. A paired predictive model and Bayesian algorithm select experiments which are performed by Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB). BioAutomata excels with black-box optimization problems, where experiments are expensive and noisy and the success of the experiment is not dependent on extensive prior knowledge of biological mechanisms.
- Publication Date:
- Research Org.:
- Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1619591
- Alternate Identifier(s):
- OSTI ID: 1575393; OSTI ID: 1575394
- Grant/Contract Number:
- SC0018420
- Resource Type:
- Published Article
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Name: Nature Communications Journal Volume: 10 Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING
Citation Formats
HamediRad, Mohammad, Chao, Ran, Weisberg, Scott, Lian, Jiazhang, Sinha, Saurabh, and Zhao, Huimin. Towards a fully automated algorithm driven platform for biosystems design. United Kingdom: N. p., 2019.
Web. doi:10.1038/s41467-019-13189-z.
HamediRad, Mohammad, Chao, Ran, Weisberg, Scott, Lian, Jiazhang, Sinha, Saurabh, & Zhao, Huimin. Towards a fully automated algorithm driven platform for biosystems design. United Kingdom. https://doi.org/10.1038/s41467-019-13189-z
HamediRad, Mohammad, Chao, Ran, Weisberg, Scott, Lian, Jiazhang, Sinha, Saurabh, and Zhao, Huimin. Wed .
"Towards a fully automated algorithm driven platform for biosystems design". United Kingdom. https://doi.org/10.1038/s41467-019-13189-z.
@article{osti_1619591,
title = {Towards a fully automated algorithm driven platform for biosystems design},
author = {HamediRad, Mohammad and Chao, Ran and Weisberg, Scott and Lian, Jiazhang and Sinha, Saurabh and Zhao, Huimin},
abstractNote = {Large-scale data acquisition and analysis are often required in the successful implementation of the design, build, test, and learn (DBTL) cycle in biosystems design. However, it has long been hindered by experimental cost, variability, biases, and missed insights from traditional analysis methods. Here, we report the application of an integrated robotic system coupled with machine learning algorithms to fully automate the DBTL process for biosystems design. As proof of concept, we have demonstrated its capacity by optimizing the lycopene biosynthetic pathway. This fully-automated robotic platform, BioAutomata, evaluates less than 1% of possible variants while outperforming random screening by 77%. A paired predictive model and Bayesian algorithm select experiments which are performed by Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB). BioAutomata excels with black-box optimization problems, where experiments are expensive and noisy and the success of the experiment is not dependent on extensive prior knowledge of biological mechanisms.},
doi = {10.1038/s41467-019-13189-z},
journal = {Nature Communications},
number = 1,
volume = 10,
place = {United Kingdom},
year = {2019},
month = {11}
}
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