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Title: 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.

Authors:
ORCiD logo; ; ORCiD logo; ORCiD logo; ; ORCiD logo
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) (SC-23)
OSTI Identifier:
1575394
Alternate Identifier(s):
OSTI ID: 1575393
Grant/Contract Number:  
SC0018420
Resource Type:
Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 2041-1723
Publisher:
Nature Publishing Group
Country of Publication:
United States
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 States: 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 States. doi: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 States. doi:10.1038/s41467-019-13189-z. https://www.osti.gov/servlets/purl/1575394.
@article{osti_1575394,
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 States},
year = {2019},
month = {11}
}

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