Towards a fully automated algorithm driven platform for biosystems design

HamediRad, Mohammad; Chao, Ran; Weisberg, Scott; Lian, Jiazhang; Sinha, Saurabh; Zhao, Huimin

doi:10.1038/s41467-019-13189-z

Title: Towards a fully automated algorithm driven platform for biosystems design

Journal Article · 13 November 2019 · Nature Communications

DOI: https://doi.org/10.1038/s41467-019-13189-z · OSTI ID:1619591

^[1]; Chao, Ran ^[1];

^[2];

^[3]; Sinha, Saurabh ^[4];

^[5]

Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States). Dept. of Chemical and Biomolecular Engineering and Carl R. Woese Inst. for Genomic Biology; LifeFoundry Inc., Champaign, IL (United States)
Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States). Dept. of Biochemistry
Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States). Dept. of Chemical and Biomolecular Engineering; Zhejiang Univ., Hangzhou (China). Key Lab. of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering
Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States). Carl R. Woese Inst. for Genomic Biology and Dept. of Computer Science
Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States). Dept. of Chemical and Biomolecular Engineering, Carl R. Woese Inst. for Genomic Biology and Dept. of Chemistry and Bioengineering

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.

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Research Organization:: Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States)

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

Grant/Contract Number:: SC0018420

OSTI ID:: 1619591

Alternate ID(s):: OSTI ID: 1575393; OSTI ID: 1575394

Journal Information:: Nature Communications, Vol. 10, Issue 1; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 73 works

Citation information provided by
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Modular control of multiple pathways using engineered orthogonal T7 polymerases Temme, Karsten; Hill, Rena; Segall-Shapiro, Thomas H. Nucleic Acids Research, Vol. 40, Issue 17 https://doi.org/10.1093/nar/gks597	journal	June 2012
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A method for efficient Bayesian optimization of self-assembly systems from scattering data Thomas, Marcus; Schwartz, Russell BMC Systems Biology, Vol. 12, Issue 1 https://doi.org/10.1186/s12918-018-0592-8	journal	June 2018
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SBOL Visual: A Graphical Language for Genetic Designs Quinn, Jacqueline Y.; Cox, Robert Sidney; Adler, Aaron PLOS Biology, Vol. 13, Issue 12 https://doi.org/10.1371/journal.pbio.1002310	journal	December 2015

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