Towards a fully automated algorithm driven platform for biosystems design

Mohammad HamediRad, Ran Chao, Scott Weisberg, Jiazhang Lian, Saurabh Sinha () and Huimin Zhao ()
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Mohammad HamediRad: University of Illinois at Urbana-Champaign
Ran Chao: University of Illinois at Urbana-Champaign
Scott Weisberg: University of Illinois at Urbana-Champaign
Jiazhang Lian: University of Illinois at Urbana-Champaign
Saurabh Sinha: University of Illinois at Urbana-Champaign
Huimin Zhao: University of Illinois at Urbana-Champaign

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-13189-z

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