| Autor: |
HamediRad M; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.; LifeFoundry Inc., 60 Hazelwood Dr., Champaign, IL, 61820, USA., Chao R; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.; LifeFoundry Inc., 60 Hazelwood Dr., Champaign, IL, 61820, USA., Weisberg S; Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA., Lian J; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.; Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, 310027, Hangzhou, China., Sinha S; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. sinhas@illinois.edu.; Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. sinhas@illinois.edu., Zhao H; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. zhao5@illinois.edu.; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. zhao5@illinois.edu.; Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. zhao5@illinois.edu.; Departments of Chemistry and Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. zhao5@illinois.edu. |
| Abstrakt: |
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. |
