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A machine learning Automated Recommendation Tool for synthetic biology

Author

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  • Tijana Radivojević

    (DOE Agile BioFoundry
    DOE Joint BioEnergy Institute
    Lawrence Berkeley National Laboratory)

  • Zak Costello

    (DOE Agile BioFoundry
    DOE Joint BioEnergy Institute
    Lawrence Berkeley National Laboratory)

  • Kenneth Workman

    (DOE Agile BioFoundry
    Lawrence Berkeley National Laboratory
    University of California)

  • Hector Garcia Martin

    (DOE Agile BioFoundry
    DOE Joint BioEnergy Institute
    Lawrence Berkeley National Laboratory
    BCAM, Basque Center for Applied Mathematics)

Abstract

Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, fatty acids, and tryptophan. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing.

Suggested Citation

  • Tijana Radivojević & Zak Costello & Kenneth Workman & Hector Garcia Martin, 2020. "A machine learning Automated Recommendation Tool for synthetic biology," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18008-4
    DOI: 10.1038/s41467-020-18008-4
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    Cited by:

    1. Anna Gogleva & Dimitris Polychronopoulos & Matthias Pfeifer & Vladimir Poroshin & Michaël Ughetto & Matthew J. Martin & Hannah Thorpe & Aurelie Bornot & Paul D. Smith & Ben Sidders & Jonathan R. Dry &, 2022. "Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Singh, Saurabh & Morya, Raj & Jaiswal, Durgesh Kumar & Keerthana, S. & Kim, Sang-Hyoun & Manimekalai, R. & Prudêncio de Araujo Pereira, Arthur & Verma, Jay Prakash, 2024. "Innovations and advances in enzymatic deconstruction of biomass and their sustainability analysis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    3. Amir Pandi & Christoph Diehl & Ali Yazdizadeh Kharrazi & Scott A. Scholz & Elizaveta Bobkova & Léon Faure & Maren Nattermann & David Adam & Nils Chapin & Yeganeh Foroughijabbari & Charles Moritz & Nic, 2022. "A versatile active learning workflow for optimization of genetic and metabolic networks," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    4. Katharina Paulick & Simon Seidel & Christoph Lange & Annina Kemmer & Mariano Nicolas Cruz-Bournazou & André Baier & Daniel Haehn, 2022. "Promoting Sustainability through Next-Generation Biologics Drug Development," Sustainability, MDPI, vol. 14(8), pages 1-31, April.
    5. Jason Fontana & David Sparkman-Yager & Ian Faulkner & Ryan Cardiff & Cholpisit Kiattisewee & Aria Walls & Tommy G. Primo & Patrick C. Kinnunen & Hector Garcia Martin & Jesse G. Zalatan & James M. Caro, 2024. "Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    6. Wang, Zhengxin & Peng, Xinggan & Xia, Ao & Shah, Akeel A. & Yan, Huchao & Huang, Yun & Zhu, Xianqing & Zhu, Xun & Liao, Qiang, 2023. "Comparison of machine learning methods for predicting the methane production from anaerobic digestion of lignocellulosic biomass," Energy, Elsevier, vol. 263(PD).
    7. Chase R. Freschlin & Sarah A. Fahlberg & Pete Heinzelman & Philip A. Romero, 2024. "Neural network extrapolation to distant regions of the protein fitness landscape," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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