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Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Author

Listed:
  • Jie Zhang

    (Technical University of Denmark, Kgs.)

  • Søren D. Petersen

    (Technical University of Denmark, Kgs.)

  • Tijana Radivojevic

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

  • Andrés Ramirez

    (TeselaGen SpA)

  • Andrés Pérez-Manríquez

    (TeselaGen SpA)

  • Eduardo Abeliuk

    (TeselaGen Biotechnology)

  • Benjamín J. Sánchez

    (Technical University of Denmark, Kgs.)

  • Zak Costello

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

  • Yu Chen

    (Chalmers University of Technology
    Chalmers University of Technology)

  • Michael J. Fero

    (TeselaGen Biotechnology)

  • Hector Garcia Martin

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

  • Jens Nielsen

    (Technical University of Denmark, Kgs.
    Chalmers University of Technology
    BioInnovation Institute)

  • Jay D. Keasling

    (Technical University of Denmark, Kgs.
    Joint BioEnergy Institute
    Lawrence Berkeley National Laboratory
    University of California)

  • Michael K. Jensen

    (Technical University of Denmark, Kgs.)

Abstract

Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

Suggested Citation

  • Jie Zhang & Søren D. Petersen & Tijana Radivojevic & Andrés Ramirez & Andrés Pérez-Manríquez & Eduardo Abeliuk & Benjamín J. Sánchez & Zak Costello & Yu Chen & Michael J. Fero & Hector Garcia Martin &, 2020. "Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17910-1
    DOI: 10.1038/s41467-020-17910-1
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    Cited by:

    1. Simon d’Oelsnitz & Daniel J. Diaz & Wantae Kim & Daniel J. Acosta & Tyler L. Dangerfield & Mason W. Schechter & Matthew B. Minus & James R. Howard & Hannah Do & James M. Loy & Hal S. Alper & Y. Jessie, 2024. "Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Charlotte Cautereels & Jolien Smets & Peter Bircham & Dries De Ruysscher & Anna Zimmermann & Peter De Rijk & Jan Steensels & Anton Gorkovskiy & Joleen Masschelein & Kevin J. Verstrepen, 2024. "Combinatorial optimization of gene expression through recombinase-mediated promoter and terminator shuffling in yeast," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    3. Baiyang Liu & Christian Cuba Samaniego & Matthew R. Bennett & Elisa Franco & James Chappell, 2023. "A portable regulatory RNA array design enables tunable and complex regulation across diverse bacteria," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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