Machine learning for metabolic engineering: A review.

Journal: Metabolic engineering
Published Date: Nov 19, 2020

Abstract

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

Authors

  • Christopher E Lawson
    Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA.
  • Jose Manuel Martí
    Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA.
  • Tijana Radivojević
    DOE Agile BioFoundry, Emeryville, CA, 94608, USA.
  • Sai Vamshi R Jonnalagadda
    Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA.
  • Reinhard Gentz
    Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
  • Nathan J Hillson
    Joint BioEnergy Institute (JBEI) , Emeryville , California 94608 , United States.
  • Sean Peisert
    Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; University of California Davis, Davis, CA, 95616, USA.
  • Joonhoon Kim
    Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Pacific Northwest National Laboratory, Richland, 99354, WA, USA.
  • Blake A Simmons
    Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA.
  • Christopher J Petzold
    Joint BioEnergy Institute (JBEI) , Emeryville , California 94608 , United States.
  • Steven W Singer
    Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA.
  • Aindrila Mukhopadhyay
    Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, USA.
  • Deepti Tanjore
    Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Advanced Biofuels and Bioproducts Process Development Unit, Emeryville, CA, 94608, USA.
  • Joshua G Dunn
    Ginkgo Bioworks, Boston, MA, 02210, USA.
  • Hector Garcia Martin
    Joint BioEnergy Institute (JBEI) , Emeryville , California 94608 , United States.

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