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Enabling pathway design by multiplex experimentation and machine learning.

Aashutosh Girish Boob1, Junyu Chen2, Huimin Zhao3

  • 1Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.

Metabolic Engineering
|December 1, 2023
PubMed
Summary
This summary is machine-generated.

This review explores advanced pathway engineering strategies, including multiplex experimentation and machine learning, to overcome limitations in microbial production of valuable molecules. These methods accelerate the discovery of optimal biosynthetic pathways for enhanced yields and cost-effectiveness.

Keywords:
Combinatorial pathway optimizationMachine learningMetabolic engineeringPathway design

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Area of Science:

  • Metabolic engineering and synthetic biology for sustainable chemical production.

Background:

  • Natural metabolic diversity offers pathways for valuable molecule synthesis.
  • Challenges in host transfer include intermediate accumulation and competing pathways, hindering yields.
  • Traditional optimization methods are slow, labor-intensive, and often yield suboptimal results.

Approach:

  • This review focuses on multiplex experimentation and machine learning for pathway engineering.
  • These advanced techniques enable broader exploration of enzyme expression landscapes.
  • They facilitate the identification of optimal pathway configurations for enhanced molecular production.

Key Points:

  • Discusses tools and strategies for pathway design, construction, and optimization.
  • Highlights applications in sustainable and cost-effective microbial production of bulk and fine chemicals.
  • Presents case studies of academic and industrial successes.

Conclusions:

  • Multiplex experimentation and machine learning significantly improve upon traditional methods.
  • These approaches are crucial for overcoming limitations in metabolic pathway engineering.
  • They enhance the discovery of efficient microbial cell factories for sustainable chemical synthesis.