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Integrating yeast biodiversity and machine learning for predictive metabolic engineering.

Akaraphol Watcharawipas1, Weerawat Runguphan2, Peerapat Khamwachirapithak3

  • 1Department of Microbiology, Faculty of Science, Mahidol University, 272 Rama VI Road, Ratchathewi, Bangkok 10400, Thailand.

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Harnessing yeast biodiversity and machine learning (ML) enhances metabolic engineering. ML predicts genetic part function and optimizes pathways, while diverse yeasts offer robust industrial traits, creating scalable platforms.

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Area of Science:

  • Microbiology and Biotechnology
  • Synthetic Biology
  • Computational Biology

Background:

  • Saccharomyces cerevisiae is a workhorse in industrial biotechnology but has limitations in complex metabolite synthesis and feedstock utilization.
  • Non-conventional yeasts like Yarrowia lipolytica and Ogataea polymorpha offer advantageous traits (e.g., thermotolerance, lipid accumulation) but lack sufficient genetic tools and predictability.
  • Widespread adoption of alternative yeasts is hindered by challenges in genetic engineering and component performance prediction.

Purpose of the Study:

  • To review how yeast biodiversity expands engineering strategies for metabolic engineering.
  • To highlight recent machine learning (ML) advances in data-guided yeast strain and pathway design.
  • To emphasize ML-guided identification and optimization of genetic elements for improved yeast platforms.

Main Methods:

  • Review of current literature on yeast biodiversity in metabolic engineering.
  • Analysis of recent machine learning applications for predicting genetic part function and optimizing gene expression.
  • Discussion of ML-driven discovery of novel biosynthetic components and pathway configurations.
  • Exploration of how leveraging yeast evolutionary diversity enhances strain robustness.

Main Results:

  • Machine learning enables accurate prediction of genetic part function and optimization of gene expression in diverse yeasts.
  • ML tools facilitate the rational selection of genetic elements and pathway designs for non-model hosts.
  • Yeast biodiversity provides expanded chassis options and toolkits, improving strain performance under industrial conditions.
  • ML-guided approaches streamline the design-build-test-learn cycle in metabolic engineering.

Conclusions:

  • The integration of yeast biodiversity and machine learning is creating more modular, predictive, and scalable yeast platforms.
  • ML advances are crucial for overcoming limitations in engineering non-conventional yeasts.
  • Harnessing evolutionary diversity alongside intelligent computation promises to revolutionize next-generation metabolic engineering.