Integrating yeast biodiversity and machine learning for predictive metabolic engineering
2
Issued Date
2025-01-30
Resource Type
eISSN
15671364
Scopus ID
2-s2.0-105025224637
Pubmed ID
41334807
Journal Title
FEMS Yeast Research
Volume
25
Rights Holder(s)
SCOPUS
Bibliographic Citation
FEMS Yeast Research Vol.25 (2025)
Suggested Citation
Watcharawipas A., Runguphan W., Khamwachirapithak P., Laothanachareon T. Integrating yeast biodiversity and machine learning for predictive metabolic engineering. FEMS Yeast Research Vol.25 (2025). doi:10.1093/femsyr/foaf072 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/113673
Title
Integrating yeast biodiversity and machine learning for predictive metabolic engineering
Corresponding Author(s)
Other Contributor(s)
Abstract
Yeast biodiversity and machine learning (ML) are transforming the landscape of metabolic engineering. While Saccharomyces cerevisiae remains foundational to industrial biotechnology due to its genetic tractability and robust growth, it struggles to synthesize complex metabolites, utilize alternative feedstocks, and withstand industrial stresses. Non-conventional yeasts such as Yarrowia lipolytica and Ogataea polymorpha possess traits such as thermotolerance, acid resistance, and lipid accumulation, making them promising alternatives. However, broader adoption remains limited by insufficient genetic tools and low predictability of engineered components across species. Recent ML advances are addressing these gaps by enabling accurate prediction of genetic part function, optimizing gene expression, and discovering novel biosynthetic components in diverse yeasts. These tools support rational selection of genetic elements and pathway configurations tailored to non-model hosts, streamlining the design-build-test-learn cycle. Leveraging biodiversity expands the available yeast chassis and toolkits, improving strain robustness under industrial conditions. This mini-review discusses how yeast biodiversity is being harnessed to broaden engineering strategies and highlights recent ML advances driving data-guided strain and pathway design. Special attention is given to ML-guided identification and optimization of genetic elements. Together, evolutionary diversity and intelligent computation promise more modular, predictive, and scalable yeast platforms for next-generation metabolic engineering.