Harnessing yeast biodiversity to identify superior chassis strains for metabolic engineering and precision fermentation.

Yeast biodiversity across eight species is linked to large differences in metabolic engineering performance metrics (titer, rate, yield), enabling better chassis selection for precision fermentation.

• Why it matters: Reliance on a narrow set of reference strains limits performance because identical pathway designs can behave differently across strain backgrounds and species.
• What they did: This review synthesizes evidence on how genomic features, metabolic regulation, substrate use, and stress tolerance shape chassis performance, and outlines diversity-informed selection using multi-omics, phenotyping, modeling, and machine learning.
• The result: The authors argue that treating chassis identity as a design variable and integrating diversity-informed selection into the Design-Build-Test-Learn cycle can improve optimal cell factory development.

AI summary of the abstract · may contain errors · not medical advice · read the paper.