Brewer's yeast (Saccharomyces cerevisiae) is a cornerstone of industrial biotechnology. Its exceptional adaptability to diverse natural and industrial environments has led to the emergence of a wide variety of strains, each with unique genetic and metabolic characteristics. 

However, most research on Saccharomyces cerevisiae and its applications still rely on a few laboratory strains such as S288c and CEN.PK, which limits the discovery of optimal strains for high-performance cell factory development.

In a study published in PNAS, a research team led by Prof. ZHOU Yongjin from the Dalian Institute of Chemical Physics of the Chinese Academy of Sciences, and Assoc. Prof. LU Hongzhong from Shanghai Jiao Tong University, developed a powerful systems biology method to address this problem. They uncovered how yeast adapts to different environments at a systems level through strain-specific metabolic modeling.

Researchers created a high-quality digital resource of the yeast pan-genome, and constructed metabolic models tailored to individual strains. Then, they developed a novel analysis pipeline that integrates genomic data, metabolic models, and multi-omics information, and used it to systematically evaluate different strains.

As a proof of concept, researchers applied this pipeline to industrial ethanol-producing strains. They revealed that enhancing downstream pathways of glycolysis is key to efficient ethanol synthesis at the genetic, transcriptional, and metabolic levels. This finding provides insights into the rational design of yeast cell factories for ethanol and its derivatives.

"Our work not only provides a comprehensive digital resource of yeast strains for academia and industry, but also new methods for evaluating and selecting optimal chassis strains for biomanufacturing applications," said Prof. ZHOU.