Model‐guided development of an evolutionarily stable yeast chassis

First‐principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model‐based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome‐scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product‐specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi‐omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re‐routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer‐aided design of microbial cell factories. SYNOPSIS Evolutionarily stable microbial chassis for producing multiple compounds are designed using mathematical modelling. Multi‐omics analyses show concordance between the operation of the post‐evolution designed cells and the model predictions. The study presents a proof‐of‐concept for pre‐optimized platform strains for producing a family of compounds. Multi‐omics data integration shows that metabolic changes following adaptive evolution are in line with in silico predictions. Together, model‐based strain design, adaptive laboratory evolution and multi‐omics data improve the accuracy of yeast genome‐scale metabolic models. Graphical Abstract Evolutionarily stable microbial chassis for producing multiple compounds are designed using mathematical modelling. Multi‐omics analyses show concordance between the operation of the post‐evolution designed cells and the model predictions.