Abstract
The production of fuel ethanol by Saccharomyces cerevisiae is the consequence of the coupling between proteome limitations, metabolism regulation and the final stress of high-gravity fermentation. Recent developments in enzyme constrained genome-scale models and dynamic flux simulations have increased the predictability of the overflow metabolism, pathway usage under stress, and pathways that are critical for growth and ethanol production. Concurrently, CRISPR based genome engineering has permitted the precise modification of the pathways for glycolysis, fermentation, stress tolerance, and pentose sugar metabolism through multiplex editing, promoter tuning, and genome-scale perturbations. Using computational design integrated with CRISPR engineering, machine-learning guided promoter selection, and adaptive laboratory evolution, yeast strains with more balanced metabolic flux and enhanced robustness and ethanol productivity under these challenging growth conditions were obtained. With further advancements in whole-cell model technology, more sophisticated CRISPR tools, synthetic regulatory circuits and AI-based automated engineering platforms, the engineering of robust yeast chassis with improved stability, stress tolerance and productivity for sustainable and large-scale bioethanol generation will be promoted.