Description
Engineering microbes with novel metabolic properties is a critical step for production of biofuels and biochemicals. Synthetic biology enables identification and engineering of metabolic pathways into microbes; however, knowledge of how to reroute cellular regulatory signals and metabolic flux remains lacking. Here we used network analysis of multi-omic data to dissect the mechanism of anaerobic xylose fermentation, a trait important for biochemical production from plant lignocellulose. We compared transcriptomic, proteomic, and phosphoproteomic differences across a series of strains evolved to ferment xylose under various conditions. Overall design: RNA-seq and transcriptome analysis of three evolved S. cerevisiae strains (Y22-3, Y127, Y128) grown aerobically or anaerobically in rich lab media with glucose, xylose, galactose, or sorbitol. Duplicates were collected on different days.