(210f) Improving Regulatory Information for the Yeast Metabolic Model iMM904 Using Synthetic Lethality Data

A pair of non-essential genes is referred to as synthetic lethal if the simultaneous deletion of both genes is lethal but the single gene deletions are not. One can generalize the concept of synthetic lethality to reactions or extend it further by considering gene/reaction groups of increasing size where only the simultaneous elimination of all genes/reactions is lethal. Previous studies have demonstrated the utility of synthetic lethal predictions for the curation of genome-scale metabolic models. We recently used synthetic lethality information to identify twenty-one model improvements for the genome-scale model of Escherichia coli, iAF1260. In this talk, we discuss the systematic identification of synthetic lethal gene combinations for the most recent genome-scale metabolic model of yeast, (i.e., iMM904) for a variety of different growth medium conditions. By contrasting the in silico lethality predictions with in vivo observations we identified/corrected many missing regulatory mechanisms in yeast. The incorporation of the altered regulatory mechanisms into the genome-scale metabolic model led to a substantial increase in the accuracy of the in silico gene essentiality predictions. Overall, this study demonstrates the utility of synthetic lethality information for correcting genome-scale metabolic models.