(465d) Development of a Genome-Scale Metabolic Model for S. Cerevisiae to Facilitate Understanding of the Differences in Metabolism between Commercial Yeast Strains
AIChE Annual Meeting
Wednesday, October 31, 2018 - 8:54am to 9:12am
Two key metabolic activities relevant to industrial wine fermentations are nutrient utilization efficiency and tolerance to high ethanol concentrations exhibited by industrial yeast strains. Therefore, to study the details of yeast metabolism, is of great interest to develop ways to control stuck or sluggish fermentations. One approach is to use computational methods due to their advantage of being comprehensive and more economical compared to experimental methods. Hence, many studies have been conducted to create genome-scale metabolic models of yeast. Despite progress in the field, most current models either focus on aerobic systems or lack the detailed lipid metabolism that has been shown experimentally to be highly correlated with nutrient utilization efficiency. One way to capture the power of these models is to use (dynamic) FBA (flux balance analysis) to predict the flux distribution of all the metabolites within the cell over the course of an entire fermentation. Using this approach, it is possible to test the predictive capability of these models by comparing predictions with experimental fermentation data. Once the models fit dynamic data, they can be used to understand differences between commercial strains and suggest genetic modification strategies towards increasing strain ethanol tolerance and nutrient utilization efficiency. In this study, we improve the latest consensus genome scale model of yeast by incorporating additional lipid pathways. Previously, we showed that nutrient utilization efficiency and ethanol tolerance of 22 different industrial yeast strains were a strong function their lipid composition while molecular mechanisms of these phenomena were not elucidated. By utilizing the latest consensus genome-scale metabolic reconstruction of yeast, which has the most comprehensive representation of fatty acid, glycerolipid, and glycerophospholipid metabolism, we can more accurately predict metabolic fluxes for various yeast strains. In turn, understand the variation in metabolism between different strains that lead to disparities in nutrient utilization efficiency and aroma production.