Ardic Arikal, David E. Block,* William Scott, Ayca Ozcan, Benjamín J. Sánchez, and Jens Nielsen
*University of California Davis, UC Davis One Shields Avenue , Davis, CA 95616 (deblock@ucdavis.edu)

Two key metabolic activities relevant to industrial wine fermentations are nutrient use efficiency and tolerance to high ethanol concentrations exhibited by industrial yeast strains. 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 economic than 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 use 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 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 among commercial strains and suggest genetic modification strategies to increase strain ethanol tolerance and nutrient use efficiency. In this study, we improve the latest consensus genome scale model of yeast by incorporating additional lipid pathways. Previously, we showed that nutrient use 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 using the Yeast 7.6 model, which has the most comprehensive representation of fatty acid, glycerolipid, and glycerophospholipid metabolism, we can more accurately predict metabolic fluxes for various yeast strains and understand the variation in metabolism among different strains that leads to disparities in nutrient use efficiency and aroma production.

Funding Support: Ernest Gallo Endowed Chair in Viticulture and Enology and UC Davis TOPS Fellowship Program