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Importance of Measuring the Biomass Composition of Saccharomyces Cerevisiae to Model Wine Fermentations Using a Genome-Scale Model

Authors: 
Arikal, A. O., University of California, Davis
Scott, W. T. Jr., University of California, Davis
Block, D. E., University of California, Davis
Two key metabolic activities relevant to industrial wine fermentations are nutrient utilization efficiency and tolerance to high ethanol concentrations exhibited by commercial wine yeast strains. Therefore, studying the details of yeast metabolism is of great interest to develop ways to control stuck or sluggish fermentations. Using a computational method called dynamic flux balance analysis (dFBA) combined with a genome-scale metabolic model of yeast we managed to simulate growth kinetics of S. cerevisiae under enological conditions. Using a metabolic model also allowed us to predict flux (reaction rate) values of each metabolic reaction in the cell, which helped to visualize metabolic pathways that are important in nutrient utilization efficiency of Saccharomyces cerevisiae. However, despite being a robust way to study yeast metabolism, current genome-scale models do not accurately represent the biomass growth equation which results in lower predicted maximum cell concentrations compared to experimental values, indicating that it is essential to experimentally measure major cellular components. This way, variation in biomass composition between different yeast strains can be assessed and the resulting improved models used to gain better insight into metabolic differences between strains. In this study we carry out a sensitivity analysis that demonstrate how much the model is affected by changes in major cell components including DNA, RNA, total lipid, protein and carbohydrate in biomass equation of Yeast 8.3.3 (developed by the Nielsen research group at Chalmers University). We also demonstrate that using measured values of the biomass components improves our computational predictions. We can also more accurately predict metabolic fluxes for various yeast strains and understand the variation in metabolism between different strains that leads to disparities in nutrient utilization efficiency.

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