(253d) Application of Robust Dynamic Flux Balance Analysis Framework to a Wine Fermentation for Understanding and Steering Aroma Formation

Various commercial S. cerevisiae yeast strains are available for use in industrial wine fermentations, each with its own combination of growth characteristics and organoleptic (sensory) profiles. However, the metabolism underlying these differences is complex and not well understood, making manipulation of these strains difficult. One tool for elucidating strain-to-strain differences and identifying key metabolic pathways related to flavor production is the use of genome-scale metabolic models (GSMM). Despite progress in the field, most current models either focus on aerobic systems, contain models that focus on a carbon-limited medium, and/or lack the detailed coverage of Ehrlich and amino acid degradation metabolic pathways that have been shown experimentally to be highly correlated with aroma formation. One way to capture the power of these models is to use dynamic flux balance analysis (dFBA) 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 steering certain aroma formation in strains of interest. Here, in this work, we applied a robust dFBA framework to an expanded GSMM of S. cerevisiae over the course of a wine fermentation. Recently, we showed that ester production during alcohol fermentation is regulated by a newly discovered family of alcohol acyl transferases (AATs) in S. cerevisiae. However, mysteries still persist regarding the whether other metabolic mechanisms are responsible for ester formation. By applying our expanded GSMM of yeast, which has the most comprehensive representation aroma forming pathways, we can more accurately predict metabolic fluxes for various yeast strains. Furthermore, this model can be calibrated with experimental data to reasonably propose genetic and process engineering strategies to improve the enological performance S. cerevisiae strains of interest.