title: Coupling kinetic expressions and metabolic networks for predicting wine fermentations.
abstract: Problematic fermentations are commonplace and cause wine industry producers substantial economic losses through wasted tank capacity and low value final products. Being able to predict such fermentations would enable enologists to take preventive actions. In this study we modeled sugar uptake kinetics and coupled them to a previously developed stoichiometric model, which describes the anaerobic metabolism of Saccharomyces cerevisiae. The resulting model was used to predict normal and slow fermentations under winemaking conditions. The effects of fermentation temperature and initial nitrogen concentration were modeled through an efficiency factor incorporated into the sugar uptake expressions. The model required few initial parameters to successfully reproduce glucose, fructose, and ethanol profiles of laboratory and industrial fermentations. Glycerol and biomass profiles were successfully predicted in nitrogen rich cultures. The time normal or slow wine fermentations needed to complete the process was predicted accurately, at different temperatures. Simulations with a model representing a genetically modified yeast fermentation, reproduced qualitatively well literature results regarding the formation of minor compounds involved in wine complexity and aroma. Therefore, the model also proves useful to explore the effects of genetic modifications on fermentation profiles.
authors.affiliation: Departamento de Ingeniería Química y Bioprocesos, Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.
authors.forename: Francisco
authors.initials: F
authors.lastname: Pizarro
authors.forename: Cristian
authors.initials: C
authors.lastname: Varela
authors.forename: Cecilia
authors.initials: C
authors.lastname: Martabit
authors.forename: Claudio
authors.initials: C
authors.lastname: Bruno
authors.forename: J Ricardo
authors.initials: JR
authors.lastname: Pérez-Correa
authors.forename: Eduardo
authors.initials: E
authors.lastname: Agosin
mesh_terms: D000465:Algorithms; D018533:Biomass; D050260:Carbohydrate Metabolism; D000431:Ethanol / Q000378:metabolism; D005285:Fermentation; D005632:Fructose / Q000378:metabolism; D005947:Glucose / Q000378:metabolism; D005990:Glycerol / Q000378:metabolism; D005993:Glycerolphosphate Dehydrogenase / Q000235:genetics / Q000378:metabolism; D006601:Hexoses / Q000378:metabolism; D007700:Kinetics; D008954:Models, Biological; D030781:Organisms, Genetically Modified; D010427:Pentose Phosphate Pathway; D011766:Pyruvate Carboxylase / Q000235:genetics / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000254:growth & development / Q000378:metabolism; D019802:Succinic Acid / Q000378:metabolism; D013696:Temperature; D014920:Wine / Q000382:microbiology*; D015003:Yeasts / Q000235:genetics / Q000254:growth & development / Q000378:metabolism*
chemical_list: D006601:Hexoses; D005632:Fructose; D000431:Ethanol; D019802:Succinic Acid; D005993:Glycerolphosphate Dehydrogenase; D011766:Pyruvate Carboxylase; D005947:Glucose; D005990:Glycerol