Saccharomyces cerevisiae and S. kudriavzevii Synthetic Wine Fermentation Performance Dissected by Predictive Modeling

Wineries face unprecedented challenges due to new market demands and climate change effects on wine quality. New yeast starters including non-conventional Saccharomyces species, such as S. kudriavzevii, may contribute to deal with some of these challenges. The design of new fermentations using non-c...

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Main Authors: David Henriques, Javier Alonso-del-Real, Amparo Querol, Eva Balsa-Canto
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-02-01
Series:Frontiers in Microbiology
Subjects:
Online Access:http://journal.frontiersin.org/article/10.3389/fmicb.2018.00088/full
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spelling doaj-26a9db60a0d646dbad682640da50799d2020-11-24T22:01:09ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2018-02-01910.3389/fmicb.2018.00088317309Saccharomyces cerevisiae and S. kudriavzevii Synthetic Wine Fermentation Performance Dissected by Predictive ModelingDavid Henriques0Javier Alonso-del-Real1Amparo Querol2Eva Balsa-Canto3(Bio)process Engineering Group, IIM-CSIC, Vigo, SpainGrupo de Biología de Sistemas en Levaduras de Interés Biotecnológico, IATA-CSIC, Valencia, SpainGrupo de Biología de Sistemas en Levaduras de Interés Biotecnológico, IATA-CSIC, Valencia, Spain(Bio)process Engineering Group, IIM-CSIC, Vigo, SpainWineries face unprecedented challenges due to new market demands and climate change effects on wine quality. New yeast starters including non-conventional Saccharomyces species, such as S. kudriavzevii, may contribute to deal with some of these challenges. The design of new fermentations using non-conventional yeasts requires an improved understanding of the physiology and metabolism of these cells. Dynamic modeling brings the potential of exploring the most relevant mechanisms and designing optimal processes more systematically. In this work we explore mechanisms by means of a model selection, reduction and cross-validation pipeline which enables to dissect the most relevant fermentation features for the species under consideration, Saccharomyces cerevisiae T73 and Saccharomyces kudriavzevii CR85. The pipeline involved the comparison of a collection of models which incorporate several alternative mechanisms with emphasis on the inhibitory effects due to temperature and ethanol. We focused on defining a minimal model with the minimum number of parameters, to maximize the identifiability and the quality of cross-validation. The selected model was then used to highlight differences in behavior between species. The analysis of model parameters would indicate that the specific growth rate and the transport of hexoses at initial times are higher for S. cervisiae T73 while S. kudriavzevii CR85 diverts more flux for glycerol production and cellular maintenance. As a result, the fermentations with S. kudriavzevii CR85 are typically slower; produce less ethanol but higher glycerol. Finally, we also explored optimal initial inoculation and process temperature to find the best compromise between final product characteristics and fermentation duration. Results reveal that the production of glycerol is distinctive in S. kudriavzevii CR85, it was not possible to achieve the same production of glycerol with S. cervisiae T73 in any of the conditions tested. This result brings the idea that the optimal design of mixed cultures may have an enormous potential for the improvement of final wine quality.http://journal.frontiersin.org/article/10.3389/fmicb.2018.00088/fullSaccharomyces speciestemperaturewine fermentationdynamic modelingparameter estimationcross-validation
collection DOAJ
language English
format Article
sources DOAJ
author David Henriques
Javier Alonso-del-Real
Amparo Querol
Eva Balsa-Canto
spellingShingle David Henriques
Javier Alonso-del-Real
Amparo Querol
Eva Balsa-Canto
Saccharomyces cerevisiae and S. kudriavzevii Synthetic Wine Fermentation Performance Dissected by Predictive Modeling
Frontiers in Microbiology
Saccharomyces species
temperature
wine fermentation
dynamic modeling
parameter estimation
cross-validation
author_facet David Henriques
Javier Alonso-del-Real
Amparo Querol
Eva Balsa-Canto
author_sort David Henriques
title Saccharomyces cerevisiae and S. kudriavzevii Synthetic Wine Fermentation Performance Dissected by Predictive Modeling
title_short Saccharomyces cerevisiae and S. kudriavzevii Synthetic Wine Fermentation Performance Dissected by Predictive Modeling
title_full Saccharomyces cerevisiae and S. kudriavzevii Synthetic Wine Fermentation Performance Dissected by Predictive Modeling
title_fullStr Saccharomyces cerevisiae and S. kudriavzevii Synthetic Wine Fermentation Performance Dissected by Predictive Modeling
title_full_unstemmed Saccharomyces cerevisiae and S. kudriavzevii Synthetic Wine Fermentation Performance Dissected by Predictive Modeling
title_sort saccharomyces cerevisiae and s. kudriavzevii synthetic wine fermentation performance dissected by predictive modeling
publisher Frontiers Media S.A.
series Frontiers in Microbiology
issn 1664-302X
publishDate 2018-02-01
description Wineries face unprecedented challenges due to new market demands and climate change effects on wine quality. New yeast starters including non-conventional Saccharomyces species, such as S. kudriavzevii, may contribute to deal with some of these challenges. The design of new fermentations using non-conventional yeasts requires an improved understanding of the physiology and metabolism of these cells. Dynamic modeling brings the potential of exploring the most relevant mechanisms and designing optimal processes more systematically. In this work we explore mechanisms by means of a model selection, reduction and cross-validation pipeline which enables to dissect the most relevant fermentation features for the species under consideration, Saccharomyces cerevisiae T73 and Saccharomyces kudriavzevii CR85. The pipeline involved the comparison of a collection of models which incorporate several alternative mechanisms with emphasis on the inhibitory effects due to temperature and ethanol. We focused on defining a minimal model with the minimum number of parameters, to maximize the identifiability and the quality of cross-validation. The selected model was then used to highlight differences in behavior between species. The analysis of model parameters would indicate that the specific growth rate and the transport of hexoses at initial times are higher for S. cervisiae T73 while S. kudriavzevii CR85 diverts more flux for glycerol production and cellular maintenance. As a result, the fermentations with S. kudriavzevii CR85 are typically slower; produce less ethanol but higher glycerol. Finally, we also explored optimal initial inoculation and process temperature to find the best compromise between final product characteristics and fermentation duration. Results reveal that the production of glycerol is distinctive in S. kudriavzevii CR85, it was not possible to achieve the same production of glycerol with S. cervisiae T73 in any of the conditions tested. This result brings the idea that the optimal design of mixed cultures may have an enormous potential for the improvement of final wine quality.
topic Saccharomyces species
temperature
wine fermentation
dynamic modeling
parameter estimation
cross-validation
url http://journal.frontiersin.org/article/10.3389/fmicb.2018.00088/full
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