A Comprehensive Mechanistic Yeast Model Able to Switch Metabolism According to Growth Conditions
This paper proposes a general approach for building a mechanistic yeast model able to predict the shift of metabolic pathways. The mechanistic model accounts for the coexistence of several metabolic pathways (aerobic fermentation, glucose respiration, anaerobic fermentation and ethanol respiration)...
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MDPI AG
2022-12-01
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Series: | Fermentation |
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Online Access: | https://www.mdpi.com/2311-5637/8/12/710 |
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author | Yusmel González-Hernández Emilie Michiels Patrick Perré |
author_facet | Yusmel González-Hernández Emilie Michiels Patrick Perré |
author_sort | Yusmel González-Hernández |
collection | DOAJ |
description | This paper proposes a general approach for building a mechanistic yeast model able to predict the shift of metabolic pathways. The mechanistic model accounts for the coexistence of several metabolic pathways (aerobic fermentation, glucose respiration, anaerobic fermentation and ethanol respiration) whose activation depends on growth conditions. This general approach is applied to a commercial strain of <i>Saccharomyces cerevisiae</i>. Stoichiometry and yeast kinetics were mostly determined from aerobic and completely anaerobic experiments. Known parameters were taken from the literature, and the remaining parameters were estimated by inverse analysis using the particle swarm optimization method. The optimized set of parameters allows the concentrations to be accurately determined over time, reporting global mean relative errors for all variables of less than 7 and 11% under completely anaerobic and aerobic conditions, respectively. Different affinities of yeast for glucose and ethanol tolerance under aerobic and anaerobic conditions were obtained. Finally, the model was successfully validated by simulating a different experiment, a batch fermentation process without gas injection, with an overall mean relative error of 7%. This model represents a useful tool for the control and optimization of yeast fermentation systems. More generally, the modeling framework proposed here is intended to be used as a building block of a digital twin of any bioproduction process. |
first_indexed | 2024-03-09T16:47:13Z |
format | Article |
id | doaj.art-b43bdac03f40487e939e2c336d278133 |
institution | Directory Open Access Journal |
issn | 2311-5637 |
language | English |
last_indexed | 2024-03-09T16:47:13Z |
publishDate | 2022-12-01 |
publisher | MDPI AG |
record_format | Article |
series | Fermentation |
spelling | doaj.art-b43bdac03f40487e939e2c336d2781332023-11-24T14:45:28ZengMDPI AGFermentation2311-56372022-12-0181271010.3390/fermentation8120710A Comprehensive Mechanistic Yeast Model Able to Switch Metabolism According to Growth ConditionsYusmel González-Hernández0Emilie Michiels1Patrick Perré2CentraleSupélec, Laboratoire de Génie des Procédés et Matériaux, SFR Condorcet FR CNRS 3417, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), Université Paris-Saclay, 3 Rue des Rouges Terres, 51110 Pomacle, FranceCentraleSupélec, Laboratoire de Génie des Procédés et Matériaux, SFR Condorcet FR CNRS 3417, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), Université Paris-Saclay, 3 Rue des Rouges Terres, 51110 Pomacle, FranceCentraleSupélec, Laboratoire de Génie des Procédés et Matériaux, SFR Condorcet FR CNRS 3417, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), Université Paris-Saclay, 3 Rue des Rouges Terres, 51110 Pomacle, FranceThis paper proposes a general approach for building a mechanistic yeast model able to predict the shift of metabolic pathways. The mechanistic model accounts for the coexistence of several metabolic pathways (aerobic fermentation, glucose respiration, anaerobic fermentation and ethanol respiration) whose activation depends on growth conditions. This general approach is applied to a commercial strain of <i>Saccharomyces cerevisiae</i>. Stoichiometry and yeast kinetics were mostly determined from aerobic and completely anaerobic experiments. Known parameters were taken from the literature, and the remaining parameters were estimated by inverse analysis using the particle swarm optimization method. The optimized set of parameters allows the concentrations to be accurately determined over time, reporting global mean relative errors for all variables of less than 7 and 11% under completely anaerobic and aerobic conditions, respectively. Different affinities of yeast for glucose and ethanol tolerance under aerobic and anaerobic conditions were obtained. Finally, the model was successfully validated by simulating a different experiment, a batch fermentation process without gas injection, with an overall mean relative error of 7%. This model represents a useful tool for the control and optimization of yeast fermentation systems. More generally, the modeling framework proposed here is intended to be used as a building block of a digital twin of any bioproduction process.https://www.mdpi.com/2311-5637/8/12/710yeastfermentationCrabtree effectswitching metabolismmodelingcalibration |
spellingShingle | Yusmel González-Hernández Emilie Michiels Patrick Perré A Comprehensive Mechanistic Yeast Model Able to Switch Metabolism According to Growth Conditions Fermentation yeast fermentation Crabtree effect switching metabolism modeling calibration |
title | A Comprehensive Mechanistic Yeast Model Able to Switch Metabolism According to Growth Conditions |
title_full | A Comprehensive Mechanistic Yeast Model Able to Switch Metabolism According to Growth Conditions |
title_fullStr | A Comprehensive Mechanistic Yeast Model Able to Switch Metabolism According to Growth Conditions |
title_full_unstemmed | A Comprehensive Mechanistic Yeast Model Able to Switch Metabolism According to Growth Conditions |
title_short | A Comprehensive Mechanistic Yeast Model Able to Switch Metabolism According to Growth Conditions |
title_sort | comprehensive mechanistic yeast model able to switch metabolism according to growth conditions |
topic | yeast fermentation Crabtree effect switching metabolism modeling calibration |
url | https://www.mdpi.com/2311-5637/8/12/710 |
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