Using Kinetic Modelling to Infer Adaptations in Saccharomyces cerevisiae Carbohydrate Storage Metabolism to Dynamic Substrate Conditions
Lao-Martil D, Verhagen KJ, Valdeira Caetano AH, Pardijs IH, van Riel NA, Wahl SA (2023)
Publication Type: Journal article
Publication year: 2023
Journal
Book Volume: 13
Article Number: 88
Journal Issue: 1
Abstract
Microbial metabolism is strongly dependent on the environmental conditions. While these can be well controlled under laboratory conditions, large-scale bioreactors are characterized by inhomogeneities and consequently dynamic conditions for the organisms. How Saccharomyces cerevisiae response to frequent perturbations in industrial bioreactors is still not understood mechanistically. To study the adjustments to prolonged dynamic conditions, we used published repeated substrate perturbation regime experimental data, extended it with proteomic measurements and used both for modelling approaches. Multiple types of data were combined; including quantitative metabolome, 13C enrichment and flux quantification data. Kinetic metabolic modelling was applied to study the relevant intracellular metabolic response dynamics. An existing model of yeast central carbon metabolism was extended, and different subsets of enzymatic kinetic constants were estimated. A novel parameter estimation pipeline based on combinatorial enzyme selection supplemented by regularization was developed to identify and predict the minimum enzyme and parameter adjustments from steady-state to dynamic substrate conditions. This approach predicted proteomic changes in hexose transport and phosphorylation reactions, which were additionally confirmed by proteome measurements. Nevertheless, the modelling also hints at a yet unknown kinetic or regulation phenomenon. Some intracellular fluxes could not be reproduced by mechanistic rate laws, including hexose transport and intracellular trehalase activity during substrate perturbation cycles.
Authors with CRIS profile
Involved external institutions
Delft University of Technology (TU Delft)
Netherlands (NL)
Eindhoven University of Technology / Technische Universiteit Eindhoven (TU/e)
Netherlands (NL)
Netherlands (NL)
Eindhoven University of Technology / Technische Universiteit Eindhoven (TU/e)
Netherlands (NL)
How to cite
APA:
Lao-Martil, D., Verhagen, K.J., Valdeira Caetano, A.H., Pardijs, I.H., van Riel, N.A., & Wahl, S.A. (2023). Using Kinetic Modelling to Infer Adaptations in Saccharomyces cerevisiae Carbohydrate Storage Metabolism to Dynamic Substrate Conditions. Metabolites, 13(1). https://dx.doi.org/10.3390/metabo13010088
MLA:
Lao-Martil, David, et al. "Using Kinetic Modelling to Infer Adaptations in Saccharomyces cerevisiae Carbohydrate Storage Metabolism to Dynamic Substrate Conditions." Metabolites 13.1 (2023).
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