Elucidating yeast glycolytic dynamics at steady state growth and glucose pulses through kinetic metabolic modeling

David Lao-Martil, Joep P.J. Schmitz, Bas Teusink, Natal A.W. van Riel

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

Microbial cell factories face changing environments during industrial fermentations. Kinetic metabolic models enable the simulation of the dynamic metabolic response to these perturbations, but their development is challenging due to model complexity and experimental data requirements. An example of this is the well-established microbial cell factory Saccharomyces cerevisiae, for which no consensus kinetic model of central metabolism has been developed and implemented in industry. Here, we aim to bring the academic and industrial communities closer to this consensus model. We developed a physiology informed kinetic model of yeast glycolysis connected to central carbon metabolism by including the effect of anabolic reactions precursors, mitochondria and the trehalose cycle. To parametrize such a large model, a parameter estimation pipeline was developed, consisting of a divide and conquer approach, supplemented with regularization and global optimization. Additionally, we show how this first mechanistic description of a growing yeast cell captures experimental dynamics at different growth rates and under a strong glucose perturbation, is robust to parametric uncertainty and explains the contribution of the different pathways in the network. Such a comprehensive model could not have been developed without using steady state and glucose perturbation data sets. The resulting metabolic reconstruction and parameter estimation pipeline can be applied in the future to study other industrially-relevant scenarios. We show this by generating a hybrid CFD-metabolic model to explore intracellular glycolytic dynamics for the first time. The model suggests that all intracellular metabolites oscillate within a physiological range, except carbon storage metabolism, which is sensitive to the extracellular environment.

Original languageEnglish
Pages (from-to)128-142
Number of pages15
JournalMetabolic engineering
Volume77
Early online date23 Mar 2023
DOIs
Publication statusPublished - 1 May 2023

Keywords

  • Glycolysis
  • Growing cell
  • Kinetic modeling
  • Parameter estimation
  • Sacchamoyces cerevisiae

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