Validation of a Metabolic Network for Saccharomyces cerevisiae Using Mixed Substrate Studies

Authors

  • Peter A. Vanrolleghem,

    Corresponding author
    1. Department of Bioprocess Engineering, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands
    Current affiliation:
    1. Department BIOMATH, University Gent, Coupure links 653, B-9000 Gent, Belgium
    • Department of Bioprocess Engineering, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands
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  • Patricia de Jong-Gubbels,

    1. Department of Microbiology and Enzymology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands
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  • Walter M. van Gulik,

    1. EPFL-Institut de Génie Chimique, CH-Ecublens, 1015 Lausanne, Switzerland
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  • Jack T. Pronk,

    1. Department of Microbiology and Enzymology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands
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  • Johannes P. van Dijken,

    1. Department of Microbiology and Enzymology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands
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  • Sef Heijnen

    1. Department of Bioprocess Engineering, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands
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Abstract

Setting up a metabolic network model for respiratory growth of Saccharomyces cerevisiae requires the estimation of only two (energetic) stoichiometric parameters: (1) the operational PO ratio and (2) a growth-related maintenance factor k. It is shown, both theoretically and practically, how chemostat cultivations with different mixtures of two substrates allow unique values to be given to these unknowns of the proposed metabolic model. For the yeast and model considered, an effective PO ratio of 1.09 mol of ATP/mol of O (95% confidence interval 1.07−1.11) and a k factor of 0.415 mol of ATP/C-mol of biomass (0.385−0.445) were obtained from biomass substrate yield data on glucose/ethanol mixtures. Symbolic manipulation software proved very valuable in this study as it supported the proof of theoretical identifiability and significantly reduced the necessary computations for parameter estimation. In the transition from 100% glucose to 100% ethanol in the feed, four metabolic regimes occur. Switching between these regimes is determined by cessation of an irreversible reaction and initiation of an alternative reaction. Metabolic network predictions of these metabolic switches compared well with activity measurements of key enzymes. As a second validation of the network, the biomass yield of S. cerevisiae on acetate was also compared to the network prediction. An excellent agreement was found for a network in which acetate transport was modeled with a proton symport, while passive diffusion of acetate gave significantly higher yield predictions.

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