To select a Saccharomyces cerevisiae reference strain amenable to experimental techniques used in (molecular) genetic, physiological and biochemical engineering research, a variety of properties were studied in four diploid, prototrophic laboratory strains. The following parameters were investigated: 1) maximum specific growth rate in shake-flask cultures; 2) biomass yields on glucose during growth on defined media in batch cultures and steady-state chemostat cultures under controlled conditions with respect to pH and dissolved oxygen concentration; 3) the critical specific growth rate above which aerobic fermentation becomes apparent in glucose-limited accelerostat cultures; 4) sporulation and mating efficiency; and 5) transformation efficiency via the lithium-acetate, bicine, and electroporation methods. On the basis of physiological as well as genetic properties, strains from the CEN.PK family were selected as a platform for cell-factory research on the stoichiometry and kinetics of growth and product formation.
A mathematical model of glycolysis in Saccharomyces cerevisiae is presented. The model is based on rate equations for the individual reactions and aims to predict changes in the levels of intra-and extracellular metabolites after a glucose pulse, as described in part I of this study. Kinetic analysis focuses on a time scale of seconds, thereby neglecting biosynthesis of new enzymes. The model structure and experimental observations are related to the aerobic growth of the yeast. The model is based on material balance equations of the key metabolites in the extracellular environment, the cytoplasm and the mitochondria, and includes mechanistically based, experimentally matched rate equations for the individual enzymes. The model includes removal of metabolites from glycolysis and TCC for biosynthesis, and also compartmentation and translocation of adenine nucleotides. The model was verified by in vivo diagnosis of intracellular enzymes, which includes the decomposition of the network of reactions to reduce the number of parameters to be estimated simultaneously. Additionally, sensitivity analysis guarantees that only those parameters are estimated that contribute to systems trajectory with reasonable sensitivity. The model predictions and experimental observations agree reasonably well for most of the metabolites, except for pyruvate and adenine nucleotides.
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