Simple generic model for dynamic experiments withSaccharomyces cerevisiae in continuous culture: Decoupling between anabolism and catabolism

Duboc, Philippe; Stockar, Urs von

doi:10.1002/(sici)1097-0290(19981020)60:2<180::aid-bit5>3.0.co;2-j

Cited by 35 publications

(23 citation statements)

References 22 publications

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“…In the shift-up experiment with S. cerevisiae ENY.WA1C, sudden fermentation was observed and the oxidative catabolism adapted in the following 3 h, as was observed previously [8]. Thus, the flux capacity of oxidative dissimilation of pyruvate was transiently saturated [1] and glycolytic flux was shunted into reductive metabolism.…”

Section: Transient Adaptation Of Respiratory Capacitysupporting

confidence: 67%

“…This assumption is valid since a change in biomass composition, which may certainly occur during transient conditions [22], has been shown to have no significant effect on the stoichiometric model when only carbon and degree of reduction balances were used [21]. The anabolic production of CO 2 can be calculated from the anaerobic yield of glycerol production [8]. Glycerol production is neglected during aerobic cultivation (measured concentrations were always less than 0.1 g/l), so that oxidative phosphorylation is the only means for re-oxidation of NADH.…”

Section: Formulation Of the Metabolic Modelmentioning

confidence: 99%

“…Reductive metabolism sets in after transition of sugarlimited conditions to sugar excess due to a transient saturation of the flux capacity of the oxidative catabolism, causing overflow of the glycolytic flux at the level of pyruvate [1]. The transient adaptation of oxidative catabolism to the new extracellular conditions has relaxation times of minutes up to a few hours [8] and is usually provoked by pulse additions to chemostat cultivations [9,10,11]. Shift-up experiments inside of the oxidative region were also performed, by which the saturation and adaptation of the oxidative catabolism were quantified by a transient uncoupling of oxidative and reductive metabolism [12].…”

Section: Introductionmentioning

confidence: 99%

“…In order to circumvent this difficulty, the adaptation kinetics of some intrinsic reactions have been modeled [8]. More recently, a rigid method for the online calculation of reconciled conversion rates under dynamic process conditions has been reported [21].…”

Section: Introductionmentioning

confidence: 99%

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A small metabolic flux model to identify transient metabolic regulations in Saccharomyces cerevisiae

Herwig

Stockar

2002

Bioprocess and Biosystems Engineering

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The understanding of dynamic metabolic regulations is important for physiological studies and strain characterization tasks. The present study combined transient experiments with online metabolic flux analysis (MFA) in order to quantify metabolic regulations, namely carbon catabolite repression of respiration and transient acetic-acid production, in Saccharomyces cerevisiae during aerobic growth on glucose. The aim was to investigate which additional information can be gained from using a small metabolic flux model to study transient growth provoked by shift-up and shift-down experiments, compared to online monitoring alone. The MFA model allowed us to propose new correlations between pathways of the central metabolism. A linear correlation between glycolytic flux and respiratory capacity holds for shift-down and shift-up experiments. This confirmed that respiratory functions were subjected to carbon catabolite repression and suggested that respiratory capacity is controlled by the glycolytic flux rather than the glucose influx. Furthermore, the model showed that control of repression of respiration by the glycolytic flux was a dynamic phenomenon. Co-factor balancing within the MFA model showed that transient acetic-acid production indicated a transient limitation in another part of the central metabolism but not in oxidative phosphorylation. However, at super-critical growth rates and when coupling of anabolism and catabolism is resumed, the limitation shifts to oxidative phosphorylation, with the consequence that ethanol is formed. The online application of small metabolic flux models to transient experiments enhanced the physiological insight into transient growth and opens up the use of transient experiments as an efficient tool to understand dynamic metabolic regulations.

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Section: Transient Adaptation Of Respiratory Capacitysupporting

confidence: 67%

Section: Formulation Of the Metabolic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A small metabolic flux model to identify transient metabolic regulations in Saccharomyces cerevisiae

Herwig

Stockar

2002

Bioprocess and Biosystems Engineering

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“…To quantify possible changes in q anabolism in phase III, we used a reaction network model, which contains six macroscopic processes (Table 6) and assumes a quasi-steady state in phase III. This type of model has been used successfully to describe catabolic and anabolic processes in postpulse periods lasting several hours (12). The quasi-steady-state fluxes were estimated from metabolite concentration profiles at the end of …”

Section: Vol 72 2006 Metabolome Dynamics and Balances In S Cerevismentioning

confidence: 99%

Short-Term Metabolome Dynamics and Carbon, Electron, and ATP Balances in Chemostat-Grown Saccharomyces cerevisiae CEN.PK 113-7D following a Glucose Pulse

Dam

Schipper

et al. 2006

Appl Environ Microbiol

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The in vivo kinetics in Saccharomyces cerevisiae CEN.PK 113-7D was evaluated during a 300-second transient period after applying a glucose pulse to an aerobic, carbon-limited chemostat culture. We quantified the responses of extracellular metabolites, intracellular intermediates in primary metabolism, intracellular free amino acids, and in vivo rates of O 2 uptake and CO 2 evolution. With these measurements, dynamic carbon, electron, and ATP balances were set up to identify major carbon, electron, and energy sinks during the postpulse period. There were three distinct metabolic phases during this time. In phase I (0 to 50 seconds after the pulse), the carbon/electron balances closed up to 85%. The accumulation of glycolytic and storage compounds accounted for 60% of the consumed glucose, caused an energy depletion, and may have led to a temporary decrease in the anabolic flux. In phase II (50 to 150 seconds), the fermentative metabolism gradually became the most important carbon/electron sink. In phase III (150 to 300 seconds), 29% of the carbon uptake was not identified in the measurements, and the ATP balance had a large surplus. These results indicate an increase in the anabolic flux, which is consistent with macroscopic balances of extracellular fluxes and the observed increase in CO 2 evolution associated with nonfermentative metabolism. The identified metabolic processes involving major carbon, electron, and energy sinks must be taken into account in in vivo kinetic models based on short-term dynamic metabolome responses.Mathematical models of in vivo enzyme kinetics in microorganisms are important for understanding metabolic control mechanisms operating on the level of the metabolome and can be used to assist the rational redesign of metabolic pathways to enhance desired functionalities of microbes (54). Kinetic parameters in this kind of model can be obtained by stimulusresponse experiments, in which cells grown in a (quasi-)steady state are perturbed by an external stimulus and the dynamic responses of intra-and extracellular metabolites are monitored. The time window of observation is usually within tens to a few hundred seconds after the application of the stimulus, and the responses are usually attributed to rapid (allosteric) enzyme-metabolite interactions (38). Kinetic parameters can be estimated from the measured responses, based on a set of (dynamic) material balances (7, 30, 49), as follows: dx/dt ϭ Sv (equation 1), where x is a vector of the metabolite concentrations, S is the stoichiometry matrix, and v is a vector of the reaction rates as a function of (yet) unknown kinetic parameters. The stimulus-response methodology is an ideal tool for obtaining kinetic information and has been applied to various microorganisms under different growth conditions (7,26,32,38,52).For Saccharomyces cerevisiae, a frequently applied perturbation is the addition of a concentrated glucose solution, i.e., a glucose pulse, to a glucose-limited chemostat culture, thereby inducing a short-term Crabtree effect. Theoba...

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Experimentally Determined Rates of Bio‐Reactions

Villadsen

2015

Fundamental Bioengineering

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Simple generic model for dynamic experiments withSaccharomyces cerevisiae in continuous culture: Decoupling between anabolism and catabolism

Cited by 35 publications

References 22 publications

A small metabolic flux model to identify transient metabolic regulations in Saccharomyces cerevisiae

A small metabolic flux model to identify transient metabolic regulations in Saccharomyces cerevisiae

Short-Term Metabolome Dynamics and Carbon, Electron, and ATP Balances in Chemostat-Grown Saccharomyces cerevisiae CEN.PK 113-7D following a Glucose Pulse

Experimentally Determined Rates of Bio‐Reactions

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