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A generalised enzyme kinetic model for predicting the behaviour of complex biochemical systems.

Wong MK, Krycer JR, Burchfield JG, James DE, Kuncic Z - FEBS Open Bio (2015)

Bottom Line: The dQSSA was found to be easily adaptable for reversible enzyme kinetic systems with complex topologies and to predict behaviour consistent with mass action kinetics in silico.Whilst the dQSSA does not account for the physical and thermodynamic interactions of all intermediate enzyme-substrate complex states, it is proposed to be suitable for modelling complex enzyme mediated biochemical systems.This is due to its simpler application, reduced parameter dimensionality and improved accuracy.

View Article: PubMed Central - PubMed

Affiliation: School of Physics, University of Sydney, Sydney, NSW 2006, Australia ; Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia ; Diabetes and Metabolism Program, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.

ABSTRACT
Quasi steady-state enzyme kinetic models are increasingly used in systems modelling. The Michaelis Menten model is popular due to its reduced parameter dimensionality, but its low-enzyme and irreversibility assumption may not always be valid in the in vivo context. Whilst the total quasi-steady state assumption (tQSSA) model eliminates the reactant stationary assumptions, its mathematical complexity is increased. Here, we propose the differential quasi-steady state approximation (dQSSA) kinetic model, which expresses the differential equations as a linear algebraic equation. It eliminates the reactant stationary assumptions of the Michaelis Menten model without increasing model dimensionality. The dQSSA was found to be easily adaptable for reversible enzyme kinetic systems with complex topologies and to predict behaviour consistent with mass action kinetics in silico. Additionally, the dQSSA was able to predict coenzyme inhibition in the reversible lactate dehydrogenase enzyme, which the Michaelis Menten model failed to do. Whilst the dQSSA does not account for the physical and thermodynamic interactions of all intermediate enzyme-substrate complex states, it is proposed to be suitable for modelling complex enzyme mediated biochemical systems. This is due to its simpler application, reduced parameter dimensionality and improved accuracy.

No MeSH data available.


Related in: MedlinePlus

The dQSSA model predicts coenzyme competition better than the Michaelis–Menten model. The pyruvate-to-lactate reaction was conducted in the presence of increasing concentrations of the opposing coenzyme, NAD+ (see Section 6). The initial velocity of these reactions () was then normalised to the initial velocity without NAD+ (). Data shown are mean ± SEM from six experiments performed with four replicate wells per condition.
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f0035: The dQSSA model predicts coenzyme competition better than the Michaelis–Menten model. The pyruvate-to-lactate reaction was conducted in the presence of increasing concentrations of the opposing coenzyme, NAD+ (see Section 6). The initial velocity of these reactions () was then normalised to the initial velocity without NAD+ (). Data shown are mean ± SEM from six experiments performed with four replicate wells per condition.

Mentions: At this point, it was necessary to verify the generated model. It is expected that when a reaction is initiated in a single direction, the presence of the opposing coenzyme will cause inhibition as some enzyme is bound with the wrong coenzyme. Given the quantitative nature of the dQSSA model, the degree of inhibition should be correctly predicted by the two models. As such, prediction of the change in initial reaction velocity of the pyruvate to lactate reaction, under varying concentration of NAD+ was used as the validating experiment. A good agreement was found between the dQSSA model’s prediction and the observed result (Fig. 7). On the other hand, the Michaelis–Menten model gave a different prediction from the dQSSA model which was a poorer fit to the experimental results (Fig. 7). Using an odds ratio quantification of the goodness of fit, the dQSSA is the better model with an odds ratio O = 2.3×1028 in favour of the dQSSA model. This shows that the dQSSA is able to make accurate temporal conditions for enzyme reactions under physiological conditions, and that the Michaelis–Menten model is inaccurate in a non-trivial way.


A generalised enzyme kinetic model for predicting the behaviour of complex biochemical systems.

Wong MK, Krycer JR, Burchfield JG, James DE, Kuncic Z - FEBS Open Bio (2015)

The dQSSA model predicts coenzyme competition better than the Michaelis–Menten model. The pyruvate-to-lactate reaction was conducted in the presence of increasing concentrations of the opposing coenzyme, NAD+ (see Section 6). The initial velocity of these reactions () was then normalised to the initial velocity without NAD+ (). Data shown are mean ± SEM from six experiments performed with four replicate wells per condition.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4383669&req=5

f0035: The dQSSA model predicts coenzyme competition better than the Michaelis–Menten model. The pyruvate-to-lactate reaction was conducted in the presence of increasing concentrations of the opposing coenzyme, NAD+ (see Section 6). The initial velocity of these reactions () was then normalised to the initial velocity without NAD+ (). Data shown are mean ± SEM from six experiments performed with four replicate wells per condition.
Mentions: At this point, it was necessary to verify the generated model. It is expected that when a reaction is initiated in a single direction, the presence of the opposing coenzyme will cause inhibition as some enzyme is bound with the wrong coenzyme. Given the quantitative nature of the dQSSA model, the degree of inhibition should be correctly predicted by the two models. As such, prediction of the change in initial reaction velocity of the pyruvate to lactate reaction, under varying concentration of NAD+ was used as the validating experiment. A good agreement was found between the dQSSA model’s prediction and the observed result (Fig. 7). On the other hand, the Michaelis–Menten model gave a different prediction from the dQSSA model which was a poorer fit to the experimental results (Fig. 7). Using an odds ratio quantification of the goodness of fit, the dQSSA is the better model with an odds ratio O = 2.3×1028 in favour of the dQSSA model. This shows that the dQSSA is able to make accurate temporal conditions for enzyme reactions under physiological conditions, and that the Michaelis–Menten model is inaccurate in a non-trivial way.

Bottom Line: The dQSSA was found to be easily adaptable for reversible enzyme kinetic systems with complex topologies and to predict behaviour consistent with mass action kinetics in silico.Whilst the dQSSA does not account for the physical and thermodynamic interactions of all intermediate enzyme-substrate complex states, it is proposed to be suitable for modelling complex enzyme mediated biochemical systems.This is due to its simpler application, reduced parameter dimensionality and improved accuracy.

View Article: PubMed Central - PubMed

Affiliation: School of Physics, University of Sydney, Sydney, NSW 2006, Australia ; Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia ; Diabetes and Metabolism Program, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.

ABSTRACT
Quasi steady-state enzyme kinetic models are increasingly used in systems modelling. The Michaelis Menten model is popular due to its reduced parameter dimensionality, but its low-enzyme and irreversibility assumption may not always be valid in the in vivo context. Whilst the total quasi-steady state assumption (tQSSA) model eliminates the reactant stationary assumptions, its mathematical complexity is increased. Here, we propose the differential quasi-steady state approximation (dQSSA) kinetic model, which expresses the differential equations as a linear algebraic equation. It eliminates the reactant stationary assumptions of the Michaelis Menten model without increasing model dimensionality. The dQSSA was found to be easily adaptable for reversible enzyme kinetic systems with complex topologies and to predict behaviour consistent with mass action kinetics in silico. Additionally, the dQSSA was able to predict coenzyme inhibition in the reversible lactate dehydrogenase enzyme, which the Michaelis Menten model failed to do. Whilst the dQSSA does not account for the physical and thermodynamic interactions of all intermediate enzyme-substrate complex states, it is proposed to be suitable for modelling complex enzyme mediated biochemical systems. This is due to its simpler application, reduced parameter dimensionality and improved accuracy.

No MeSH data available.


Related in: MedlinePlus