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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.


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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)

© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

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.