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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 hypothetical network constructed for in silico model validation. Network is illustrated in accordance for the Systems Biology Graphical Notation (SBGN) [45]. (A, B) and (C, D) Belong to two different pathways which cross-talk when I (circular species) is added. This activates shaded reactions and enables formation of shaded species. The numbers in parentheses next to the species refer to their indices in the model equations in the computational program (Doc S5).
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f0010: The hypothetical network constructed for in silico model validation. Network is illustrated in accordance for the Systems Biology Graphical Notation (SBGN) [45]. (A, B) and (C, D) Belong to two different pathways which cross-talk when I (circular species) is added. This activates shaded reactions and enables formation of shaded species. The numbers in parentheses next to the species refer to their indices in the model equations in the computational program (Doc S5).

Mentions: The network was designed with an activator I which flips the network between two regimes (Fig. 2). The first regime (the I-off network), is designed to satisfy the simpler secondary goal of mechanistic accuracy. The only active reactions are unimolecular, bimolecular and a reversible enzyme reaction (the reverse reaction for reaction 10 is technically active in the I-off network, but as it is unfavoured, its effects are mostly negligible). This allows us to isolate the performance of the dQSSA within a controlled enzymatic system. A separate arm also tests the dQSSA’s ability to model fundamental reactions. The second regime (the I-on network), opens the network to topological complexity which allows us to assess the ability of the dQSSA to handle networks with complex coupling between reactions. The following coupling mechanisms were tested:•


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 hypothetical network constructed for in silico model validation. Network is illustrated in accordance for the Systems Biology Graphical Notation (SBGN) [45]. (A, B) and (C, D) Belong to two different pathways which cross-talk when I (circular species) is added. This activates shaded reactions and enables formation of shaded species. The numbers in parentheses next to the species refer to their indices in the model equations in the computational program (Doc S5).
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

f0010: The hypothetical network constructed for in silico model validation. Network is illustrated in accordance for the Systems Biology Graphical Notation (SBGN) [45]. (A, B) and (C, D) Belong to two different pathways which cross-talk when I (circular species) is added. This activates shaded reactions and enables formation of shaded species. The numbers in parentheses next to the species refer to their indices in the model equations in the computational program (Doc S5).
Mentions: The network was designed with an activator I which flips the network between two regimes (Fig. 2). The first regime (the I-off network), is designed to satisfy the simpler secondary goal of mechanistic accuracy. The only active reactions are unimolecular, bimolecular and a reversible enzyme reaction (the reverse reaction for reaction 10 is technically active in the I-off network, but as it is unfavoured, its effects are mostly negligible). This allows us to isolate the performance of the dQSSA within a controlled enzymatic system. A separate arm also tests the dQSSA’s ability to model fundamental reactions. The second regime (the I-on network), opens the network to topological complexity which allows us to assess the ability of the dQSSA to handle networks with complex coupling between reactions. The following coupling mechanisms were tested:•

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