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2014 ; 8
(ä): 52
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A model reduction method for biochemical reaction networks
#MMPMID24885656
Rao S
; van der Schaft A
; van Eunen K
; Bakker BM
; Jayawardhana B
BMC Syst Biol
2014[May]; 8
(ä): 52
PMID24885656
show ga
BACKGROUND: In this paper we propose a model reduction method for biochemical
reaction networks governed by a variety of reversible and irreversible enzyme
kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics. The
method proceeds by a stepwise reduction in the number of complexes, defined as
the left and right-hand sides of the reactions in the network. It is based on the
Kron reduction of the weighted Laplacian matrix, which describes the graph
structure of the complexes and reactions in the network. It does not rely on
prior knowledge of the dynamic behaviour of the network and hence can be
automated, as we demonstrate. The reduced network has fewer complexes, reactions,
variables and parameters as compared to the original network, and yet the
behaviour of a preselected set of significant metabolites in the reduced network
resembles that of the original network. Moreover the reduced network largely
retains the structure and kinetics of the original model. RESULTS: We apply our
method to a yeast glycolysis model and a rat liver fatty acid beta-oxidation
model. When the number of state variables in the yeast model is reduced from 12
to 7, the difference between metabolite concentrations in the reduced and the
full model, averaged over time and species, is only 8%. Likewise, when the number
of state variables in the rat-liver beta-oxidation model is reduced from 42 to
29, the difference between the reduced model and the full model is 7.5%.
CONCLUSIONS: The method has improved our understanding of the dynamics of the two
networks. We found that, contrary to the general disposition, the first few
metabolites which were deleted from the network during our stepwise reduction
approach, are not those with the shortest convergence times. It shows that our
reduction approach performs differently from other approaches that are based on
time-scale separation. The method can be used to facilitate fitting of the
parameters or to embed a detailed model of interest in a more coarse-grained yet
realistic environment.
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