Dataset Information

Reduced-order modelling of biochemical networks: application to the GTPase-cycle signalling module.

ABSTRACT: Biochemical systems embed complex networks and hence development and analysis of their detailed models pose a challenge for computation. Coarse-grained biochemical models, called reduced-order models (ROMs), consisting of essential biochemical mechanisms are more useful for computational analysis and for studying important features of a biochemical network. The authors present a novel method to model-reduction by identifying potentially important parameters using multidimensional sensitivity analysis. A ROM is generated for the GTPase-cycle module of m1 muscarinic acetylcholine receptor, Gq, and regulator of G-protein signalling 4 (a GTPase-activating protein or GAP) starting from a detailed model of 48 reactions. The resulting ROM has only 17 reactions. The ROM suggested that complexes of G-protein coupled receptor (GPCR) and GAP--which were proposed in the detailed model as a hypothesis--are required to fit the experimental data. Models previously published in the literature are also simulated and compared with the ROM. Through this comparison, a minimal ROM, that also requires complexes of GPCR and GAP, with just 15 parameters is generated. The proposed reduced-order modelling methodology is scalable to larger networks and provides a general framework for the reduction of models of biochemical systems.

SUBMITTER: Maurya MR

PROVIDER: S-EPMC3417759 | biostudies-other | 2005 Dec

REPOSITORIES: biostudies-other

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Reduced-order modelling of biochemical networks: application to the GTPase-cycle signalling module.

Maurya M R MR Bornheimer S J SJ Venkatasubramanian V V Subramaniam S S

Systems biology 20051201 4

Biochemical systems embed complex networks and hence development and analysis of their detailed models pose a challenge for computation. Coarse-grained biochemical models, called reduced-order models (ROMs), consisting of essential biochemical mechanisms are more useful for computational analysis and for studying important features of a biochemical network. The authors present a novel method to model-reduction by identifying potentially important parameters using multidimensional sensitivity ana ...[more]

PMID: 16986265

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Dataset Information

Reduced-order modelling of biochemical networks: application to the GTPase-cycle signalling module.

Publications

Reduced-order modelling of biochemical networks: application to the GTPase-cycle signalling module.

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