Journal
IEE PROCEEDINGS SYSTEMS BIOLOGY
Volume 152, Issue 4, Pages 229-242Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/ip-syb:20050014
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Funding
- NIGMS NIH HHS [R01 GM078005, R01 GM078005-04, U54 GM062114, U54 GM62114] Funding Source: Medline
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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.
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