ArticleSystems biology2005
Reduced-order modelling of biochemical networks: application to the GTPase-cycle signalling module.
Article in Systems biology, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 42 citations in OpenAlex.
- HillTau: A fast, compact abstraction for model reduction in biochemical signaling networks.PLoS computational biology · 2021Article
- Inferring phenomenological models of first passage processes.PLoS computational biology · 2021Article
- Model reduction in mathematical pharmacology : Integration, reduction and linking of PBPK and systems biology models.Journal of pharmacokinetics and pharmacodynamics · 2018Article
- Evaluating model reduction under parameter uncertainty.BMC systems biology · 2018Article
- Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends.Bulletin of mathematical biology · 2017Article
- A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment.Current opinion in systems biology · 2017Article
- A combined model reduction algorithm for controlled biochemical systems.BMC systems biology · 2017Article
- Article
- A model reduction method for biochemical reaction networks.BMC systems biology · 2014Article
- Crosstalks between cytokines and Sonic Hedgehog in Helicobacter pylori infection: a mathematical model.PloS one · 2014Article
- Reduced dynamic models in epithelial transport.Journal of biophysics (Hindawi Publishing Corporation : Online) · 2013Article
- Mixed-integer nonlinear optimisation approach to coarse-graining biochemical networks.IET systems biology · 2009Article
- Dynamical modeling of the cholesterol regulatory pathway with Boolean networks.BMC systems biology · 2008Article
- A top-down approach to mechanistic biological modeling: application to the single-chain antibody folding pathway.Biophysical journal · 2008Article
- Memory switches in chemical reaction space.PLoS computational biology · 2008Article
- A kinetic model for calcium dynamics in RAW 264.7 cells: 1. Mechanisms, parameters, and subpopulational variability.Biophysical journal · 2007Article
- Both ligand- and cell-specific parameters control ligand agonism in a kinetic model of g protein-coupled receptor signaling.PLoS computational biology · 2007Article
- Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models.Wiley interdisciplinary reviews. Systems biology and medicineReview
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.