Dataset Information

Maurya2005_GTPaseCycle_reducedOrder

ABSTRACT: This model is according to the paper Reduced-order modeling of biochemical networks: application to the GTPase-cycle signalling module by Maurya et al 2006.The figure 4c is reproduced by Copasi 4.0.19 (development) .It is three-dimensional logarithmic plots show the output of simulations of Z at various concentrations of R and GAP. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

SUBMITTER: Sharat Vayttaden

PROVIDER: BIOMD0000000085 | BioModels | 2024-09-02

REPOSITORIES: BioModels

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Dataset's files

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			Action	DRS
	BIOMD0000000085?filename=BIOMD0000000085-biopax2.owl	Owl
	BIOMD0000000085?filename=BIOMD0000000085-biopax3.owl	Owl
	BIOMD0000000085?filename=BIOMD0000000085-matlab.m	Other
	BIOMD0000000085?filename=BIOMD0000000085-octave.m	Other
	BIOMD0000000085?filename=BIOMD0000000085.m	Other

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Publications

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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