Dataset Information

Suh2004_KCNQ_Regulation

ABSTRACT: The model reproduces FIG 11A and FIG 11B of the paper. However, please note that FIG 11B is a plot of normalised amounts versus time. The "stoichiometry" field has been used to convert fluxes from membrane species to volume species. The value of 0.0009967 is a product of (Surface to Volume_M*(1/Avagadro's number)*1E21. 0.6 is the surface to volume ratio of the plasma membrane, 1E21 is required for a unit surface to volume ratio and the Avagadro's number is present in the denominator to convert molecules to moles. The model was successfully tested using MathSBML and SBML ODESolver. All the kinetic laws have the unit items per second , which requires the one reaction taking place in the cytoplasm - IP3Phosphatase - to include an explicit conversion factor both in the kinetic law and the stoichiometry of IP3_C . The kinetic law is multiplied and the stoichiometry divided by the number of molecules per micro-mole. This conversion factor is only required for correct units and can be replaced by 1, if it should lead to numerical problems. 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: Harish Dharuri

PROVIDER: BIOMD0000000081 | BioModels | 2024-09-02

REPOSITORIES: BioModels

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	BIOMD0000000081?filename=BIOMD0000000081-biopax2.owl	Owl
	BIOMD0000000081?filename=BIOMD0000000081-biopax3.owl	Owl
	BIOMD0000000081?filename=BIOMD0000000081-matlab.m	Other
	BIOMD0000000081?filename=BIOMD0000000081.m	Other
	BIOMD0000000081?filename=BIOMD0000000081.ode	Other

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Publications

Regulation of KCNQ2/KCNQ3 current by G protein cycling: the kinetics of receptor-mediated signaling by Gq.

Suh Byung-Chang BC Horowitz Lisa F LF Hirdes Wiebke W Mackie Ken K Hille Bertil B

The Journal of general physiology 20040601 6

Receptor-mediated modulation of KCNQ channels regulates neuronal excitability. This study concerns the kinetics and mechanism of M1 muscarinic receptor-mediated regulation of the cloned neuronal M channel, KCNQ2/KCNQ3 (Kv7.2/Kv7.3). Receptors, channels, various mutated G-protein subunits, and an optical probe for phosphatidylinositol 4,5-bisphosphate (PIP2) were coexpressed by transfection in tsA-201 cells, and the cells were studied by whole-cell patch clamp and by confocal microscopy. Constitu ...[more]

PMID: 15173220

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Project description:This is the core model described in the article: Covering a Broad Dynamic Range: Information Processing at the Erythropoietin Receptor Verena Becker, Marcel Schilling, Julie Bachmann, Ute Baumann, Andreas Raue, Thomas Maiwald, Jens Timmer and Ursula Klingmüller; Science Published Online May 20, 2010; DOI: 10.1126/science.1184913 PMID: 20488988 Abstract: Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input, covering a broad range of ligand concentrations. This relation solely depends on EpoR turnover independent of ligand binding, suggesting an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system. SBML model exported from PottersWheel. % PottersWheel model definition file function m = BeckerSchilling2010_EpoR_CoreModel() m = pwGetEmptyModel(); %% Meta information m.ID = 'BeckerSchilling2010_EpoR_CoreModel'; m.name = 'BeckerSchilling2010_EpoR_CoreModel'; m.description = 'BeckerSchilling2010_EpoR_CoreModel'; m.authors = {'Verena Becker',' Marcel Schilling'}; m.dates = {'2010'}; m.type = 'PW-2-0-42'; %% X: Dynamic variables % m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue) m = pwAddX(m, 'EpoR' , 516, 'fix' , 0, 10000, [], 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'Epo' , 2030.19, 'global', 1890, 2310, [], 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'Epo_EpoR' , 0, 'fix' , 0, 10000, [], 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'Epo_EpoRi', 0, 'fix' , 0, 10000, [], 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'dEpoi' , 0, 'fix' , 0, 10000, [], 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'dEpoe' , 0, 'fix' , 0, 10000, [], 'cell', [] , [] , [] , [] , 'protein.generic'); %% R: Reactions % m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible) m = pwAddR(m, { }, {'EpoR' }, { }, 'C' , [] , 'k1*k2', {'kt','Bmax'}, [], 'reaction0001'); m = pwAddR(m, {'EpoR' }, { }, { }, 'MA', [] , [] , {'kt' }, [], 'reaction0002'); m = pwAddR(m, {'Epo','EpoR'}, {'Epo_EpoR' }, { }, 'MA', [] , [] , {'kon' }, [], 'reaction0003'); m = pwAddR(m, {'Epo_EpoR' }, {'Epo','EpoR'}, { }, 'MA', [] , [] , {'koff' }, [], 'reaction0004'); m = pwAddR(m, {'Epo_EpoR' }, {'Epo_EpoRi' }, { }, 'MA', [] , [] , {'ke' }, [], 'reaction0005'); m = pwAddR(m, {'Epo_EpoRi' }, {'Epo','EpoR'}, { }, 'MA', [] , [] , {'kex' }, [], 'reaction0006'); m = pwAddR(m, {'Epo_EpoRi' }, {'dEpoi' }, { }, 'MA', [] , [] , {'kdi' }, [], 'reaction0007'); m = pwAddR(m, {'Epo_EpoRi' }, {'dEpoe' }, { }, 'MA', [] , [] , {'kde' }, [], 'reaction0008'); %% C: Compartments % m = pwAddC(m, ID, size, outside, spatialDimensions, name, unit, constant) m = pwAddC(m, 'cell', 1); %% K: Dynamical parameters % m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description) m = pwAddK(m, 'kt' , 0.0329366 , 'global', 1e-007, 1000); m = pwAddK(m, 'Bmax', 516 , 'fix' , 492 , 540 ); m = pwAddK(m, 'kon' , 0.00010496, 'global', 1e-007, 1000); m = pwAddK(m, 'koff', 0.0172135 , 'global', 1e-007, 1000); m = pwAddK(m, 'ke' , 0.0748267 , 'global', 1e-007, 1000); m = pwAddK(m, 'kex' , 0.00993805, 'global', 1e-007, 1000); m = pwAddK(m, 'kdi' , 0.00317871, 'global', 1e-007, 1000); m = pwAddK(m, 'kde' , 0.0164042 , 'global', 1e-007, 1000); %% Default sampling time points m.t = 0:3:99; %% Y: Observables % m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps) m = pwAddY(m, 'Epo + dEpoe' , 'Epo_extracellular_obs'); m = pwAddY(m, 'Epo_EpoR' , 'Epo_cellsurface_obs' ); m = pwAddY(m, 'Epo_EpoRi + dEpoi', 'Epo_intracellular_obs'); %% S: Scaling parameters % m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description) m = pwAddS(m, 'scale_Epo_extracellular_obs', 1, 'fix', 0, 100); m = pwAddS(m, 'scale_Epo_cellsurface_obs' , 1, 'fix', 0, 100); m = pwAddS(m, 'scale_Epo_intracellular_obs', 1, 'fix', 0, 100); %% Designer properties (do not modify) m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0]; This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels.net Team. For more information see the terms of use . 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.

Project description:This is the auxiliary model described in the article: Covering a Broad Dynamic Range: Information Processing at the Erythropoietin Receptor Verena Becker, Marcel Schilling, Julie Bachmann, Ute Baumann, Andreas Raue, Thomas Maiwald, Jens Timmer and Ursula Klingmüller; Science Published Online May 20, 2010; DOI: 10.1126/science.1184913 PMID: 20488988 Abstract: Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input, covering a broad range of ligand concentrations. This relation solely depends on EpoR turnover independent of ligand binding, suggesting an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system. SBML model exported from PottersWheel. % PottersWheel model definition file function m = BeckerSchilling2010_EpoR_AuxiliaryMode() m = pwGetEmptyModel(); %% Meta information m.ID = 'BeckerSchilling2010_EpoR_AuxiliaryMode'; m.name = 'BeckerSchilling2010_EpoR_AuxiliaryModel'; m.description = 'BeckerSchilling2010_EpoR_AuxiliaryModel'; m.authors = {'Verena Becker',' Marcel Schilling'}; m.dates = {'2010'}; m.type = 'PW-2-0-42'; %% X: Dynamic variables % m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue) m = pwAddX(m, 'EpoR' , 76, 'fix' , 0, 10000, [], 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'SAv' , 999.293, 'global', 900, 1100, [], 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'SAv_EpoR' , 0, 'fix' , 0, 10000, [], 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'SAv_EpoRi', 0, 'fix' , 0, 10000, [], 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'dSAvi' , 0, 'fix' , 0, 10000, [], 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'dSAve' , 0, 'fix' , 0, 10000, [], 'cell', [] , [] , [] , [] , 'protein.generic'); %% R: Reactions % m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible) m = pwAddR(m, { }, {'EpoR' }, { }, 'C' , [] , 'k1*k2', {'kt','Bmax_SAv'}, [], 'reaction0001'); m = pwAddR(m, {'EpoR' }, { }, { }, 'MA', [] , [] , {'kt' }, [], 'reaction0002'); m = pwAddR(m, {'SAv','EpoR'}, {'SAv_EpoR' }, { }, 'MA', [] , [] , {'kon_SAv' }, [], 'reaction0003'); m = pwAddR(m, {'SAv_EpoR' }, {'SAv','EpoR'}, { }, 'MA', [] , [] , {'koff_SAv' }, [], 'reaction0004'); m = pwAddR(m, {'SAv_EpoR' }, {'SAv_EpoRi' }, { }, 'MA', [] , [] , {'kt' }, [], 'reaction0005'); m = pwAddR(m, {'SAv_EpoRi' }, {'SAv' }, { }, 'MA', [] , [] , {'kex_SAv' }, [], 'reaction0006'); m = pwAddR(m, {'SAv_EpoRi' }, {'dSAvi' }, { }, 'MA', [] , [] , {'kdi' }, [], 'reaction0007'); m = pwAddR(m, {'SAv_EpoRi' }, {'dSAve' }, { }, 'MA', [] , [] , {'kde' }, [], 'reaction0008'); %% C: Compartments % m = pwAddC(m, ID, size, outside, spatialDimensions, name, unit, constant) m = pwAddC(m, 'cell', 1); %% K: Dynamical parameters % m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description) m = pwAddK(m, 'kt' , 0.0329366 , 'global', 1e-007, 1000); m = pwAddK(m, 'Bmax_SAv', 76 , 'fix' , 61 , 91 ); m = pwAddK(m, 'kon_SAv' , 2.29402e-006, 'global', 1e-007, 1000); m = pwAddK(m, 'koff_SAv', 0.00679946 , 'global', 1e-007, 1000); m = pwAddK(m, 'kex_SAv' , 0.01101 , 'global', 1e-007, 1000); m = pwAddK(m, 'kdi' , 0.00317871 , 'global', 1e-007, 1000); m = pwAddK(m, 'kde' , 0.0164042 , 'global', 1e-007, 1000); %% Default sampling time points m.t = 0:3:99; %% Y: Observables % m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps) m = pwAddY(m, 'SAv + dSAve' , 'SAv_extracellular_obs'); m = pwAddY(m, 'SAv_EpoR' , 'SAv_cellsurface_obs' ); m = pwAddY(m, 'SAv_EpoRi + dSAvi', 'SAv_intracellular_obs'); %% S: Scaling parameters % m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description) m = pwAddS(m, 'scale_SAv_extracellular_obs', 1, 'fix', 0, 100); m = pwAddS(m, 'scale_SAv_cellsurface_obs' , 1, 'fix', 0, 100); m = pwAddS(m, 'scale_SAv_intracellular_obs', 1, 'fix', 0, 100); %% Designer properties (do not modify) m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0]; This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels.net Team. For more information see the terms of use . 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.