Dataset Information

Raia2010 - IL13 Signalling MedB1

ABSTRACT: This is the model of IL13 induced signalling in MedB-1 cell described in the article: Dynamic Mathematical Modeling of IL13-Induced Signaling in Hodgkin and Primary Mediastinal B-Cell Lymphoma Allows Prediction of Therapeutic Targets. Raia V, Schilling M, Böhm M, Hahn B, Kowarsch A, Raue A, Sticht C, Bohl S, Saile M, Möller P, Gretz N, Timmer J, Theis F, Lehmann WD, Lichter P and Klingmüller U. Cancer Res. 2011 Feb 1;71(3):693-704. PubmedID:21127196; DOI:10.1158/0008-5472.CAN-10-2987 Abstract: Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets. All concentrations in the model, apart from IL13, are in molecules/cell. IL13 is given in ng/ml. As the cell volume is not explicitely given in the article, it is just approximately derived from the MW of IL13 () and the conversion factor 2.265 molecules IL13/cell = 1 ng/ml to be around 60 fl. SBML model exported from PottersWheel on 2010-08-10 12:14:57. Inline follows the original matlab code: % PottersWheel model definition file function m = Raia2010_IL13_MedB1() m = pwGetEmptyModel(); %% Meta information m.ID = 'Raia2010_IL13_MedB1'; m.name = 'Raia2010_IL13_MedB1'; m.description = ''; m.authors = {'Raia et al'}; m.dates = {'2010'}; m.type = 'PW-2-0-47'; %% X: Dynamic variables % m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue) m = pwAddX(m, 'Rec' , 1.3, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'Rec_i' , 113.193916718733, 'global', 0.001, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'IL13_Rec' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'p_IL13_Rec' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'p_IL13_Rec_i', 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'JAK2' , 2.8, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'pJAK2' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'SHP1' , 91, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'STAT5' , 165, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'pSTAT5' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'SOCS3mRNA' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'DecoyR' , 0.34, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'IL13_DecoyR' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'SOCS3' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'CD274mRNA' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', '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, {'Rec' }, {'IL13_Rec' }, {'IL13stimulation' }, 'C' , [] , 'k1 * m1 * r1 * 2.265' , {'Kon_IL13Rec' }); m = pwAddR(m, {'Rec' }, {'Rec_i' }, { }, 'MA', [] , [] , {'Rec_intern' }); m = pwAddR(m, {'Rec_i' }, {'Rec' }, { }, 'MA', [] , [] , {'Rec_recycle' }); m = pwAddR(m, {'IL13_Rec' }, {'p_IL13_Rec' }, {'pJAK2' }, 'E' , [] , [] , {'Rec_phosphorylation' }); m = pwAddR(m, {'JAK2' }, {'pJAK2' }, {'IL13_Rec','SOCS3' }, 'C' , [] , 'k1 * m1 * r1 / (1 + k2 * m2)', {'JAK2_phosphorylation','JAK2_p_inhibition'}); m = pwAddR(m, {'JAK2' }, {'pJAK2' }, {'p_IL13_Rec','SOCS3'}, 'C' , [] , 'k1 * m1 * r1 / (1 + k2 * m2)', {'JAK2_phosphorylation','JAK2_p_inhibition'}); m = pwAddR(m, {'p_IL13_Rec' }, {'p_IL13_Rec_i'}, { }, 'MA', [] , [] , {'pRec_intern' }); m = pwAddR(m, {'p_IL13_Rec_i'}, { }, { }, 'MA', [] , [] , {'pRec_degradation' }); m = pwAddR(m, {'pJAK2' }, {'JAK2' }, {'SHP1' }, 'E' , [] , [] , {'pJAK2_dephosphorylation' }); m = pwAddR(m, {'STAT5' }, {'pSTAT5' }, {'pJAK2' }, 'E' , [] , [] , {'STAT5_phosphorylation' }); m = pwAddR(m, {'pSTAT5' }, {'STAT5' }, {'SHP1' }, 'E' , [] , [] , {'pSTAT5_dephosphorylation' }); m = pwAddR(m, {'DecoyR' }, {'IL13_DecoyR' }, {'IL13stimulation' }, 'C' , [] , 'k1 * m1 * r1 * 2.265' , {'DecoyR_binding' }); m = pwAddR(m, { }, {'SOCS3mRNA' }, {'pSTAT5' }, 'C' , [] , 'm1*k1' , {'SOCS3mRNA_production' }); m = pwAddR(m, { }, {'SOCS3' }, {'SOCS3mRNA' }, 'C' , [] , 'm1*k1/(k2+m1)' , {'SOCS3_translation','SOCS3_accumulation' }); m = pwAddR(m, {'SOCS3' }, { }, { }, 'MA', [] , [] , {'SOCS3_degradation' }); m = pwAddR(m, { }, {'CD274mRNA' }, {'pSTAT5' }, 'C' , [] , 'm1*k1' , {'CD274mRNA_production' }); %% 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, 'Kon_IL13Rec' , 0.00341992477561527 , 'global', 1e-009, 1000); m = pwAddK(m, 'Rec_phosphorylation' , 999.630699390459 , 'global', 1e-009, 1000); m = pwAddK(m, 'pRec_intern' , 0.152540135862128 , 'global', 1e-009, 1000); m = pwAddK(m, 'pRec_degradation' , 0.17292753960894 , 'global', 1e-009, 1000); m = pwAddK(m, 'Rec_intern' , 0.103345784175639 , 'global', 1e-009, 1000); m = pwAddK(m, 'Rec_recycle' , 0.00135598001330518 , 'global', 1e-009, 1000); m = pwAddK(m, 'JAK2_phosphorylation' , 0.157057142470047 , 'global', 1e-009, 1000); m = pwAddK(m, 'pJAK2_dephosphorylation' , 0.000621906059346898 , 'global', 1e-009, 1000); m = pwAddK(m, 'STAT5_phosphorylation' , 0.0382596267705733 , 'global', 1e-009, 1000); m = pwAddK(m, 'pSTAT5_dephosphorylation', 0.000343391620492938 , 'global', 1e-009, 1000); m = pwAddK(m, 'SOCS3mRNA_production' , 0.00215826062955433 , 'global', 1e-009, 1000); m = pwAddK(m, 'DecoyR_binding' , 0.000124391087466499 , 'global', 1e-009, 1000); m = pwAddK(m, 'JAK2_p_inhibition' , 0.0168267797836881 , 'global', 1e-009, 1000); m = pwAddK(m, 'SOCS3_translation' , 11.9086462945188 , 'global', 1e-009, 1000); m = pwAddK(m, 'SOCS3_accumulation' , 3.70803336415341 , 'global', 1 , 1000); m = pwAddK(m, 'SOCS3_degradation' , 0.0429185935645562 , 'global', 1e-009, 1000); m = pwAddK(m, 'CD274mRNA_production' , 8.21752278733562e-005, 'global', 1e-009, 1000); %% U: Driving input % m = pwAddU(m, ID, uType, uTimes, uValues, compartment, name, description, u2Values, alternativeIDs, designerProps) m = pwAddU(m, 'IL13stimulation', 'steps', [-100 0] , [0 1] , [], [], [], [], {}, [], 'protein.generic'); %% Default sampling time points m.t = 0:1:120; %% Y: Observables % m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps) m = pwAddY(m, 'Rec + IL13_Rec + p_IL13_Rec' , 'RecSurf_obs' , 'scale_RecSurf' , '0.10 * y + 0.1 * max(y)'); m = pwAddY(m, 'IL13_Rec + p_IL13_Rec + p_IL13_Rec_i + IL13_DecoyR', 'IL13-cell_obs', 'scale_IL13-cell', '0.15 * y + 0.05 * max(y)'); m = pwAddY(m, 'p_IL13_Rec + p_IL13_Rec_i' , 'pIL4Ra_obs' , 'scale_pIL4Ra' , '0.1 * y + 0.15 * max(y)'); m = pwAddY(m, 'pJAK2' , 'pJAK2_obs' , 'scale_pJAK2' , '0.15 * y + 0.1 * max(y)'); m = pwAddY(m, 'SOCS3mRNA' , 'SOCS3mRNA_obs', 'scale_SOCS3mRNA', '0.1 * y + 0.1 * max(y)'); m = pwAddY(m, 'CD274mRNA' , 'CD274mRNA_obs', 'scale_CD274mRNA', '0.1 * y + 0.1 * max(y)'); m = pwAddY(m, 'SOCS3' , 'SOCS3_obs' , 'scale_SOCS3' , '0.1 * y + 0.15 * max(y)'); m = pwAddY(m, 'pSTAT5' , 'pSTAT5_obs' , 'scale_pSTAT5' , '0.15 * y + 0.1 * max(y)'); %% S: Scaling parameters % m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description) m = pwAddS(m, 'scale_pJAK2' , 1.39039557075997, 'global', 0.001, 10000); m = pwAddS(m, 'scale_pIL4Ra' , 1.88700484471494, 'global', 0.001, 10000); m = pwAddS(m, 'scale_RecSurf' , 1, 'fix', 0.001, 10000); m = pwAddS(m, 'scale_IL13-cell', 5.56750251420935, 'global', 0.001, 10000); m = pwAddS(m, 'scale_SOCS3mRNA', 17.6699101927908, 'global', 0.001, 10000); m = pwAddS(m, 'scale_CD274mRNA', 2.48547378765387, 'global', 0.001, 10000); m = pwAddS(m, 'scale_pSTAT5' , 1, 'fix', 0.001, 10000); m = pwAddS(m, 'scale_SOCS3' , 1, 'fix', 0.001, 10000); %% 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 (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 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 Nov��re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

OTHER RELATED OMICS DATASETS IN: PRJNA131727

DISEASE(S): Mature T-cell And Nk-cell Lymphoma

SUBMITTER: Marcel Schilling

PROVIDER: BIOMD0000000313 | BioModels | 2024-09-02

REPOSITORIES: BioModels

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			Action	DRS
	BIOMD0000000313?filename=BIOMD0000000313-biopax2.owl	Owl
	BIOMD0000000313?filename=BIOMD0000000313-biopax3.owl	Owl
	BIOMD0000000313?filename=BIOMD0000000313-matlab.m	Other
	BIOMD0000000313?filename=BIOMD0000000313.m	Other
	BIOMD0000000313?filename=BIOMD0000000313.ode	Other

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Publications

Dynamic mathematical modeling of IL13-induced signaling in Hodgkin and primary mediastinal B-cell lymphoma allows prediction of therapeutic targets.

Raia Valentina V Schilling Marcel M Böhm Martin M Hahn Bettina B Kowarsch Andreas A Raue Andreas A Sticht Carsten C Bohl Sebastian S Saile Maria M Möller Peter P Gretz Norbert N Timmer Jens J Theis Fabian F Lehmann Wolf-Dieter WD Lichter Peter P Klingmüller Ursula U

Cancer research 20101202 3

Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 ...[more]

PMID: 21127196

Similar Datasets

Project description:This is the model of IL13 induced signalling in L1236 cells described in the article: Dynamic Mathematical Modeling of IL13-Induced Signaling in Hodgkin and Primary Mediastinal B-Cell Lymphoma Allows Prediction of Therapeutic Targets. Raia V, Schilling M, Böhm M, Hahn B, Kowarsch A, Raue A, Sticht C, Bohl S, Saile M, Möller P, Gretz N, Timmer J, Theis F, Lehmann WD, Lichter P and Klingmüller U. Cancer Res. 2011 Feb 1;71(3):693-704. PubmedID: 21127196 ; DOI: 10.1158/0008-5472.CAN-10-2987 Abstract: Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets. All concentrations in the model, apart from IL13, are in molecules/cell. IL13 is given in ng/ml. As the cell volume is not explicitely given in the article, it is just approximately derived from the MW of IL13 (15.8 kDa) and the conversion factor 3.776 molecules IL13/cell = 1 ng/ml to be around 100 fl. SBML model exported from PottersWheel on 2010-08-10 12:14:57. Inline follows the original matlab code: % PottersWheel model definition file function m = Raia2010_IL13_L1236() m = pwGetEmptyModel(); %% Meta information m.ID = 'Raia2010_IL13_L1236'; m.name = 'Raia2010_IL13_L1236'; m.description = ''; m.authors = {'Raia et al'}; m.dates = {'2010'}; m.type = 'PW-2-0-47'; %% X: Dynamic variables % m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue) m = pwAddX(m, 'Rec' , 1.8, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'Rec_i' , 118.598421286338, 'global', 0.001, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'IL13_Rec' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'p_IL13_Rec' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'p_IL13_Rec_i', 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'JAK2' , 24, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'pJAK2' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'SHP1' , 9.4, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'STAT5' , 209, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'pSTAT5' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic'); m = pwAddX(m, 'CD274mRNA' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', '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, {'Rec' }, {'IL13_Rec' }, {'IL13stimulation'}, 'C' , [] , 'k1 * m1 * r1 * 3.776', {'Kon_IL13Rec' }); m = pwAddR(m, {'Rec' }, {'Rec_i' }, { }, 'MA', [] , [] , {'Rec_intern' }); m = pwAddR(m, {'Rec_i' }, {'Rec' }, { }, 'MA', [] , [] , {'Rec_recycle' }); m = pwAddR(m, {'IL13_Rec' }, {'p_IL13_Rec' }, {'pJAK2' }, 'E' , [] , [] , {'Rec_phosphorylation' }); m = pwAddR(m, {'JAK2' }, {'pJAK2' }, {'IL13_Rec' }, 'E' , [] , [] , {'JAK2_phosphorylation' }); m = pwAddR(m, {'JAK2' }, {'pJAK2' }, {'p_IL13_Rec' }, 'E' , [] , [] , {'JAK2_phosphorylation' }); m = pwAddR(m, {'p_IL13_Rec' }, {'p_IL13_Rec_i'}, { }, 'MA', [] , [] , {'pRec_intern' }); m = pwAddR(m, {'p_IL13_Rec_i'}, { }, { }, 'MA', [] , [] , {'pRec_degradation' }); m = pwAddR(m, {'pJAK2' }, {'JAK2' }, {'SHP1' }, 'E' , [] , [] , {'pJAK2_dephosphorylation' }); m = pwAddR(m, {'STAT5' }, {'pSTAT5' }, {'pJAK2' }, 'E' , [] , [] , {'STAT5_phosphorylation' }); m = pwAddR(m, {'pSTAT5' }, {'STAT5' }, {'SHP1' }, 'E' , [] , [] , {'pSTAT5_dephosphorylation'}); m = pwAddR(m, { }, {'CD274mRNA' }, {'pSTAT5' }, 'C' , [] , 'm1*k1' , {'CD274mRNA_production' }); %% 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, 'Kon_IL13Rec' , 0.00174086832237195, 'global', 1e-009, 1000); m = pwAddK(m, 'Rec_phosphorylation' , 9.07540737285078 , 'global', 1e-009, 1000); m = pwAddK(m, 'pRec_intern' , 0.324132341358502 , 'global', 1e-009, 1000); m = pwAddK(m, 'pRec_degradation' , 0.417538218767296 , 'global', 1e-009, 1000); m = pwAddK(m, 'Rec_intern' , 0.259685756311325 , 'global', 1e-009, 1000); m = pwAddK(m, 'Rec_recycle' , 0.00392430355501153, 'global', 1e-009, 1000); m = pwAddK(m, 'JAK2_phosphorylation' , 0.300019047540849 , 'global', 1e-009, 1000); m = pwAddK(m, 'pJAK2_dephosphorylation' , 0.0981610557569751 , 'global', 1e-009, 1000); m = pwAddK(m, 'STAT5_phosphorylation' , 0.00426766529531612, 'global', 1e-009, 1000); m = pwAddK(m, 'pSTAT5_dephosphorylation', 0.0116388587580445 , 'global', 1e-009, 1000); m = pwAddK(m, 'CD274mRNA_production' , 0.0115927572109515 , 'global', 1e-009, 1000); %% U: Driving input % m = pwAddU(m, ID, uType, uTimes, uValues, compartment, name, description, u2Values, alternativeIDs, designerProps) m = pwAddU(m, 'IL13stimulation', 'steps', [-100 0] , [0 1] , [], [], [], [], {}, [], 'protein.generic'); %% Default sampling time points m.t = 0:1:120; %% Y: Observables % m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps) m = pwAddY(m, 'Rec + IL13_Rec + p_IL13_Rec' , 'RecSurf_obs' , 'scale_RecSurf' , '0.1 * y + 0.1 * max(y)'); m = pwAddY(m, 'IL13_Rec + p_IL13_Rec + p_IL13_Rec_i', 'IL13-cell_obs', 'scale_IL13-cell', '0.15 * y + 0.05 * max(y)'); m = pwAddY(m, 'p_IL13_Rec + p_IL13_Rec_i' , 'pIL4Ra_obs' , 'scale_pIL4Ra' , '0.10 * y + 0.15 * max(y)'); m = pwAddY(m, 'pJAK2' , 'pJAK2_obs' , 'scale_pJAK2' , '0.1 * y + 0.1 * max(y)'); m = pwAddY(m, 'CD274mRNA' , 'CD274mRNA_obs', 'scale_CD274mRNA', '0.1 * y + 0.1 * max(y)'); m = pwAddY(m, 'pSTAT5' , 'pSTAT5_obs' , 'scale_pSTAT5' , '0.1 * y + 0.1 * max(y)'); %% S: Scaling parameters % m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description) m = pwAddS(m, 'scale_pJAK2' , 0.469836894150194, 'global', 0.001, 10000); m = pwAddS(m, 'scale_pIL4Ra' , 1.80002942264669, 'global', 0.001, 10000); m = pwAddS(m, 'scale_RecSurf' , 1, 'fix', 0.0001, 10000); m = pwAddS(m, 'scale_IL13-cell', 174.726805005048, 'global', 0.001, 10000); m = pwAddS(m, 'scale_CD274mRNA', 0.110568221201943, 'global', 0.001, 10000); m = pwAddS(m, 'scale_pSTAT5' , 1, 'fix', 0.001, 10000); %% Designer properties (do not modify) m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];

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.

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 model is from the article: Quantitative analysis of transient and sustained transforming growth factor-β signaling dynamics. Zhike Zi, Zipei Feng, Douglas A Chapnick, Markus Dahl, Difan Deng, Edda Klipp, Aristidis Moustakas & Xuedong Liu Molecular Systems Biology 2011 May 24;7:492. 21613981 , Abstract: Mammalian cells can decode the concentration of extracellular transforming growth factor-β (TGF-β) and transduce this cue into appropriate cell fate decisions. How variable TGF-β ligand doses quantitatively control intracellular signaling dynamics and how continuous ligand doses are translated into discontinuous cellular fate decisions remain poorly understood. Using a combined experimental and mathematical modeling approach, we discovered that cells respond differently to continuous and pulsating TGF-β stimulation. The TGF-β pathway elicits a transient signaling response to a single pulse of TGF-β stimulation, whereas it is capable of integrating repeated pulses of ligand stimulation at short time interval, resulting in sustained phospho-Smad2 and transcriptional responses. Additionally, the TGF-β pathway displays different sensitivities to ligand doses at different time scales. While ligand-induced short-term Smad2 phosphorylation is graded, long-term Smad2 phosphorylation is switch-like to a small change in TGF-β levels. Correspondingly, the short-term Smad7 gene expression is graded, while long-term PAI-1 gene expression is switch-like, as is the long-term growth inhibitory response. Our results suggest that long-term switch-like signaling responses in the TGF-β pathway might be critical for cell fate determination. Note: Developer of the model: Zhike Zi Reference: Zi Z. et al., Quantitative Analysis of Transient and Sustained Transforming Growth Factor-beta Signaling Dynamics, Molecular Systems Biology, 2011 1. The global parameter that set the type of stimulation (a) for sustained TGF-beta stimulation: set stimulation_type = 1. (b) for single pulse of TGF-beta stimulation: set stimulation_type = 2. parameter "single_pulse_duration" is for the duration of stimulation, for example, single_pulse_duration = 0.5, for 0.5 min (30 seconds) of TGF-beta stimulation. *Note: make sure that the time course cover the time point when the event is triggered. (c) for single pulse of TGF-beta stimulation in COPASI change the trigger of event "single_pulse_TGF_beta_washout" from "and(eq(stimulation_type, 2), eq(time, single_pulse_duration))" (for SBML-SAT) to "and(eq(stimulation_type, 2), gt(time, single_pulse_duration))" (for COPASI) 2. Notes for TGF-beta dose in terms of molecules per cell (a) The following equation applies for conversion of TGF-beta dose in molecules per cell TGF_beta_dose_mol_per_cell = initial TGF_beta_ex*1e-9*Vmed*6e23 (b) for standard experimental setup 1e6 cells in 2 mL medium 0.001 nM initial TGF_beta_ex is approximately equal to the dose of 1200 TGF-beta molecules/cell 0.050 nM initial TGF_beta_ex is approximately equal to the dose of 60000 TGF-beta molecules/cell (c) For 1e6 cells in 10 mL medium, please change the initial compartment size of Vmed and the corresponding assignment rule for Vmed. initial Vmed = 1e-8 (1e6 cells in 10 mL medium) Vmed = 0.010/(1e6*exp(log(1.45)*time/1440)) (1e6 cells in 10 mL medium) 3. Please note that this model contains events and the medium compartment size is varied. 4. For the model simulation in SBML-SAT, please remove initialAssignments and save it as SBML Level 2 Verion 1 file.