public model
Model Identifier
BIOMD0000000313
Short description

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.

Format
SBML (L2V4)
Related Publication
  • 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, Klingmüller U
  • Cancer research , 2/ 2011 , Volume 71 , pages: 693-704 , PubMed ID: 21127196
  • Division of Systems Biology of Signal Transduction, DKFZ-ZMBH Alliance and Molecular Genetics, German Cancer Research Center, Heidelberg, Germany.
  • 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.
Contributors
Marcel Schilling

Metadata information

is
BioModels Database MODEL1102020001
BioModels Database BIOMD0000000313
isDescribedBy
PubMed 21127196
hasTaxon
Taxonomy Homo sapiens
isVersionOf
hasProperty
Human Disease Ontology mature T-cell and NK-cell lymphoma
occursIn
Brenda Tissue Ontology B-lymphoma cell line

Curation status
Curated

Tags
Name Description Size Actions

Model files

BIOMD0000000313_url.xml SBML L2V4 representation of Raia2010 - IL13 Signalling MedB1 48.31 KB Preview | Download

Additional files

BIOMD0000000313.xpp Auto-generated XPP file 5.81 KB Preview | Download
BIOMD0000000313.m Auto-generated Octave file 8.43 KB Preview | Download
BIOMD0000000313-biopax3.owl Auto-generated BioPAX (Level 3) 40.77 KB Preview | Download
BIOMD0000000313.vcml Auto-generated VCML file 69.85 KB Preview | Download
BIOMD0000000313.sci Auto-generated Scilab file 165.00 Bytes Preview | Download
BIOMD0000000313.pdf Auto-generated PDF file 231.72 KB Preview | Download
BIOMD0000000313.png Auto-generated Reaction graph (PNG) 133.00 KB Preview | Download
BIOMD0000000313-biopax2.owl Auto-generated BioPAX (Level 2) 27.96 KB Preview | Download
BIOMD0000000313_urn.xml Auto-generated SBML file with URNs 47.75 KB Preview | Download
BIOMD0000000313.svg Auto-generated Reaction graph (SVG) 40.54 KB Preview | Download

  • Model originally submitted by : Marcel Schilling
  • Submitted: 02-Feb-2011 09:45:15
  • Last Modified: 18-May-2017 12:22:27
Revisions
  • Version: 2 public model Download this version
    • Submitted on: 18-May-2017 12:22:27
    • Submitted by: Marcel Schilling
    • With comment: Current version of Raia2010 - IL13 Signalling MedB1
  • Version: 1 public model Download this version
    • Submitted on: 02-Feb-2011 09:45:15
    • Submitted by: Marcel Schilling
    • With comment: Original import of Raia2010_IL13_MedB1

(*) You might be seeing discontinuous revisions as only public revisions are displayed here. Any private revisions unpublished model revision of this model will only be shown to the submitter and their collaborators.

Legends
: Variable used inside SBML models


Species
Reactions
Reactions Rate Parameters
Rec => IL13_Rec; IL13 Kon_IL13Rec*IL13*Rec*cell Kon_IL13Rec = 0.00341992
IL13_Rec => p_IL13_Rec; pJAK2 Rec_phosphorylation*IL13_Rec*pJAK2*cell Rec_phosphorylation = 999.631
pJAK2 => JAK2; SHP1 pJAK2_dephosphorylation*pJAK2*SHP1*cell pJAK2_dephosphorylation = 6.21906E-4
JAK2 => pJAK2; IL13_Rec, SOCS3 JAK2_phosphorylation*IL13_Rec*JAK2/(1+JAK2_p_inhibition*SOCS3)*cell JAK2_phosphorylation = 0.157057; JAK2_p_inhibition = 0.0168268
Rec => Rec_i Rec_intern*Rec*cell Rec_intern = 0.103346
Rec_i => Rec Rec_recycle*Rec_i*cell Rec_recycle = 0.00135598
p_IL13_Rec => p_IL13_Rec_i pRec_intern*p_IL13_Rec*cell pRec_intern = 0.15254
p_IL13_Rec_i => pRec_degradation*p_IL13_Rec_i*cell pRec_degradation = 0.172928
JAK2 => pJAK2; p_IL13_Rec, SOCS3 JAK2_phosphorylation*p_IL13_Rec*JAK2/(1+JAK2_p_inhibition*SOCS3)*cell JAK2_phosphorylation = 0.157057; JAK2_p_inhibition = 0.0168268
Curator's comment:
(added: 14 Feb 2011, 03:33:19, updated: 14 Feb 2011, 03:33:19)
Time course simulations with varying IL13 stimulation as in figure 5B of the original publication. Integration was performed using SBML ODESolver.