Raia2011 - IL13 L1236

  public model
Model Identifier
BIOMD0000000314
Short 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];
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
Submitter of the first revision: Marcel Schilling
Submitter of this revision: Marcel Schilling
Modellers: Marcel Schilling

Metadata information

is
BioModels Database MODEL1102020002
BioModels Database BIOMD0000000314
isDescribedBy
PubMed 21127196
hasTaxon
Taxonomy Homo sapiens
isVersionOf
Gene Ontology JAK-STAT cascade
hasProperty
Human Disease Ontology Hodgkin's lymphoma
occursIn
Brenda Tissue Ontology Hodgkin lymphoma cell line

Curation status
Curated

Tags

Connected external resources

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Name Description Size Actions

Model files

BIOMD0000000314_url.xml SBML L2V4 representation of Raia2011 - IL13 L1236 37.33 KB Preview | Download

Additional files

BIOMD0000000314-biopax2.owl Auto-generated BioPAX (Level 2) 24.02 KB Preview | Download
BIOMD0000000314-biopax3.owl Auto-generated BioPAX (Level 3) 34.17 KB Preview | Download
BIOMD0000000314.m Auto-generated Octave file 6.64 KB Preview | Download
BIOMD0000000314.pdf Auto-generated PDF file 209.41 KB Preview | Download
BIOMD0000000314.png Auto-generated Reaction graph (PNG) 80.09 KB Preview | Download
BIOMD0000000314.sci Auto-generated Scilab file 165.00 Bytes Preview | Download
BIOMD0000000314.svg Auto-generated Reaction graph (SVG) 29.88 KB Preview | Download
BIOMD0000000314.vcml Auto-generated VCML file 55.78 KB Preview | Download
BIOMD0000000314.xpp Auto-generated XPP file 4.32 KB Preview | Download
BIOMD0000000314_urn.xml Auto-generated SBML file with URNs 36.82 KB Preview | Download

  • Model originally submitted by : Marcel Schilling
  • Submitted: Feb 2, 2011 9:47:17 AM
  • Last Modified: May 18, 2017 12:31:04 PM
Revisions
  • Version: 2 public model Download this version
    • Submitted on: May 18, 2017 12:31:04 PM
    • Submitted by: Marcel Schilling
    • With comment: Current version of Raia2011 - IL13 L1236
  • Version: 1 public model Download this version
    • Submitted on: Feb 2, 2011 9:47:17 AM
    • Submitted by: Marcel Schilling
    • With comment: Original import of Raia2010_IL13_L1236

(*) 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
=> CD274mRNA; pSTAT5 pSTAT5*CD274mRNA_production*cell CD274mRNA_production = 0.0115928
Rec => IL13_Rec; IL13 Kon_IL13Rec*IL13*Rec*cell Kon_IL13Rec = 0.00174087
JAK2 => pJAK2; p_IL13_Rec JAK2_phosphorylation*JAK2*p_IL13_Rec*cell JAK2_phosphorylation = 0.300019
p_IL13_Rec => p_IL13_Rec_i pRec_intern*p_IL13_Rec*cell pRec_intern = 0.324132
Rec => Rec_i Rec_intern*Rec*cell Rec_intern = 0.259686
pJAK2 => JAK2; SHP1 pJAK2_dephosphorylation*pJAK2*SHP1*cell pJAK2_dephosphorylation = 0.0981611
IL13_Rec => p_IL13_Rec; pJAK2 Rec_phosphorylation*IL13_Rec*pJAK2*cell Rec_phosphorylation = 9.07541
Curator's comment:
(added: 14 Feb 2011, 03:56:30, updated: 14 Feb 2011, 03:56:30)
Time course simulations with varying IL13 stimulation as in figure S27A of the supplement of the original publication. Integration was performed using SBML ODESolver.