Chen2009 - ErbB Signaling

  public model
Model Identifier
BIOMD0000000255
Short description

This is A431 IERMv1.0 model described in the article
Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data.
William W Chen, Birgit Schoeberl, Paul J Jasper, Mario Niepel, Ulrik B Nielsen, Douglas A Lauffenburger and Peter K Sorger. Molecular Systems Biology 2009; 5:239. PMID: 19156131 , DOI: 10.1038/msb.2008.74

Abstract:
The ErbB signaling pathways, which regulate diverse physiological responses such as cell survival, proliferation and motility, have been subjected to extensive molecular analysis. Nonetheless, it remains poorly understood how different ligands induce different responses and how this is affected by oncogenic mutations. To quantify signal flow through ErbB-activated pathways we have constructed, trained and analyzed a mass action model of immediate-early signaling involving ErbB1-4 receptors (EGFR, HER2/Neu2, ErbB3 and ErbB4), and the MAPK and PI3K/Akt cascades. We find that parameter sensitivity is strongly dependent on the feature (e.g. ERK or Akt activation) or condition (e.g. EGF or heregulin stimulation) under examination and that this context dependence is informative with respect to mechanisms of signal propagation. Modeling predicts log-linear amplification so that significant ERK and Akt activation is observed at ligand concentrations far below the K(d) for receptor binding. However, MAPK and Akt modules isolated from the ErbB model continue to exhibit switch-like responses. Thus, key system-wide features of ErbB signaling arise from nonlinear interaction among signaling elements, the properties of which appear quite different in context and in isolation.

The sbml model is available as supplemental material to the article and at http://www.cdpcenter.org/resources/models/chen-et-al-2008/ . It was slightly changed to make it valid SBML and to incorporate the step functions, described in the readme file and needed for inhibitor preincubation. the equilibration processes end at 1800 sec, so to reproduce the dynamics shown in the publication and supplemental material, only the time points after 1800 need to be considered. The parameter set is the hand fitted one used for Sfigure 3 in the supplemental materials. All species are in molecules, apart from HRG, EGF and Inh, which are in M.

The results shown in SFigure 3 can be calculated dividing the parameters ERK_PP , AKT_PP and ERB_B1_P_tot by ERK_t , AKT_t and EGFR_t , respectively. Somehow we did not find the right scaleing factor for the phosphorylated ErbB1 receptor. Therefore the model does only qualitatively reproduces the timecourses shown in the first row of Sfigure 3.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov������re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

Format
SBML (L2V3)
Related Publication
  • Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data.
  • Chen WW, Schoeberl B, Jasper PJ, Niepel M, Nielsen UB, Lauffenburger DA, Sorger PK
  • Molecular Systems Biology , 0/ 2009 , Volume 5 , pages: 239 , PubMed ID: 19156131
  • Department of Systems Biology, Center for Cell Decision Processes, Harvard Medical School, Boston, MA 02115, USA.
  • The ErbB signaling pathways, which regulate diverse physiological responses such as cell survival, proliferation and motility, have been subjected to extensive molecular analysis. Nonetheless, it remains poorly understood how different ligands induce different responses and how this is affected by oncogenic mutations. To quantify signal flow through ErbB-activated pathways we have constructed, trained and analyzed a mass action model of immediate-early signaling involving ErbB1-4 receptors (EGFR, HER2/Neu2, ErbB3 and ErbB4), and the MAPK and PI3K/Akt cascades. We find that parameter sensitivity is strongly dependent on the feature (e.g. ERK or Akt activation) or condition (e.g. EGF or heregulin stimulation) under examination and that this context dependence is informative with respect to mechanisms of signal propagation. Modeling predicts log-linear amplification so that significant ERK and Akt activation is observed at ligand concentrations far below the K(d) for receptor binding. However, MAPK and Akt modules isolated from the ErbB model continue to exhibit switch-like responses. Thus, key system-wide features of ErbB signaling arise from nonlinear interaction among signaling elements, the properties of which appear quite different in context and in isolation.
Contributors
Lukas Endler

Metadata information

is
BioModels Database MODEL1007260001
BioModels Database BIOMD0000000255
isDerivedFrom
BioModels Database BIOMD0000000019
isDescribedBy
PubMed 19156131
hasTaxon
Taxonomy Homo sapiens
hasVersion
Gene Ontology MAPK cascade
Gene Ontology Ras protein signal transduction
occursIn
Brenda Tissue Ontology HeLa cell

Curation status
Curated

Original model(s)
http://www.cdpcenter.org/wordpress/wp-content/uploads/2009/01/ErbB-Chen_et_al_2008-A431.xml

Tags
Name Description Size Actions

Model files

BIOMD0000000255_url.xml SBML L2V3 representation of Chen2009 - ErbB Signaling 1.07 MB Preview | Download

Additional files

BIOMD0000000255.xpp Auto-generated XPP file 223.40 KB Preview | Download
BIOMD0000000255.sci Auto-generated Scilab file 181.00 bytes Preview | Download
BIOMD0000000255.m Auto-generated Octave file 279.84 KB Preview | Download
BIOMD0000000255.svg Auto-generated Reaction graph (SVG) 861.00 bytes Preview | Download
BIOMD0000000255.pdf Auto-generated PDF file 3.75 MB Preview | Download
BIOMD0000000255.png Auto-generated Reaction graph (PNG) 5.07 KB Preview | Download
BIOMD0000000255-biopax2.owl Auto-generated BioPAX (Level 2) 1.01 MB Preview | Download
BIOMD0000000255-biopax3.owl Auto-generated BioPAX (Level 3) 1.93 MB Preview | Download
BIOMD0000000255.vcml Auto-generated VCML file 923.00 bytes Preview | Download
BIOMD0000000255_urn.xml Auto-generated SBML file with URNs 1.07 MB Preview | Download

  • Model originally submitted by : Lukas Endler
  • Submitted: 26-Jul-2010 18:32:01
  • Last Modified: 08-Apr-2016 17:15:31
Revisions
  • Version: 2 public model Download this version
    • Submitted on: 08-Apr-2016 17:15:31
    • Submitted by: Lukas Endler
    • With comment: Current version of Chen2009 - ErbB Signaling
  • Version: 1 public model Download this version
    • Submitted on: 26-Jul-2010 18:32:01
    • Submitted by: Lukas Endler
    • With comment: Original import of Chen2009_ErbB_Signaling
Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
ATP 1.2e9 1.2E9 item
(EGF:ErbB1:ErbB3):ATP

Pro-epidermal growth factor ; Epidermal growth factor receptor
0.0 item
2(ErbB2) P:GAP:(Shc P):Grb2:Sos:(Ras:GTP) 0.0 item
2(ErbB2) P:GAP:(Shc P):Grb2:Sos:(Ras:GTP):cPP 0.0 item
2(ErbB2) P:GAP:Grb2 0.0 item
2(ErbB2) P:GAP:Grb2:cPP 0.0 item
Reactions
Reactions Rate Parameters
(2(EGF:ErbB1)_P:GAP:Grb2:(Gab1_P#) + ATP 1.2e9) => (2(EGF:ErbB1)_P:GAP:Grb2:Gab1:ATP)

([2(EGF:ErbB1)_P:GAP:Grb2:(Gab1_P#)] + [ATP 1.2e9]) => ([2(EGF:ErbB1)_P:GAP:Grb2:Gab1:ATP])
k123*c486*c105-kd123*c136

k123*[2(EGF:ErbB1)_P:GAP:Grb2:(Gab1_P#)]*[ATP 1.2e9]-kd123*[2(EGF:ErbB1)_P:GAP:Grb2:Gab1:ATP]
kd123 = 0.177828; k123 = 0.0
((ErbB1:ErbB4)_P + ATP 1.2e9) => ((HRG:ErbB4:ErbB1):ATP)

([(ErbB1:ErbB4)_P] + [ATP 1.2e9]) => ([(HRG:ErbB4:ErbB1):ATP])
k123*c150*c105-kd123*c138

k123*[(ErbB1:ErbB4)_P]*[ATP 1.2e9]-kd123*[(HRG:ErbB4:ErbB1):ATP]
kd123 = 0.177828; k123 = 0.0
((ErbB4:ErbB2)_P + ATP 1.2e9) => ((HRG:ErbB4):ErbB2:ATP)

([Receptor tyrosine-protein kinase erbB-4; Receptor tyrosine-protein kinase erbB-2] + [ATP 1.2e9]) => ([(HRG:ErbB4):ErbB2:ATP])
k123*c336*c105-kd123*c139

k123*[Receptor tyrosine-protein kinase erbB-4; Receptor tyrosine-protein kinase erbB-2]*[ATP 1.2e9]-kd123*[(HRG:ErbB4):ErbB2:ATP]
kd123 = 0.177828; k123 = 0.0
((ErbB3:ErbB2)_P + ATP 1.2e9) => (((HRG:ErbB3):ErbB2):ATP)

([(ErbB3:ErbB2)_P] + [ATP 1.2e9]) => ([((HRG:ErbB3):ErbB2):ATP])
k123*c337*c105-kd123*c169

k123*[(ErbB3:ErbB2)_P]*[ATP 1.2e9]-kd123*[((HRG:ErbB3):ErbB2):ATP]
kd123 = 0.177828; k123 = 0.0
(ErbB1_h + ATP 1.2e9) => (ErbB1_h:ATP)

([ErbB1_h] + [ATP 1.2e9]) => ([ErbB1_h:ATP])
k122*c532*c105-kd122*c524

k122*[ErbB1_h]*[ATP 1.2e9]-kd122*[ErbB1_h:ATP]
kd122 = 1.0; k122 = 1.8704E-8
((EGF:ErbB1:Inh::EGF:ErbB1_h:ATP) + ATP 1.2e9) => ((EGF:ErbB1:Inh::EGF:ErbB1_h:ATP)-HalfActive)

([(EGF:ErbB1:Inh::EGF:ErbB1_h:ATP)] + [ATP 1.2e9]) => ([(EGF:ErbB1:Inh::EGF:ErbB1_h:ATP)-HalfActive])
k122*c551*c105-kd122*c556

k122*[(EGF:ErbB1:Inh::EGF:ErbB1_h:ATP)]*[ATP 1.2e9]-kd122*[(EGF:ErbB1:Inh::EGF:ErbB1_h:ATP)-HalfActive]
kd122 = 1.0; k122 = 1.8704E-8
(2(EGF:ErbB1)_P + ATP 1.2e9) => ((EGF:ErbB1:Inh::EGF:ErbB1_h:ATP)-HalfActive)

([2(EGF:ErbB1)_P] + [ATP 1.2e9]) => ([(EGF:ErbB1:Inh::EGF:ErbB1_h:ATP)-HalfActive])
k123h*c5*c105-kd123h*c556

k123h*[2(EGF:ErbB1)_P]*[ATP 1.2e9]-kd123h*[(EGF:ErbB1:Inh::EGF:ErbB1_h:ATP)-HalfActive]
kd123h = 0.1; k123h = 0.0
(2(EGF:ErbB1)_P + ATP 1.2e9) => (2(EGF:ErbB1_h:ATP)-FullActive)

([2(EGF:ErbB1)_P] + [ATP 1.2e9]) => ([2(EGF:ErbB1_h:ATP)-FullActive])
k123*c5*c105-kd123*c557

k123*[2(EGF:ErbB1)_P]*[ATP 1.2e9]-kd123*[2(EGF:ErbB1_h:ATP)-FullActive]
kd123 = 0.177828; k123 = 0.0
(2(EGF:ErbB1)_P + ATP 1.2e9) => ((EGF:ErbB1:ATP::EGF:ErbB1_h:Inh)-HalfActive)

([2(EGF:ErbB1)_P] + [ATP 1.2e9]) => ([(EGF:ErbB1:ATP::EGF:ErbB1_h:Inh)-HalfActive])
k123h*c5*c105-kd123h*c558

k123h*[2(EGF:ErbB1)_P]*[ATP 1.2e9]-kd123h*[(EGF:ErbB1:ATP::EGF:ErbB1_h:Inh)-HalfActive]
kd123h = 0.1; k123h = 0.0
((EGF:ErbB1:ErbB3) + ATP 1.2e9) => ((EGF:ErbB1:ErbB3):ATP)

([Pro-epidermal growth factor; Epidermal growth factor receptor] + [ATP 1.2e9]) => ([Pro-epidermal growth factor; Epidermal growth factor receptor])
k122*c160*c105-kd122*c124

k122*[Pro-epidermal growth factor; Epidermal growth factor receptor]*[ATP 1.2e9]-kd122*[Pro-epidermal growth factor; Epidermal growth factor receptor]
kd122 = 1.0; k122 = 1.8704E-8
(Ras_activated:GTP + 2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos) => (2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos:(Ras:GTP))

([Ras_activated:GTP] + [2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos]) => ([2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos:(Ras:GTP)])
k20*c43*c303-kd20*c309

k20*[Ras_activated:GTP]*[2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos]-kd20*[2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos:(Ras:GTP)]
k20 = 1.1068E-5; kd20 = 0.4
(cPP + 2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos:(Ras:GTP)) => (2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos:(Ras:GTP):cPP)

([cPP] + [2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos:(Ras:GTP)]) => ([2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos:(Ras:GTP):cPP])
k5*c9*c311-kd5b*c310

k5*[cPP]*[2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos:(Ras:GTP)]-kd5b*[2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos:(Ras:GTP):cPP]
k5 = 0.0; kd5b = 0.0080833
(2(ErbB2)_P:GAP:Grb2 + cPP) => (2(ErbB2)_P:GAP:Grb2:cPP)

([2(ErbB2)_P:GAP:Grb2] + [cPP]) => ([2(ErbB2)_P:GAP:Grb2:cPP])
k4b*c312*c12-kd4*c313

k4b*[2(ErbB2)_P:GAP:Grb2]*[cPP]-kd4*[2(ErbB2)_P:GAP:Grb2:cPP]
kd4 = 1.66E-4; k4b = 0.0
(Sos + 2(ErbB2)_P:GAP:Grb2) => (2(ErbB2)_P:GAP:Grb2:Sos)

([Son of sevenless homolog 1; 182530] + [2(ErbB2)_P:GAP:Grb2]) => ([2(ErbB2)_P:GAP:Grb2:Sos])
k17*c24*c312-kd17*c315

k17*[Son of sevenless homolog 1; 182530]*[2(ErbB2)_P:GAP:Grb2]-kd17*[2(ErbB2)_P:GAP:Grb2:Sos]
k17 = 1.67E-5; kd17 = 0.06
Curator's comment:
(added: 26 Jul 2010, 18:38:28, updated: 26 Jul 2010, 18:38:28)
Reproduction of the figure S3 from the supplemental material of the original article. The time courses of ErbB1_P are only qualitatively correct, as the scaling factors were not known. On the lower time axis the model time including the equilibration phase (8000s) is shown, on the upper one the time after stimulation in minutes. The time-courses were simulated using sbml odesolver.