Schoeberl2002 - EGF MAPK

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Model Identifier
BIOMD0000000019
Short description
Schoeberl2002 - EGF MAPK

Computational model that offers an integrated quantitative, dynamic, and topological representation of intracellular signal networks, based on known components of epidermal growth factor (EGF) receptor signal pathways.

The initial model was constructed by Ken Lau from the MATLAB source code.

This model is described in the article:

Schoeberl B, Eichler-Jonsson C, Gilles ED, Müller G
Nat. Biotechnol. 2002 Apr; 20(4): 370-375

Abstract:

We present a computational model that offers an integrated quantitative, dynamic, and topological representation of intracellular signal networks, based on known components of epidermal growth factor (EGF) receptor signal pathways. The model provides insight into signal-response relationships between the binding of EGF to its receptor at the cell surface and the activation of downstream proteins in the signaling cascade. It shows that EGF-induced responses are remarkably stable over a 100-fold range of ligand concentration and that the critical parameter in determining signal efficacy is the initial velocity of receptor activation. The predictions of the model agree well with experimental analysis of the effect of EGF on two downstream responses, phosphorylation of ERK-1/2 and expression of the target gene, c-fos.

This model does not exactly reproduce the results given in the original publication. It has, though, the same reaction graph and gives very similar time courses for the conditions depicted in the article.

Several corrections were applied to the parameters described in the paper's supplementary materials. Some parameter names were replaced by the corresponding identical ones: k(r)26 by k(r)18, k(r)27 by k(r)19, k(r)30 by k(r)20, k(r)38 by k(r)24, k(r)39 by k(r)37, k(r)46 by k(r)44, k51 by k49, k(r)54 by k(r)52 and k62 by k62. In particular the parameter values described in the column "remark" of supplementary table 1 override the values explicitely written in the numerical columns:

name in suppl. value used in model value used remarks
kr16 0.055 0.275
k30 7.9e6 2.1e6 as k20
kr30 0.3 0.4 as kr24
k38 3e7 1e7 as k20
kr38 0.055 0.55 as kr24
k52 1.1e5 5.34e7

k5 was used for v116, v119, v122 and v125 in addition of v107, v110 and v113 as listed in the legend of supplementary figure 2. k5 is calculated using th eformula from the matlab file not given in the supplements.

All rate constants were rescaled to minutes (k[min] = 60*k[sec]) and all second order rate constants additionally to molecules/cell with a cell volume of 1 picolitre (k[molecs/cell] = k[M]/(Vc*Na), with Vc=1e-12 l and Na = 6e23).

The association constant of internalized EGF was rescaled to molecules/endosome using an endosomal volume of 4.3 al (= 4.3*10 -18 litre).

The extracellular EGF concentration was converted to molecules per picolitre with a MW of 6045 Da.

[ng/ml] [numb/pl]
50 4962
0.5 49.6
0.125 12.4

With the initial conditions given in the paper, the results could not be reproduced at all. Therefore the initial conditions used in the MATLAB file were adopted for SHC (1.01 * 10 5 instead of 1.01 * 10 6 ) and Ras_GDP. (7.2 * 10 4 instead of 1.14 * 10 7 )

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.

Format
SBML (L2V4)
Related Publication
  • Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors.
  • Schoeberl B, Eichler-Jonsson C, Gilles ED, Müller G
  • Nature biotechnology , 4/ 2002 , Volume 20 , pages: 370-375 , PubMed ID: 11923843
  • Max Planck Institute for Dynamics of Complex Technical Systems, Leipziger Str. 44, D-39120 Magdeburg, Germany.
  • We present a computational model that offers an integrated quantitative, dynamic, and topological representation of intracellular signal networks, based on known components of epidermal growth factor (EGF) receptor signal pathways. The model provides insight into signal-response relationships between the binding of EGF to its receptor at the cell surface and the activation of downstream proteins in the signaling cascade. It shows that EGF-induced responses are remarkably stable over a 100-fold range of ligand concentration and that the critical parameter in determining signal efficacy is the initial velocity of receptor activation. The predictions of the model agree well with experimental analysis of the effect of EGF on two downstream responses, phosphorylation of ERK-1/2 and expression of the target gene, c-fos.
Contributors
Submitter of the first revision: Nicolas Le Novère
Submitter of this revision: Nicolas Le Novère
Modellers: Nicolas Le Novère

Metadata information

is (2 statements)
BioModels Database MODEL6617455076
BioModels Database BIOMD0000000019

isDescribedBy (1 statement)
PubMed 11923843

hasTaxon (1 statement)
Taxonomy Homo sapiens

isVersionOf (2 statements)
hasVersion (2 statements)
Gene Ontology Ras protein signal transduction
Gene Ontology MAPK cascade


Curation status
Curated

Tags

Connected external resources

SBGN view in Newt Editor

Name Description Size Actions

Model files

BIOMD0000000019_url.xml SBML L2V4 representation of Schoeberl2002 - EGF MAPK 149.81 KB Preview | Download

Additional files

BIOMD0000000019-biopax2.owl Auto-generated BioPAX (Level 2) 166.34 KB Preview | Download
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BIOMD0000000019.m Auto-generated Octave file 37.30 KB Preview | Download
BIOMD0000000019.pdf Auto-generated PDF file 686.92 KB Preview | Download
BIOMD0000000019.png Auto-generated Reaction graph (PNG) 2.84 MB Preview | Download
BIOMD0000000019.sci Auto-generated Scilab file 185.00 Bytes Preview | Download
BIOMD0000000019.svg Auto-generated Reaction graph (SVG) 287.94 KB Preview | Download
BIOMD0000000019.vcml Auto-generated VCML file 897.00 Bytes Preview | Download
BIOMD0000000019.xpp Auto-generated XPP file 28.05 KB Preview | Download
BIOMD0000000019_urn.xml Auto-generated SBML file with URNs 148.65 KB Preview | Download

  • Model originally submitted by : Nicolas Le Novère
  • Submitted: Sep 13, 2005 2:19:14 PM
  • Last Modified: Apr 8, 2016 3:31:50 PM
Revisions
  • Version: 2 public model Download this version
    • Submitted on: Apr 8, 2016 3:31:50 PM
    • Submitted by: Nicolas Le Novère
    • With comment: Current version of Schoeberl2002 - EGF MAPK
  • Version: 1 public model Download this version
    • Submitted on: Sep 13, 2005 2:19:14 PM
    • Submitted by: Nicolas Le Novère
    • With comment: Original import of BIOMD0000000019.xml.origin

(*) 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
Species Initial Concentration/Amount
x12 81000.0 item
x23 0.0 item
x25 0.0 item
x26

GDP ; GTPase HRas
72000.0 item
x35 0.0 item
x41

RAF proto-oncogene serine/threonine-protein kinase ; 164760
40000.0 item
Reactions
Reactions Rate Parameters
x23 + x12 => x7 k4*x23*x12-kr4*x7 kr4 = 0.0996 permin; k4 = 1.038E-5 peritempermin
x9 => x12 k15*x9 k15 = 600000.0 permin
x23 => x18 k7*x23 k7 = 0.003 permin
x24 + x23 => x25 k17*x24*x23-kr17*x25 k17 = 0.001 peritempermin; kr17 = 3.6 permin
x26 + x35 => x36 k18*x26*x35-kr18*x36 k18 = 0.0015 peritempermin; kr18 = 78.0 permin
x26 + x19 => x20 k18*x26*x19-kr18*x20 k18 = 0.0015 peritempermin; kr18 = 78.0 permin
x35 + x43 => x37 k20*x35*x43-kr20*x37 k20 = 2.1E-4 peritempermin; kr20 = 24.0 permin
x35 + x12 => x92 k4*x35*x12-kr4*x92 kr4 = 0.0996 permin; k4 = 1.038E-5 peritempermin
x28 + x41 => x42 k28*x28*x41-kr28*x42 kr28 = 0.318 permin; k28 = 1.0E-4 peritempermin
x69 + x41 => x70 k28*x69*x41-kr28*x70 kr28 = 0.318 permin; k28 = 1.0E-4 peritempermin
Curator's comment:
(added: 14 Jul 2008, 18:05:03, updated: 14 Jul 2008, 18:05:03)
time course of active species as in fig. 2 c,d,e,f in the original publication