Hornberg2005 - MAPKsignalling

This model is described in the article:
Abstract:
Oncogenesis results from changes in kinetics or in abundance of proteins in signal transduction networks. Recently, it was shown that control of signalling cannot reside in a single gene product, and might well be dispersed over many components. Which of the reactions in these complex networks are most important, and how can the existing molecular information be used to understand why particular genes are oncogenes whereas others are not? We implement a new method to help address such questions. We apply control analysis to a detailed kinetic model of the epidermal growth factor-induced mitogen-activated protein kinase network. We determine the control of each reaction with respect to three biologically relevant characteristics of the output of this network: the amplitude, duration and integrated output of the transient phosphorylation of extracellular signal-regulated kinase (ERK). We confirm that control is distributed, but far from randomly: a small proportion of reactions substantially control signalling. In particular, the activity of Raf is in control of all characteristics of the transient profile of ERK phosphorylation, which may clarify why Raf is an oncogene. Most reactions that really matter for one signalling characteristic are also important for the other characteristics. Our analysis also predicts the effects of mutations and changes in gene expression.
This model is hosted on BioModels Database and identified by: BIOMD0000000667.
To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.
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Control of MAPK signalling: from complexity to what really matters.
- Hornberg JJ, Binder B, Bruggeman FJ, Schoeberl B, Heinrich R, Westerhoff HV
- Oncogene , 8/ 2005 , Volume 24 , Issue 36 , pages: 5533-5542 , PubMed ID: 16007170
- Department of Molecular Cell Physiology, Institute of Molecular Cell Biology, Faculty of Earth and Life Sciences, Vrije Universiteit, Amsterdam, The Netherlands.
- Oncogenesis results from changes in kinetics or in abundance of proteins in signal transduction networks. Recently, it was shown that control of signalling cannot reside in a single gene product, and might well be dispersed over many components. Which of the reactions in these complex networks are most important, and how can the existing molecular information be used to understand why particular genes are oncogenes whereas others are not? We implement a new method to help address such questions. We apply control analysis to a detailed kinetic model of the epidermal growth factor-induced mitogen-activated protein kinase network. We determine the control of each reaction with respect to three biologically relevant characteristics of the output of this network: the amplitude, duration and integrated output of the transient phosphorylation of extracellular signal-regulated kinase (ERK). We confirm that control is distributed, but far from randomly: a small proportion of reactions substantially control signalling. In particular, the activity of Raf is in control of all characteristics of the transient profile of ERK phosphorylation, which may clarify why Raf is an oncogene. Most reactions that really matter for one signalling characteristic are also important for the other characteristics. Our analysis also predicts the effects of mutations and changes in gene expression.
Submitter of this revision: administrator
Modellers: administrator, Vijayalakshmi Chelliah
Metadata information
isDescribedBy (2 statements)
isVersionOf (1 statement)
Connected external resources
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Model files |
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BIOMD0000000667_url.xml | SBML L2V4 representation of Hornberg2005 - MAPKsignalling | 573.99 KB | Preview | Download |
Additional files |
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BIOMD0000000667-biopax2.owl | Auto-generated BioPAX (Level 2) | 232.25 KB | Preview | Download |
BIOMD0000000667-biopax3.owl | Auto-generated BioPAX (Level 3) | 410.94 KB | Preview | Download |
BIOMD0000000667.m | Auto-generated Octave file | 48.06 KB | Preview | Download |
BIOMD0000000667.pdf | Auto-generated PDF file | 545.95 KB | Preview | Download |
BIOMD0000000667.png | Auto-generated Reaction graph (PNG) | 2.09 MB | Preview | Download |
BIOMD0000000667.sci | Auto-generated Scilab file | 47.07 KB | Preview | Download |
BIOMD0000000667.svg | Auto-generated Reaction graph (SVG) | 325.55 KB | Preview | Download |
BIOMD0000000667.xpp | Auto-generated XPP file | 38.51 KB | Preview | Download |
BIOMD0000000667_urn.xml | Auto-generated SBML file with URNs | 573.50 KB | Preview | Download |
MODEL0848279215.cps | COPASI file with annotations and plot to produce figure 2 that shows ERK-PP concentration over time (seconds) | 600.71 KB | Preview | Download |
MODEL0848279215.sedml | SED-ML file to produce figure 2. Model time units are seconds and not minutes. Y-axis: ERK_PP. | 1.67 KB | Preview | Download |
- Model originally submitted by : Vijayalakshmi Chelliah
- Submitted: Apr 28, 2009 1:25:23 PM
- Last Modified: Feb 1, 2018 9:57:43 AM
Revisions
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Version: 3
- Submitted on: Feb 1, 2018 9:57:43 AM
- Submitted by: administrator
- With comment: Notes updated using online editor.
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Version: 2
- Submitted on: Apr 28, 2009 1:25:23 PM
- Submitted by: Vijayalakshmi Chelliah
- With comment: Current version of Hornberg2005_MAPKsignalling
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Version: 1
- Submitted on: Apr 28, 2009 1:25:23 PM
- Submitted by: Vijayalakshmi Chelliah
- With comment: Original import of Hornberg2005_MAPKsignalling
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: Variable used inside SBML models
Species | Initial Concentration/Amount |
---|---|
MEK P Raf RAF proto-oncogene serine/threonine-protein kinase ; Dual specificity mitogen-activated protein kinase kinase 1 ; Phosphoprotein |
0.0 mol |
EGF EGFRi 2 GAP SHC 0 Shc-EGFR complex |
0.0 mol |
EGF EGFRi 2deg Epidermal growth factor receptor ; Pro-epidermal growth factor |
0.0 mol |
Sos ERKi PP Son of sevenless homolog 1 ; Mitogen-activated protein kinase 1 ; Phosphoprotein |
0.0 mol |
EGFR Epidermal growth factor receptor |
49999.9999999991 mol |
EGF EGFRi 2 Pro-epidermal growth factor ; Epidermal growth factor receptor |
0.0 mol |
Proti Interleukin-4 receptor subunit alpha |
0.0 mol |
Prot Interleukin-4 receptor subunit alpha |
80999.9999999976 mol |
Reactions | Rate | Parameters |
---|---|---|
MEK_P + Raf_0 => MEK_P_Raf | Compartment*(k44*MEK_P*Raf_0-kd52*MEK_P_Raf) | k44 = 1.95E-5; kd52 = 0.033 |
_EGF_EGFRi__2_GAP + Shc_0 => _EGF_EGFRi__2_GAP_SHC_0 | Compartment*(k37*_EGF_EGFRi__2_GAP*Shc_0-kd37*_EGF_EGFRi__2_GAP_SHC_0) | k37 = 1.5E-6; kd37 = 0.3 |
_EGF_EGFRi__2_GAP_SHC__Grb2 => _EGF_EGFRi___2deg | Compartment*k60*_EGF_EGFRi__2_GAP_SHC__Grb2 | k60 = 0.0055 |
ERKi_PP + Sos => Sos_ERKi_PP | Compartment*(k126*ERKi_PP*Sos-kd126*Sos_ERKi_PP) | k126 = 1.66E-7; kd126 = 2.0 |
EGFR => EGF_EGFR; EGF | Compartment*(k1*EGF*EGFR-kd1*EGF_EGFR) | kd1 = 0.00384; k1 = 3.0E7 |
_EGF_EGFRi_2 => _EGF_EGFRi__2 | Compartment*(k3*_EGF_EGFRi_2-kd3*_EGF_EGFRi__2) | k3 = 1.0; kd3 = 0.01 |
_EGF_EGFR__2_GAP_Grb2_Sos_Prot => Proti + _EGF_EGFRi__2_GAP_Grb2_Sos | Compartment*kd5*_EGF_EGFR__2_GAP_Grb2_Sos_Prot | kd5 = 0.0146 |
_EGF_EGFR__2_GAP_SHC__Grb2_Prot_0 => Proti + _EGF_EGFRi__2_GAP_SHC__Grb2 | Compartment*kd5*_EGF_EGFR__2_GAP_SHC__Grb2_Prot_0 | kd5 = 0.0146 |
_EGF_EGFR__2_GAP_SHC__Grb2 + Prot => _EGF_EGFR__2_GAP_SHC__Grb2_Prot_0 | Compartment*(k4*_EGF_EGFR__2_GAP_SHC__Grb2*Prot-kd4*_EGF_EGFR__2_GAP_SHC__Grb2_Prot_0) | kd4 = 0.00166; k4 = 1.73E-7 |
_EGF_EGFR__2_GAP_SHC__Grb2_Sos_Ras_GDP + Prot => _EGF_EGFR__2_GAP_SHC__Grb2_Sos_Ras_GDP_Prot_0 | Compartment*(k4*_EGF_EGFR__2_GAP_SHC__Grb2_Sos_Ras_GDP*Prot-kd4*_EGF_EGFR__2_GAP_SHC__Grb2_Sos_Ras_GDP_Prot_0) | kd4 = 0.00166; k4 = 1.73E-7 |
(added: 01 Feb 2018, 09:54:40, updated: 01 Feb 2018, 09:54:40)