Sivakumar2011 - EGF Receptor Signaling Pathway

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Model Identifier
BIOMD0000000394
Short description
Sivakumar2011 - EGF Receptor Signaling Pathway

EGFR belongs to the human epidermal receptor (HER) family of receptor tyrosine kinases, which consists of four closely related receptors (EGFR (HER1, erbB1), HER2 (neu, erbB2), HER3 (erbB3), and HER4 (erbB4)) that mediate cellular signaling pathways involved in growth and proliferation in response to the binding of a variety of growth factor ligands. There are currently six known endogenous ligands for EGFR: EGF, transforming growth factor- (TGF-), amphiregulin, betacellulin, heparin-binding EGF (HB-EGF), and epiregulin.Upon ligand binding, the EGFR forms homo- or heterodimeric complexes (usually with HER2), which leads to activation of the receptor tyrosine kinase, via autophosphorylation.

References:

This model is described in the article:

Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
Omics: a Journal of Integrative Biology. 2011; 15(10):729-737

Abstract:

The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

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 (L2V1)
Related Publication
  • A systems biology approach to model neural stem cell regulation by notch, shh, wnt, and EGF signaling pathways.
  • Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S
  • Omics : a journal of integrative biology , 10/ 2011 , Volume 15 , pages: 729-737 , PubMed ID: 21978399
  • Bioinformatics Facility, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India.
  • The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.
Contributors
KC Sivakumar

Metadata information

is
BioModels Database MODEL1101270003
BioModels Database BIOMD0000000394
isDescribedBy
PubMed 21978399
hasTaxon
Taxonomy Mammalia
isDerivedFrom
PANTHER Pathway P00018
occursIn
Cell Type Ontology neuronal stem cell

Curation status
Curated

Tags
Name Description Size Actions

Model files

BIOMD0000000394_url.xml SBML L2V1 representation of Sivakumar2011 - EGF Receptor Signaling Pathway 135.88 KB Preview | Download

Additional files

BIOMD0000000394.sci Auto-generated Scilab file 8.22 KB Preview | Download
BIOMD0000000394.vcml Auto-generated VCML file 69.30 KB Preview | Download
BIOMD0000000394-biopax2.owl Auto-generated BioPAX (Level 2) 21.07 KB Preview | Download
BIOMD0000000394.svg Auto-generated Reaction graph (SVG) 38.14 KB Preview | Download
BIOMD0000000394.m Auto-generated Octave file 10.35 KB Preview | Download
BIOMD0000000394.xpp Auto-generated XPP file 7.49 KB Preview | Download
BIOMD0000000394_urn.xml Auto-generated SBML file with URNs 135.27 KB Preview | Download
BIOMD0000000394.pdf Auto-generated PDF file 209.61 KB Preview | Download
BIOMD0000000394-biopax3.owl Auto-generated BioPAX (Level 3) 31.54 KB Preview | Download
BIOMD0000000394.png Auto-generated Reaction graph (PNG) 149.89 KB Preview | Download

  • Model originally submitted by : KC Sivakumar
  • Submitted: 27-Jan-2011 04:42:05
  • Last Modified: 08-Apr-2016 18:15:18
Revisions
  • Version: 2 public model Download this version
    • Submitted on: 08-Apr-2016 18:15:18
    • Submitted by: KC Sivakumar
    • With comment: Current version of Sivakumar2011 - EGF Receptor Signaling Pathway
  • Version: 1 public model Download this version
    • Submitted on: 27-Jan-2011 04:42:05
    • Submitted by: KC Sivakumar
    • With comment: Original import of BIOMD0000000394.xml.origin
Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
Ras

Ras-like protein 1
5.0 mol
GDP

GDP
0.5 mol
Akt

RAC-alpha serine/threonine-protein kinase
5.0 mol
MEK1 minus 2

Dual specificity mitogen-activated protein kinase kinase 1
0.0 mol
ERK1 minus 2

MAP kinase activity
0.0 mol
EGFR

Receptor protein-tyrosine kinase
0.0 mol
Complex br (EGFR/../ br GAP)

protein complex
0.0 mol
Complex(Grb2/../PLC)

protein complex
5.0 mol
RKIP

Phosphatidylethanolamine-binding protein 1
0.0 mol
Mitogenesis br Differentiation 1.0 mol
Raf minus 1

RAF proto-oncogene serine/threonine-protein kinase
5.0 mol
Reactions
Reactions Rate Parameters
(Ras + GDP) => (Ras + GTP)

([Ras-like protein 1] + [GDP]) => ([Ras-like protein 1] + [GTP])
s144*(kass_r4_s144*s124*s125-kdiss_r4_s144*s124*s126)

[protein complex]*(kass_r4_s144*[Ras-like protein 1]*[GDP]-kdiss_r4_s144*[Ras-like protein 1]*[GTP])
kdiss_r4_s144 = 1.0; kass_r4_s144 = 1.0
(Akt) => (Akt)

([RAC-alpha serine/threonine-protein kinase]) => ([RAC-alpha serine/threonine-protein kinase])
s144*(kass_r7_s144*s21-kdiss_r7_s144*s22)

[protein complex]*(kass_r7_s144*[RAC-alpha serine/threonine-protein kinase]-kdiss_r7_s144*[RAC-alpha serine/threonine-protein kinase])
kass_r7_s144 = 1.0; kdiss_r7_s144 = 1.0
(MEK1_minus_2) => (MEK1_minus_2)

([Dual specificity mitogen-activated protein kinase kinase 1]) => ([Dual specificity mitogen-activated protein kinase kinase 1])
s24*(kcatp_r9/kM_r9_s25*s25-kcatn_r9/kM_r9_s26*s26)/(1+s25/kM_r9_s25+s26/kM_r9_s26)

[RAF proto-oncogene serine/threonine-protein kinase]*(kcatp_r9/kM_r9_s25*[Dual specificity mitogen-activated protein kinase kinase 1]-kcatn_r9/kM_r9_s26*[Dual specificity mitogen-activated protein kinase kinase 1])/(1+[Dual specificity mitogen-activated protein kinase kinase 1]/kM_r9_s25+[Dual specificity mitogen-activated protein kinase kinase 1]/kM_r9_s26)
kcatp_r9 = 2.0; kcatn_r9 = 0.693; kM_r9_s25 = 0.626; kM_r9_s26 = 0.463
(ERK1_minus_2) => (ERK1_minus_2)

([MAP kinase activity]) => ([MAP kinase activity])
s26*(kcatp_r14/kM_r14_s27*s27-kcatn_r14/kM_r14_s28*s28)/(1+s27/kM_r14_s27+s28/kM_r14_s28)

[Dual specificity mitogen-activated protein kinase kinase 1]*(kcatp_r14/kM_r14_s27*[MAP kinase activity]-kcatn_r14/kM_r14_s28*[MAP kinase activity])/(1+[MAP kinase activity]/kM_r14_s27+[MAP kinase activity]/kM_r14_s28)
kM_r14_s27 = 0.038; kM_r14_s28 = 1.65; kcatn_r14 = 0.725; kcatp_r14 = 0.558
(ERK1_minus_2) => (Mitogenesis_br_Differentiation)

([MAP kinase activity]) => ([Mitogenesis_br_Differentiation])
kass_r15*s28-kdiss_r15*s34

kass_r15*[MAP kinase activity]-kdiss_r15*[Mitogenesis_br_Differentiation]
kass_r15 = 2.0; kdiss_r15 = 0.074
(EGFR) => (EGFR)

([Receptor protein-tyrosine kinase]) => ([Receptor protein-tyrosine kinase])
s3*(kass_r17_s3*s123^2-kdiss_r17_s3*s129)

[Pro-epidermal growth factor]*(kass_r17_s3*[Receptor protein-tyrosine kinase]^2-kdiss_r17_s3*[Receptor protein-tyrosine kinase])
kass_r17_s3 = 0.73; kdiss_r17_s3 = 1.13
(EGFR + Complex(Grb2/../PLC)) => (Complex_br_(EGFR/../_br_GAP))

([Receptor protein-tyrosine kinase] + [protein complex]) => ([protein complex])
kI_re11_s142/(kI_re11_s142+s142)*Vp_re11*s129*s147/(ki_re11_s129*kM_re11_s147+kM_re11_s147*s129+kM_re11_s129*s147+s129*s147)

kI_re11_s142/(kI_re11_s142+[erlotinib])*Vp_re11*[Receptor protein-tyrosine kinase]*[protein complex]/(ki_re11_s129*kM_re11_s147+kM_re11_s147*[Receptor protein-tyrosine kinase]+kM_re11_s129*[protein complex]+[Receptor protein-tyrosine kinase]*[protein complex])
kI_re11_s142 = 1.0; kM_re11_s129 = 1.0; Vp_re11 = 1.0; ki_re11_s129 = 1.0; kM_re11_s147 = 1.0
(RKIP) => (RKIP)

([Phosphatidylethanolamine-binding protein 1]) => ([Phosphatidylethanolamine-binding protein 1])
s127*(kcatp_r11/kM_r11_s29*s29-kcatn_r11/kM_r11_s30*s30)/(1+s29/kM_r11_s29+s30/kM_r11_s30)

[Protein kinase C alpha type]*(kcatp_r11/kM_r11_s29*[Phosphatidylethanolamine-binding protein 1]-kcatn_r11/kM_r11_s30*[Phosphatidylethanolamine-binding protein 1])/(1+[Phosphatidylethanolamine-binding protein 1]/kM_r11_s29+[Phosphatidylethanolamine-binding protein 1]/kM_r11_s30)
kcatn_r11 = 0.566; kM_r11_s30 = 1.021; kM_r11_s29 = 1.459; kcatp_r11 = 0.787
(Raf_minus_1) => (Raf_minus_1)

([RAF proto-oncogene serine/threonine-protein kinase]) => ([RAF proto-oncogene serine/threonine-protein kinase])
kI_r8_s22/(kI_r8_s22+s22)*kI_r8_s29/(kI_r8_s29+s29)*kI_r8_s33/(kI_r8_s33+s33)*(s124*(kcatp_r8_s124/kM_r8_s124_s23*s23-kcatn_r8_s124/kM_r8_s124_s24*s24)/(1+s23/kM_r8_s124_s23+s24/kM_r8_s124_s24)+s31*(kcatp_r8_s31/kM_r8_s31_s23*s23-kcatn_r8_s31/kM_r8_s31_s24*s24)/(1+s23/kM_r8_s31_s23+s24/kM_r8_s31_s24))

kI_r8_s22/(kI_r8_s22+[RAC-alpha serine/threonine-protein kinase])*kI_r8_s29/(kI_r8_s29+[Phosphatidylethanolamine-binding protein 1])*kI_r8_s33/(kI_r8_s33+[14-3-3 protein gamma])*([Ras-like protein 1]*(kcatp_r8_s124/kM_r8_s124_s23*[RAF proto-oncogene serine/threonine-protein kinase]-kcatn_r8_s124/kM_r8_s124_s24*[RAF proto-oncogene serine/threonine-protein kinase])/(1+[RAF proto-oncogene serine/threonine-protein kinase]/kM_r8_s124_s23+[RAF proto-oncogene serine/threonine-protein kinase]/kM_r8_s124_s24)+[Serine/threonine-protein phosphatase 2A catalytic subunit beta isoform]*(kcatp_r8_s31/kM_r8_s31_s23*[RAF proto-oncogene serine/threonine-protein kinase]-kcatn_r8_s31/kM_r8_s31_s24*[RAF proto-oncogene serine/threonine-protein kinase])/(1+[RAF proto-oncogene serine/threonine-protein kinase]/kM_r8_s31_s23+[RAF proto-oncogene serine/threonine-protein kinase]/kM_r8_s31_s24))
kcatp_r8_s31 = 0.727; kM_r8_s124_s23 = 0.47; kcatn_r8_s31 = 0.636; kM_r8_s31_s23 = 0.614; kI_r8_s29 = 1.219; kM_r8_s31_s24 = 1.367; kI_r8_s22 = 0.583; kcatp_r8_s124 = 0.511; kI_r8_s33 = 0.293; kcatn_r8_s124 = 1.083; kM_r8_s124_s24 = 0.786
Curator's comment:
(added: 02 Nov 2011, 14:40:45, updated: 02 Nov 2011, 14:40:45)
Figure 1D of the reference publication has been reproduced here. The model was simulated using Copasi v4.7 (Build 34).