public model
Model Identifier
BIOMD0000000223
Short description

described in: Systems-level interactions between insulin-EGF networks amplify mitogenic signaling.
Borisov N, Aksamitiene E, Kiyatkin A, Legewie S, Berkhout J, Maiwald T, Kaimachnikov NP, Timmer J, Hoek JB, Kholodenko BN.;Mol Syst Biol. 2009;5:256. Epub 2009 Apr 7. PMID:19357636; doi:10.1038/msb.2009.19
Abstract:
Crosstalk mechanisms have not been studied as thoroughly as individual signaling pathways. We exploit experimental and computational approaches to reveal how a concordant interplay between the insulin and epidermal growth factor (EGF) signaling networks can potentiate mitogenic signaling. In HEK293 cells, insulin is a poor activator of the Ras/ERK (extracellular signal-regulated kinase) cascade, yet it enhances ERK activation by low EGF doses. We find that major crosstalk mechanisms that amplify ERK signaling are localized upstream of Ras and at the Ras/Raf level. Computational modeling unveils how critical network nodes, the adaptor proteins GAB1 and insulin receptor substrate (IRS), Src kinase, and phosphatase SHP2, convert insulin-induced increase in the phosphatidylinositol-3,4,5-triphosphate (PIP(3)) concentration into enhanced Ras/ERK activity. The model predicts and experiments confirm that insulin-induced amplification of mitogenic signaling is abolished by disrupting PIP(3)-mediated positive feedback via GAB1 and IRS. We demonstrate that GAB1 behaves as a non-linear amplifier of mitogenic responses and insulin endows EGF signaling with robustness to GAB1 suppression. Our results show the feasibility of using computational models to identify key target combinations and predict complex cellular responses to a mixture of external cues.

An extracellular compartment with 34 times the volume of the cell was added and the association rate as well as the dissociation constants for Insulin and EGF binding were altered (kon'=34*kon, KD'=KD/34). This was done to allow using the concentrations for those species given in the article and retaining the same dynamics and Ligand depletion as in the matlab file the SBML file was exported from.

SBML model exported from PottersWheel on 2008-10-14 16:26:44.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Format
SBML (L2V4)
Related Publication
  • Systems-level interactions between insulin-EGF networks amplify mitogenic signaling.
  • Borisov N, Aksamitiene E, Kiyatkin A, Legewie S, Berkhout J, Maiwald T, Kaimachnikov NP, Timmer J, Hoek JB, Kholodenko BN
  • Molecular Systems Biology , 0/ 2009 , Volume 5 , pages: 256 , PubMed ID: 19357636
  • Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA 19107, USA.
  • Crosstalk mechanisms have not been studied as thoroughly as individual signaling pathways. We exploit experimental and computational approaches to reveal how a concordant interplay between the insulin and epidermal growth factor (EGF) signaling networks can potentiate mitogenic signaling. In HEK293 cells, insulin is a poor activator of the Ras/ERK (extracellular signal-regulated kinase) cascade, yet it enhances ERK activation by low EGF doses. We find that major crosstalk mechanisms that amplify ERK signaling are localized upstream of Ras and at the Ras/Raf level. Computational modeling unveils how critical network nodes, the adaptor proteins GAB1 and insulin receptor substrate (IRS), Src kinase, and phosphatase SHP2, convert insulin-induced increase in the phosphatidylinositol-3,4,5-triphosphate (PIP(3)) concentration into enhanced Ras/ERK activity. The model predicts and experiments confirm that insulin-induced amplification of mitogenic signaling is abolished by disrupting PIP(3)-mediated positive feedback via GAB1 and IRS. We demonstrate that GAB1 behaves as a non-linear amplifier of mitogenic responses and insulin endows EGF signaling with robustness to GAB1 suppression. Our results show the feasibility of using computational models to identify key target combinations and predict complex cellular responses to a mixture of external cues.
Contributors
Nicolas Le Novère

Metadata information

is
BioModels Database MODEL6194251662
BioModels Database BIOMD0000000223
isDerivedFrom
BioModels Database BIOMD0000000026
PubMed 17052120
BioModels Database BIOMD0000000027
BioModels Database BIOMD0000000048
BioModels Database BIOMD0000000028
BioModels Database BIOMD0000000030
BioModels Database BIOMD0000000146
BioModels Database BIOMD0000000029
BioModels Database BIOMD0000000031
isDescribedBy
PubMed 19357636
hasTaxon
Taxonomy Homo sapiens

Curation status
Curated

Tags
Name Description Size Actions

Model files

BIOMD0000000223_url.xml SBML L2V4 representation of Borisov2009_EGF_Insulin_Crosstalk 160.38 KB Preview | Download

Additional files

BIOMD0000000223-biopax3.owl Auto-generated BioPAX (Level 3) 255.88 KB Preview | Download
BIOMD0000000223-biopax2.owl Auto-generated BioPAX (Level 2) 141.65 KB Preview | Download
BIOMD0000000223_urn.xml Auto-generated SBML file with URNs 159.91 KB Preview | Download
BIOMD0000000223.m Auto-generated Octave file 45.27 KB Preview | Download
BIOMD0000000223.svg Auto-generated Reaction graph (SVG) 278.10 KB Preview | Download
BIOMD0000000223.vcml Auto-generated VCML file 897.00 bytes Preview | Download
BIOMD0000000223.xpp Auto-generated XPP file 35.64 KB Preview | Download
BIOMD0000000223.sci Auto-generated Scilab file 186.00 bytes Preview | Download
BIOMD0000000223.pdf Auto-generated PDF file 666.31 KB Preview | Download
BIOMD0000000223.png Auto-generated Reaction graph (PNG) 2.70 MB Preview | Download

  • Model originally submitted by : Nicolas Le Novère
  • Submitted: 28-Jun-2009 15:04:15
  • Last Modified: 28-May-2014 01:41:56
Revisions
  • Version: 2 public model Download this version
    • Submitted on: 28-May-2014 01:41:56
    • Submitted by: Nicolas Le Novère
    • With comment: Current version of Borisov2009_EGF_Insulin_Crosstalk
  • Version: 1 public model Download this version
    • Submitted on: 28-Jun-2009 15:04:15
    • Submitted by: Nicolas Le Novère
    • With comment: Original import of InsEGFdiam_2008_10_03_debugged
Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
GAB 225.0 nmol
mGAB 0.0 nmol
mGABp 0.0 nmol
mGABp GS 0.0 nmol
mGABp pSHP2 0.0 nmol
PIP3 0.0 nmol
Reactions
Reactions Rate Parameters
(GABp_GS) => (GS + GAB)

([GABp_GS]) => ([GS] + [GAB])
V51*GABp_GS/(Km51+GABp_GS)*cell

V51*[GABp_GS]/(Km51+[GABp_GS])*cell
V51 = 333.0 nM per sec; Km51 = 130.0 nM
(GABp_SHP2) => (SHP2 + GAB)

([GABp_SHP2]) => ([SHP2] + [GAB])
k56*GABp_SHP2*cell

k56*[GABp_SHP2]*cell
k56 = 0.666 per second
(GABp_pSHP2_GS) => (GS + SHP2 + GAB)

([GABp_pSHP2_GS]) => ([GS] + [SHP2] + [GAB])
k56*GABp_pSHP2_GS*cell

k56*[GABp_pSHP2_GS]*cell
k56 = 0.666 per second
(mGAB) => (mGABp)

([mGAB]) => ([mGABp])
kcat50*mGAB*(Rp+alpha50*aSrc)/(Km50+mGAB)*cell

kcat50*[mGAB]*([Pro-epidermal growth factor; Epidermal growth factor receptor]+alpha50*[aSrc])/(Km50+[mGAB])*cell
kcat50 = 3333.0 per second; Km50 = 150.0 nM; alpha50 = 1.0E-4 dimensionless
(mGABp) => (mGAB)

([mGABp]) => ([mGAB])
V51*mGABp/(Km51+mGABp)*cell

V51*[mGABp]/(Km51+[mGABp])*cell
V51 = 333.0 nM per sec; Km51 = 130.0 nM
(mGAB) => (imGAB)

([mGAB]) => ([imGAB])
(kcat80*mGAB*ppErk/(Km80+mGAB)-k_80*imGAB)*cell

(kcat80*[mGAB]*[ppErk]/(Km80+[mGAB])-k_80*[imGAB])*cell
k_80 = 6.66E-5 per second; kcat80 = 0.04 per second; Km80 = 700.0 nM
(mGABp_pSHP2_GS) => (GS + SHP2 + mGAB)

([mGABp_pSHP2_GS]) => ([GS] + [SHP2] + [mGAB])
k56*mGABp_pSHP2_GS*cell

k56*[mGABp_pSHP2_GS]*cell
k56 = 0.666 per second
(SHP2 + mGABp) => (mGABp_SHP2)

([SHP2] + [mGABp]) => ([mGABp_SHP2])
(k55*mGABp*SHP2-k_55*mGABp_SHP2)*cell

(k55*[mGABp]*[SHP2]-k_55*[mGABp_SHP2])*cell
k_55 = NaN per second; k55 = 6.66E-4 per nM per s
(mGABp) => (imGABp)

([mGABp]) => ([imGABp])
(2*kcat80*mGABp*ppErk/(Km80+mGABp)-k_80*imGABp)*cell

(2*kcat80*[mGABp]*[ppErk]/(Km80+[mGABp])-k_80*[imGABp])*cell
k_80 = 6.66E-5 per second; kcat80 = 0.04 per second; Km80 = 700.0 nM
(mGABp_GS) => (PIP3 + GABp_GS)

([mGABp_GS]) => ([PIP3] + [GABp_GS])
(k_42*mGABp_GS-k42*PIP3*GABp_GS)*cell

(k_42*[mGABp_GS]-k42*[PIP3]*[GABp_GS])*cell
k_42 = NaN per second; k42 = 0.00666 per nM per s
(mGABp_SHP2) => (mGABp_pSHP2)

([mGABp_SHP2]) => ([mGABp_pSHP2])
kcat57*mGABp_SHP2*(Rp+aSrc)/(Km57+mGABp_SHP2)*cell

kcat57*[mGABp_SHP2]*([Pro-epidermal growth factor; Epidermal growth factor receptor]+[aSrc])/(Km57+[mGABp_SHP2])*cell
Km57 = 150.0 nM; kcat57 = 0.133 per second
(mGABp_pSHP2) => (mGABp_SHP2)

([mGABp_pSHP2]) => ([mGABp_SHP2])
V58*mGABp_pSHP2/(Km58+mGABp_pSHP2)*cell

V58*[mGABp_pSHP2]/(Km58+[mGABp_pSHP2])*cell
Km58 = 130.0 nM; V58 = 2.0 nM per sec
(GS + mGABp_pSHP2) => (mGABp_pSHP2_GS)

([GS] + [mGABp_pSHP2]) => ([mGABp_pSHP2_GS])
(k59*mGABp_pSHP2*GS-k_59*mGABp_pSHP2_GS)*cell

(k59*[mGABp_pSHP2]*[GS]-k_59*[mGABp_pSHP2_GS])*cell
k59 = 0.01 per nM per s; k_59 = NaN per second
(IRS + PIP3) => (mIRS)

([IRS] + [PIP3]) => ([mIRS])
(k42*IRS*PIP3-k_42*mIRS)*cell

(k42*[IRS]*[PIP3]-k_42*[mIRS])*cell
k_42 = NaN per second; k42 = 0.00666 per nM per s
Curator's comment:
(added: 13 Jul 2009, 14:32:32, updated: 13 Jul 2009, 14:32:32)
Reproduction of some time courses from figure 3A of the original article.
Simulations were performed using SBML ODESolver, the graphs created using R.