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.
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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.
Metadata information
PubMed 17052120
BioModels Database BIOMD0000000027
BioModels Database BIOMD0000000048
BioModels Database BIOMD0000000028
BioModels Database BIOMD0000000030
BioModels Database BIOMD0000000146
BioModels Database BIOMD0000000029
BioModels Database BIOMD0000000031
KEGG Pathway Insulin signaling pathway - Homo sapiens (human)
Reactome Signaling by Insulin receptor
Reactome Signaling by EGFR
Name | Description | Size | Actions |
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Model files |
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BIOMD0000000223_url.xml | SBML L2V4 representation of Borisov2009_EGF_Insulin_Crosstalk | 160.38 KB | Preview | Download |
Additional files |
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BIOMD0000000223.xpp | Auto-generated XPP file | 35.64 KB | Preview | Download |
BIOMD0000000223.pdf | Auto-generated PDF file | 666.31 KB | Preview | Download |
BIOMD0000000223.sci | Auto-generated Scilab file | 186.00 Bytes | Preview | Download |
BIOMD0000000223-biopax2.owl | Auto-generated BioPAX (Level 2) | 141.65 KB | Preview | Download |
BIOMD0000000223-biopax3.owl | Auto-generated BioPAX (Level 3) | 255.88 KB | Preview | Download |
BIOMD0000000223.svg | Auto-generated Reaction graph (SVG) | 278.10 KB | Preview | Download |
BIOMD0000000223.m | Auto-generated Octave file | 45.27 KB | Preview | Download |
BIOMD0000000223_urn.xml | Auto-generated SBML file with URNs | 159.91 KB | Preview | Download |
BIOMD0000000223.vcml | Auto-generated VCML file | 897.00 Bytes | 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
- Submitted on: 28-May-2014 01:41:56
- Submitted by: Nicolas Le Novère
- With comment: Current version of Borisov2009_EGF_Insulin_Crosstalk
-
Version: 1
- Submitted on: 28-Jun-2009 15:04:15
- Submitted by: Nicolas Le Novère
- With comment: Original import of InsEGFdiam_2008_10_03_debugged
(*) You might be seeing discontinuous
revisions as only public revisions are displayed here. Any private revisions
of this model will only be shown to the submitter and their collaborators.
: Variable used inside SBML models
Species | Initial Concentration/Amount |
---|---|
mGABp | 0.0 nmol |
mGABp GS | 0.0 nmol |
mGABp PI3K | 0.0 nmol |
mGABp SHP2 | 0.0 nmol |
mGABp pSHP2 | 0.0 nmol |
PIP3 | 0.0 nmol |
Reactions | Rate | Parameters |
---|---|---|
PI3K + mGABp => mGABp_PI3K | (k53*mGABp*PI3K-k_53*mGABp_PI3K)*cell | k53 = 0.0133 per nM per s; k_53 = NaN per second |
RasGAP + mGABp => mGABp_RasGAP | (k54*mGABp*RasGAP-k_54*mGABp_RasGAP)*cell | k_54 = NaN per second; k54 = 1.0E-5 per nM per s |
SHP2 + mGABp => mGABp_SHP2 | (k55*mGABp*SHP2-k_55*mGABp_SHP2)*cell | k_55 = NaN per second; k55 = 6.66E-4 per nM per s |
mGABp => PIP3 + GABp | (k_42*mGABp-k42*PIP3*GABp)*cell | k_42 = NaN per second; k42 = 0.00666 per nM per s |
mGABp => imGABp; ppErk | (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 | (k_42*mGABp_GS-k42*PIP3*GABp_GS)*cell | k_42 = NaN per second; k42 = 0.00666 per nM per s |
mGABp_SHP2 => SHP2 + mGAB | k56*mGABp_SHP2*cell | k56 = 0.666 per second |
mGABp_SHP2 => mGABp_pSHP2; Rp, aSrc | kcat57*mGABp_SHP2*(Rp+aSrc)/(Km57+mGABp_SHP2)*cell | Km57 = 150.0 nM; kcat57 = 0.133 per second |
mGABp_pSHP2 => mGABp_SHP2 | V58*mGABp_pSHP2/(Km58+mGABp_pSHP2)*cell | Km58 = 130.0 nM; V58 = 2.0 nM per sec |
mGABp_SHP2 => PIP3 + GABp_SHP2 | (k_42*mGABp_SHP2-k42*PIP3*GABp_SHP2)*cell | k_42 = NaN per second; k42 = 0.00666 per nM per s |
GS + mGABp_pSHP2 => mGABp_pSHP2_GS | (k59*mGABp_pSHP2*GS-k_59*mGABp_pSHP2_GS)*cell | k59 = 0.01 per nM per s; k_59 = NaN per second |
mGABp_pSHP2 => PIP3 + GABp_pSHP2 | (k_42*mGABp_pSHP2-k42*PIP3*GABp_pSHP2)*cell | k_42 = NaN per second; k42 = 0.00666 per nM per s |
GAB + PIP3 => mGAB | (k49*GAB*PIP3-k_49*mGAB)*cell | k49 = 6.66E-4 per nM per s; k_49 = NaN per second |
(added: 13 Jul 2009, 14:32:32, updated: 13 Jul 2009, 14:32:32)
Simulations were performed using SBML ODESolver, the graphs created using R.