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BIOMD0000000423 - Nyman2012_InsulinSignalling

 

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Reference Publication
Publication ID: 22248283
Nyman E, Fagerholm S, Jullesson D, Strålfors P, Cedersund G.
Mechanistic explanations for counter-intuitive phosphorylation dynamics of the insulin receptor and insulin receptor substrate-1 in response to insulin in murine adipocytes.
FEBS J. 2012 Mar; 279(6): 987-999
Department of Clinical and Experimental Medicine, Diabetes and Integrative Systems Biology, Linköping University, Sweden.  [more]
Model
Original Model: BIOMD0000000423.xml.origin
Submitter: Elin Nyman
Submission ID: MODEL1207270000
Submission Date: 27 Jul 2012 10:18:37 UTC
Last Modification Date: 09 Aug 2012 15:53:40 UTC
Creation Date: 27 Jul 2012 12:02:12 UTC
Encoders:  Vijayalakshmi Chelliah
   Elin Nyman
set #1
bqbiol:occursIn Taxonomy Murinae
Brenda Tissue Ontology BTO:0000443
bqbiol:isVersionOf Gene Ontology insulin receptor signaling pathway
bqmodel:isDerivedFrom BioModels Database Brannmark2010_InsulinSignalling_Mifamodel
Notes

This model is from the article:
Mechanistic explanations for counter-intuitive phosphorylation dynamics of the insulin receptor and insulin receptor substrate-1 in response to insulin in murine adipocytes.
Nyman E, Fagerholm S, Jullesson D, Strålfors P, Cedersund G. FEBS J. 2012 Jan 16. 22248283 ,
Abstract:
Insulin signaling through insulin receptor (IR) and insulin receptor substrate-1 (IRS1) is important for insulin control of target cells. We have previously demonstrated a rapid and simultaneous overshoot behavior in the phosphorylation dynamics of IR and IRS1 in human adipocytes. Herein, we demonstrate that in murine adipocytes a similar overshoot behavior is not simultaneous for IR and IRS1. The peak of IRS1 phosphorylation, which is a direct consequence of the phosphorylation and the activation of IR, occurs earlier than the peak of IR phosphorylation. We used a conclusive modeling framework to unravel the mechanisms behind this counter-intuitive order of phosphorylation. Through a number of rejections, we demonstrate that two fundamentally different mechanisms may create the reversed order of peaks: (i) two pools of phosphorylated IR, where a large pool of internalized IR peaks late, but phosphorylation of IRS1 is governed by a small plasma membrane-localized pool of IR with an early peak, or (ii) inhibition of the IR-catalyzed phosphorylation of IRS1 by negative feedback. Although (i) may explain the reversed order, this two-pool hypothesis alone requires extensive internalization of IR, which is not supported by experimental data. However, with the additional assumption of limiting concentrations of IRS1, (i) can explain all data. Also, (ii) can explain all available data. Our findings illustrate how modeling can potentiate reasoning, to help draw nontrivial conclusions regarding competing mechanisms in signaling networks. Our work also reveals new differences between human and murine insulin signaling. Database The mathematical model described here has been submitted to the Online Cellular Systems Modelling Database and can be accessed at http://jjj.biochem.sun.ac.za/database/nyman/index.html free of charge.

Model
Publication ID: 22248283 Submission Date: 27 Jul 2012 10:18:37 UTC Last Modification Date: 09 Aug 2012 15:53:40 UTC Creation Date: 27 Jul 2012 12:02:12 UTC
Mathematical expressions
Reactions
v1a v1b v1c v1d
v1e v1g v1r v2
vm2 v3 vm3  
Rules
Assignment Rule (variable: measIRS1) Assignment Rule (variable: measIRp) Assignment Rule (variable: IRmem)  
Physical entities
Compartments Species
default IR IRins IRp
IRiP IRi IRS
IRSiP X Xp
Global parameters
k1a k1aBasic k1b k1c
k1d k1e k1f k1g
k1r k21 k22 km2
km23 k3 km3 ins
measIRS1 measIRp IRmem  
Reactions (11)
 
 v1a [IR] → [IRins];  
 
 v1b [IRins] → [IR];  
 
 v1c [IRins] → [IRp];  
 
 v1d [IRp] → [IRiP];  
 
 v1e [IRiP] → [IRi];   {Xp}
 
 v1g [IRp] → [IR];  
 
 v1r [IRi] → [IR];  
 
 v2 [IRS] → [IRSiP];   {IRiP} , {IRp} , {Xp}
 
 vm2 [IRSiP] → [IRS];  
 
 v3 [X] → [Xp];   {IRSiP}
 
 vm3 [Xp] → [X];  
 
Rules (3)
 
 Assignment Rule (name: measIRS1) measIRS1 = IRSiP
 
 Assignment Rule (name: measIRp) measIRp = IRp+IRiP
 
 Assignment Rule (name: IRmem) IRmem = IRp+IRins+IR
 
 default Spatial dimensions: 3.0  Compartment size: 1.0
 
 IR
Compartment: default
Initial concentration: 8.94067597532632
 
 IRins
Compartment: default
Initial concentration: 0.59688996214639
 
 IRp
Compartment: default
Initial concentration: 0.0383525925240207
 
 IRiP
Compartment: default
Initial concentration: 0.424076631823384
 
 IRi
Compartment: default
Initial concentration: 4.83863890758515E-6
 
 IRS
Compartment: default
Initial concentration: 9.43998194225544
 
 IRSiP
Compartment: default
Initial concentration: 0.560018057744573
 
 X
Compartment: default
Initial concentration: 9.99635886407151
 
 Xp
Compartment: default
Initial concentration: 0.00364113592848386
 
Global Parameters (19)
 
   k1a
Value: 0.153418
Constant
 
   k1aBasic
Value: 0.0383389
Constant
 
   k1b
Value: 3.4699E-6
Constant
 
   k1c
Value: 0.574266
Constant
 
   k1d
Value: 4.78844
Constant
 
   k1e
Value: 5.25027E-5
Constant
 
   k1f
Value: 119.353
Constant
 
   k1g
Value: 4.14899
Constant
 
   k1r
Value: 37954.7
Constant
 
   k21
Value: 538004.0
Constant
 
   k22
Value: 1.7252E-6
Constant
 
   km2
Value: 262759.0
Constant
 
   km23
Value: 88.9096
Constant
 
   k3
Value: 8.62917E-5
Constant
 
   km3
Value: 0.132671
Constant
 
   ins
Value: 100.0
Constant
 
   measIRS1  
 
   measIRp  
 
   IRmem  
 
Representative curation result(s)
Representative curation result(s) of BIOMD0000000423

Curator's comment: (updated: 27 Jul 2012 13:00:23 BST)

The model reproduces figure 5b,5c and 5d of the reference publication. The y-axis of the plots in the paper are rescaled. To reproduce the plot in the paper, 1) multiply model variable measIRp by a scaling factor of 31.8, 2) multiply model variable measIRS1 by a scaling factor of 21.2 and 3) multiply model variable IRmem by a scaling factor of 10.

The model simulation was done using Copasi v4.8 (Build 35). The data were obtained from Copasi and plotted using Gnuplot.

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