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BIOMD0000000372 - Tolic2000_InsulinGlucoseFeedback

 

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Reference Publication
Publication ID: 11082306
Tolić IM, Mosekilde E, Sturis J.
Modeling the insulin-glucose feedback system: the significance of pulsatile insulin secretion.
J. Theor. Biol. 2000 Dec; 207(3): 361-375
The Rugjer Boskovic Institute, Zagreb, Croatia. itolic@hsph.harvard.edu  [more]
Model
Original Model: CellML logo
Submitter: Camille Laibe
Submission ID: MODEL1006230109
Submission Date: 23 Jun 2010 09:12:44 UTC
Last Modification Date: 10 Oct 2014 10:30:40 UTC
Creation Date: 28 Sep 2011 21:30:33 UTC
Encoders:  Ishan Ajmera
   Catherine Lloyd
set #1
bqbiol:occursIn Brenda Tissue Ontology pancreatic beta cell
set #2
bqmodel:isDerivedFrom BioModels Database Sturis1991_InsulinGlucoseModel_UltradianOscillation
set #3
bqbiol:isVersionOf Gene Ontology regulation of insulin secretion involved in cellular response to glucose stimulus
set #4
bqbiol:hasTaxon Taxonomy Homo sapiens
set #5
bqbiol:hasProperty Human Disease Ontology diabetes mellitus
Notes

This a model from the article:
Modeling the insulin-glucose feedback system: the significance of pulsatile insulin secretion.
Tolic IM, Mosekilde E, Sturis J. J Theor Biol2000 Dec 7;207(3):361-75 11082306,
Abstract:
A mathematical model of the insulin-glucose feedback regulation in man is used to examine the effects of an oscillatory supply of insulin compared to a constant supply at the same average rate. We show that interactions between the oscillatory insulin supply and the receptor dynamics can be of minute significance only. It is possible, however, to interpret seemingly conflicting results of clinical studies in terms of their different experimental conditions with respect to the hepatic glucose release. If this release is operating near an upper limit, an oscillatory insulin supply will be more efficient in lowering the blood glucose level than a constant supply. If the insulin level is high enough for the hepatic release of glucose to nearly vanish, the opposite effect is observed. For insulin concentrations close to the point of inflection of the insulin-glucose dose-response curve an oscillatory and a constant insulin infusion produce similar effects. Copyright 2000 Academic Press.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Tolic IM, Mosekilde E, Sturis J. (2000) - version=1.0

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.

Model
Publication ID: 11082306 Submission Date: 23 Jun 2010 09:12:44 UTC Last Modification Date: 10 Oct 2014 10:30:40 UTC Creation Date: 28 Sep 2011 21:30:33 UTC
Mathematical expressions
Rules
Assignment Rule (variable: f1_G) Assignment Rule (variable: Ip_conc) Assignment Rule (variable: Ii_conc) Assignment Rule (variable: G_conc)
Assignment Rule (variable: f2_G) Assignment Rule (variable: f3_G) Assignment Rule (variable: f4_Ii) Assignment Rule (variable: f5_x3)
Rate Rule (variable: Ip) Rate Rule (variable: Ii) Rate Rule (variable: G) Rate Rule (variable: x3)
Rate Rule (variable: x1) Rate Rule (variable: x2)    
Physical entities
Compartments Species
COMpartment x1 x2 x3
G Ii Ip
Global parameters
Vp Vi Vg E
Ip_conc tp td f1_G
Rm C1 a1 Ii_conc
ti G_conc Gin f2_G
f3_G f4_Ii f5_x3 C2
C3 C4 C5 U0
Um Ub beta Rg
alpha      
Reactions (0)
Rules (14)
 
 Assignment Rule (name: f1_G) f1_G = Rm/(1+exp((C1-G/Vg)/a1))
 
 Assignment Rule (name: Ip_conc) Ip_conc = Ip/Vp
 
 Assignment Rule (name: Ii_conc) Ii_conc = Ii/Vi
 
 Assignment Rule (name: G_conc) G_conc = G/(Vg*10)
 
 Assignment Rule (name: f2_G) f2_G = Ub*(1-exp((-G)/(C2*Vg)))
 
 Assignment Rule (name: f3_G) f3_G = G/(C3*Vg)
 
 Assignment Rule (name: f4_Ii) f4_Ii = U0+(Um-U0)/(1+exp((-beta)*log(Ii/C4*(1/Vi+1/(E*ti)))))
 
 Assignment Rule (name: f5_x3) f5_x3 = Rg/(1+exp(alpha*(x3*1/Vp-C5)))
 
 Rate Rule (name: Ip) d [ Ip] / d t= f1_G-(E*(Ip/Vp-Ii/Vi)+Ip/tp)
 
 Rate Rule (name: Ii) d [ Ii] / d t= E*(Ip/Vp-Ii/Vi)-Ii/ti
 
 Rate Rule (name: G) d [ G] / d t= Gin+f5_x3+(-(f2_G+f3_G*f4_Ii))
 
 Rate Rule (name: x3) d [ x3] / d t= 3/td*(x2-x3)
 
 Rate Rule (name: x1) d [ x1] / d t= 3/td*(Ip/1-x1)
 
 Rate Rule (name: x2) d [ x2] / d t= 3/td*(x1-x2)
 
   COMpartment Spatial dimensions: 3.0  Compartment size: 1.0
 
 x1
Compartment: COMpartment
Initial amount: 110.420253
 
 x2
Compartment: COMpartment
Initial amount: 112.7601171
 
 x3
Compartment: COMpartment
Initial amount: 104.5878705
 
 G
Compartment: COMpartment
Initial amount: 12342.61665
 
 Ii
Compartment: COMpartment
Initial amount: 243.2865183
 
 Ip
Compartment: COMpartment
Initial amount: 93.36441699
 
Global Parameters (29)
 
 Vp
Value: 3.0
Constant
 
 Vi
Value: 11.0
Constant
 
 Vg
Value: 10.0
Constant
 
 E
Value: 0.2
Constant
 
  Ip_conc
Value: 31.12147233
 
 tp
Value: 6.0
Constant
 
 td
Value: 36.0
Constant
 
   f1_G
Value: 15.174877041143
 
 Rm
Value: 210.0
Constant
 
 C1
Value: 2000.0
Constant
 
 a1
Value: 300.0
Constant
 
   Ii_conc
Value: 22.1169562090909
 
 ti
Value: 100.0
Constant
 
   G_conc
Value: 123.4261665
 
 Gin
Value: 216.0
Constant
 
   f2_G
Value: 71.9863579104227
 
   f3_G
Value: 1.234261665
 
   f4_Ii
Value: 204.190214963962
 
   f5_x3
Value: 12.7950632297315
 
 C2
Value: 144.0
Constant
 
 C3
Value: 1000.0
Constant
 
 C4
Value: 80.0
Constant
 
 C5
Value: 26.0
Constant
 
 U0
Value: 40.0
Constant
 
 Um
Value: 940.0
Constant
 
 Ub
Value: 72.0
Constant
 
 beta
Value: 1.77
Constant
 
 Rg
Value: 180.0
Constant
 
 alpha
Value: 0.29
Constant
 
Representative curation result(s)
Representative curation result(s) of BIOMD0000000372

Curator's comment: (updated: 28 Sep 2011 23:51:33 BST)

The model reproduces fig 1 of the reference publication.
The model was integrated and simulated using Copasi v4.7 (Build 34).

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