DiCamillo2016 - Insulin signalling pathway - Rule-based model

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  • A rule-based model of insulin signalling pathway.
  • Di Camillo B, Carlon A, Eduati F, Toffolo GM
  • BMC systems biology , 1/ 2016 , Volume 10 , pages: 38 , PubMed ID: 27245161
  • Department of Information Engineering, University of Padova, Via Gradenigo 6A, Padova, 35131, Italy.
  • The insulin signalling pathway (ISP) is an important biochemical pathway, which regulates some fundamental biological functions such as glucose and lipid metabolism, protein synthesis, cell proliferation, cell differentiation and apoptosis. In the last years, different mathematical models based on ordinary differential equations have been proposed in the literature to describe specific features of the ISP, thus providing a description of the behaviour of the system and its emerging properties. However, protein-protein interactions potentially generate a multiplicity of distinct chemical species, an issue referred to as "combinatorial complexity", which results in defining a high number of state variables equal to the number of possible protein modifications. This often leads to complex, error prone and difficult to handle model definitions.In this work, we present a comprehensive model of the ISP, which integrates three models previously available in the literature by using the rule-based modelling (RBM) approach. RBM allows for a simple description of a number of signalling pathway characteristics, such as the phosphorylation of signalling proteins at multiple sites with different effects, the simultaneous interaction of many molecules of the signalling pathways with several binding partners, and the information about subcellular localization where reactions take place. Thanks to its modularity, it also allows an easy integration of different pathways. After RBM specification, we simulated the dynamic behaviour of the ISP model and validated it using experimental data. We the examined the predicted profiles of all the active species and clustered them in four clusters according to their dynamic behaviour. Finally, we used parametric sensitivity analysis to show the role of negative feedback loops in controlling the robustness of the system.The presented ISP model is a powerful tool for data simulation and can be used in combination with experimental approaches to guide the experimental design. The model is available at http://sysbiobig.dei.unipd.it/ was submitted to Biomodels Database ( https://www.ebi.ac.uk/biomodels-main/ # MODELĀ 1604100005).
Submitter of the first revision: Vijayalakshmi Chelliah
Submitter of this revision: Mohammad Umer Sharif Shohan
Modellers: Vijayalakshmi Chelliah, Mohammad Umer Sharif Shohan

Metadata information

isInstanceOf (3 statements)
BioModels Database MODEL1604100005
Gene Ontology insulin receptor signaling pathway
Taxonomy Homo sapiens

hasProperty (2 statements)
BioModels Database MODEL1604100005
Taxonomy Homo sapiens

is (2 statements)
BioModels Database MODEL1604100005
BioModels Database BIOMD0000000833

isDescribedBy (2 statements)

Curation status


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Model files

DiCamillo2016.xml SBML L2V4 representation of DiCamillo2016 - Insulin signalling pathway - Rule-based model 386.47 KB Preview | Download

Additional files

DiCamillo2016.cps COPASI version 4.24 (Build 197) Insulin signalling pathway- Rule-based model 670.01 KB Preview | Download
DiCamillo2016.sedml SEDML L1V2 Insulin signalling pathway - Rule-based model 1.01 KB Preview | Download
MODEL1604100005-biopax2.owl Auto-generated BioPAX (Level 2) 84.88 KB Preview | Download
MODEL1604100005-biopax3.owl Auto-generated BioPAX (Level 3) 158.31 KB Preview | Download
MODEL1604100005.m Auto-generated Octave file 50.81 KB Preview | Download
MODEL1604100005.png Auto-generated Reaction graph (PNG) 928.99 KB Preview | Download
MODEL1604100005.sci Auto-generated Scilab file 23.71 KB Preview | Download
MODEL1604100005.svg Auto-generated Reaction graph (SVG) 151.88 KB Preview | Download
MODEL1604100005.vcml Auto-generated VCML file 948.00 Bytes Preview | Download
MODEL1604100005.xpp Auto-generated XPP file 45.89 KB Preview | Download
MODEL1604100005_url.xml old xml file 237.56 KB Preview | Download
MODEL1604100005_urn.xml Auto-generated SBML file with URNs 237.56 KB Preview | Download

  • Model originally submitted by : Vijayalakshmi Chelliah
  • Submitted: Apr 10, 2016 8:32:23 PM
  • Last Modified: Oct 14, 2019 11:42:25 AM
  • Version: 6 public model Download this version
    • Submitted on: Oct 14, 2019 11:42:25 AM
    • Submitted by: Mohammad Umer Sharif Shohan
    • With comment: Automatically added model identifier BIOMD0000000833
  • Version: 2 public model Download this version
    • Submitted on: Jul 22, 2016 9:16:45 AM
    • Submitted by: Vijayalakshmi Chelliah
    • With comment: Current version of DiCamillo2016 - Insulin signalling pathway - Rule-based model
  • Version: 1 public model Download this version
    • Submitted on: Apr 10, 2016 8:32:23 PM
    • Submitted by: Vijayalakshmi Chelliah
    • With comment: Original import of NoName

(*) You might be seeing discontinuous revisions as only public revisions are displayed here. Any private revisions unpublished model revision of this model will only be shown to the submitter and their collaborators.

: Variable used inside SBML models

Species Initial Concentration/Amount

Insulin receptor substrate 1
541800.0 item
S20 361200.0 item
S52 0.0 item

RAF proto-oncogene serine/threonine-protein kinase
0.0 item

0.0 item

0.0 item
S8 1679.58 item

AKT kinase-transforming protein
180600.0 item

0.0 item
Reactions Rate Parameters
S4 => S32 cell*f7*S4/cell f7 = 0.0
S20 + S32 => S52 cell*k26*S20*S32/cell k26 = 2.21262458471761E-5
S52 => S20 + S32 cell*k_26*S52/cell k_26 = 3996.0
S45 => S54 cell*f31*S45/cell f31 = 0.0
S58 => S21 + S49 cell*k_22*S58/cell k_22 = 7.992
S60 => S3 cell*f6*S60/cell f6 = 0.461
S33 => S4 cell*k_7p*S33/cell k_7p = 1.386
S9 => S8 cell*k10*S9/cell k10 = 2.9638017137931
S15 => S34 cell*f11*S15/cell f11 = 0.0
S30 => S1 + S2 cell*k_1*S30/cell k_1 = 0.2
Curator's comment:
(added: 14 Oct 2019, 11:38:41, updated: 14 Oct 2019, 11:38:41)
The model is encoded using COPASI 4.24 (Build 197) and plots are generated using R ggplot package. Model simulation time is 60 min. Two of the figures from Figure 3 have been reproduced (bottom left). The figures are exact. The submitted xml file has some difference between the parameters compared to the manuscript. parameters for k17 was changed to 4.592e-4 and alpha32 was changed to (1e-6)/(u*(1/60)) as given in the paper supplement file.