DiCamillo2016 - Insulin signalling pathway - Rule-based model
Model Identifier
BIOMD0000000833
Short description
Format
SBML
(L2V4)
Related Publication
-
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/ # MODEL 1604100005).
Contributors
Submitter of the first revision: Vijayalakshmi Chelliah
Submitter of this revision: Mohammad Umer Sharif Shohan
Modellers: Vijayalakshmi Chelliah, Mohammad Umer Sharif Shohan
Submitter of this revision: Mohammad Umer Sharif Shohan
Modellers: Vijayalakshmi Chelliah, Mohammad Umer Sharif Shohan
Metadata information
isInstanceOf (3 statements)
hasProperty (2 statements)
is (2 statements)
isDescribedBy (2 statements)
BioModels Database
MODEL1604100005
Gene Ontology insulin receptor signaling pathway
Taxonomy Homo sapiens
Gene Ontology insulin receptor signaling pathway
Taxonomy Homo sapiens
hasProperty (2 statements)
is (2 statements)
isDescribedBy (2 statements)
Curation status
Curated
Tags
Connected external resources
SBGN view in Newt Editor
| Name | Description | Size | Actions |
|---|---|---|---|
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
Revisions
-
Version: 6
- Submitted on: Oct 14, 2019 11:42:25 AM
- Submitted by: Mohammad Umer Sharif Shohan
- With comment: Automatically added model identifier BIOMD0000000833
-
Version: 2
- 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
- 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
of this model will only be shown to the submitter and their collaborators.
Legends
: Variable used inside SBML models
: Variable used inside SBML models
Species
| Species | Initial Concentration/Amount |
|---|---|
| S4 Insulin receptor substrate 1 |
541800.0 item |
| S20 | 361200.0 item |
| S52 | 0.0 item |
| S54 RAF proto-oncogene serine/threonine-protein kinase |
0.0 item |
| S58 Insulin |
0.0 item |
| S60 Insulin |
0.0 item |
| S8 | 1679.58 item |
| S15 AKT kinase-transforming protein |
180600.0 item |
| S30 Insulin |
0.0 item |
Reactions
| 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)
(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.