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A rule-based model of insulin signalling pathway
BACKGROUND: 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 model...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888568/ https://www.ncbi.nlm.nih.gov/pubmed/27245161 http://dx.doi.org/10.1186/s12918-016-0281-4 |
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author | Di Camillo, Barbara Carlon, Azzurra Eduati, Federica Toffolo, Gianna Maria |
author_facet | Di Camillo, Barbara Carlon, Azzurra Eduati, Federica Toffolo, Gianna Maria |
author_sort | Di Camillo, Barbara |
collection | PubMed |
description | BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0281-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4888568 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48885682016-06-02 A rule-based model of insulin signalling pathway Di Camillo, Barbara Carlon, Azzurra Eduati, Federica Toffolo, Gianna Maria BMC Syst Biol Research Article BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0281-4) contains supplementary material, which is available to authorized users. BioMed Central 2016-06-01 /pmc/articles/PMC4888568/ /pubmed/27245161 http://dx.doi.org/10.1186/s12918-016-0281-4 Text en © Di Camillo et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Di Camillo, Barbara Carlon, Azzurra Eduati, Federica Toffolo, Gianna Maria A rule-based model of insulin signalling pathway |
title | A rule-based model of insulin signalling pathway |
title_full | A rule-based model of insulin signalling pathway |
title_fullStr | A rule-based model of insulin signalling pathway |
title_full_unstemmed | A rule-based model of insulin signalling pathway |
title_short | A rule-based model of insulin signalling pathway |
title_sort | rule-based model of insulin signalling pathway |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888568/ https://www.ncbi.nlm.nih.gov/pubmed/27245161 http://dx.doi.org/10.1186/s12918-016-0281-4 |
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