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Generative probabilistic models for protein–protein interaction networks—the biclique perspective
Motivation: Much of the large-scale molecular data from living cells can be represented in terms of networks. Such networks occupy a central position in cellular systems biology. In the protein–protein interaction (PPI) network, nodes represent proteins and edges represent connections between them,...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117378/ https://www.ncbi.nlm.nih.gov/pubmed/21685063 http://dx.doi.org/10.1093/bioinformatics/btr201 |
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author | Schweiger, Regev Linial, Michal Linial, Nathan |
author_facet | Schweiger, Regev Linial, Michal Linial, Nathan |
author_sort | Schweiger, Regev |
collection | PubMed |
description | Motivation: Much of the large-scale molecular data from living cells can be represented in terms of networks. Such networks occupy a central position in cellular systems biology. In the protein–protein interaction (PPI) network, nodes represent proteins and edges represent connections between them, based on experimental evidence. As PPI networks are rich and complex, a mathematical model is sought to capture their properties and shed light on PPI evolution. The mathematical literature contains various generative models of random graphs. It is a major, still largely open question, which of these models (if any) can properly reproduce various biologically interesting networks. Here, we consider this problem where the graph at hand is the PPI network of Saccharomyces cerevisiae. We are trying to distinguishing between a model family which performs a process of copying neighbors, represented by the duplication–divergence (DD) model, and models which do not copy neighbors, with the Barabási–Albert (BA) preferential attachment model as a leading example. Results: The observed property of the network is the distribution of maximal bicliques in the graph. This is a novel criterion to distinguish between models in this area. It is particularly appropriate for this purpose, since it reflects the graph's growth pattern under either model. This test clearly favors the DD model. In particular, for the BA model, the vast majority (92.9%) of the bicliques with both sides ≥4 must be already embedded in the model's seed graph, whereas the corresponding figure for the DD model is only 5.1%. Our results, based on the biclique perspective, conclusively show that a naïve unmodified DD model can capture a key aspect of PPI networks. Contact: regevs01@cs.huji.ac.il; michall@cc.huji.ac.il; nati@cs.huji.ac.il Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-3117378 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-31173782011-06-17 Generative probabilistic models for protein–protein interaction networks—the biclique perspective Schweiger, Regev Linial, Michal Linial, Nathan Bioinformatics Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria Motivation: Much of the large-scale molecular data from living cells can be represented in terms of networks. Such networks occupy a central position in cellular systems biology. In the protein–protein interaction (PPI) network, nodes represent proteins and edges represent connections between them, based on experimental evidence. As PPI networks are rich and complex, a mathematical model is sought to capture their properties and shed light on PPI evolution. The mathematical literature contains various generative models of random graphs. It is a major, still largely open question, which of these models (if any) can properly reproduce various biologically interesting networks. Here, we consider this problem where the graph at hand is the PPI network of Saccharomyces cerevisiae. We are trying to distinguishing between a model family which performs a process of copying neighbors, represented by the duplication–divergence (DD) model, and models which do not copy neighbors, with the Barabási–Albert (BA) preferential attachment model as a leading example. Results: The observed property of the network is the distribution of maximal bicliques in the graph. This is a novel criterion to distinguish between models in this area. It is particularly appropriate for this purpose, since it reflects the graph's growth pattern under either model. This test clearly favors the DD model. In particular, for the BA model, the vast majority (92.9%) of the bicliques with both sides ≥4 must be already embedded in the model's seed graph, whereas the corresponding figure for the DD model is only 5.1%. Our results, based on the biclique perspective, conclusively show that a naïve unmodified DD model can capture a key aspect of PPI networks. Contact: regevs01@cs.huji.ac.il; michall@cc.huji.ac.il; nati@cs.huji.ac.il Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2011-07-01 2011-06-14 /pmc/articles/PMC3117378/ /pubmed/21685063 http://dx.doi.org/10.1093/bioinformatics/btr201 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria Schweiger, Regev Linial, Michal Linial, Nathan Generative probabilistic models for protein–protein interaction networks—the biclique perspective |
title | Generative probabilistic models for protein–protein interaction networks—the biclique perspective |
title_full | Generative probabilistic models for protein–protein interaction networks—the biclique perspective |
title_fullStr | Generative probabilistic models for protein–protein interaction networks—the biclique perspective |
title_full_unstemmed | Generative probabilistic models for protein–protein interaction networks—the biclique perspective |
title_short | Generative probabilistic models for protein–protein interaction networks—the biclique perspective |
title_sort | generative probabilistic models for protein–protein interaction networks—the biclique perspective |
topic | Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117378/ https://www.ncbi.nlm.nih.gov/pubmed/21685063 http://dx.doi.org/10.1093/bioinformatics/btr201 |
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