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Construction and analysis of protein–protein interaction networks

Protein–protein interactions form the basis for a vast majority of cellular events, including signal transduction and transcriptional regulation. It is now understood that the study of interactions between cellular macromolecules is fundamental to the understanding of biological systems. Interaction...

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Detalles Bibliográficos
Autor principal: Raman, Karthik
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2834675/
https://www.ncbi.nlm.nih.gov/pubmed/20334628
http://dx.doi.org/10.1186/1759-4499-2-2
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author Raman, Karthik
author_facet Raman, Karthik
author_sort Raman, Karthik
collection PubMed
description Protein–protein interactions form the basis for a vast majority of cellular events, including signal transduction and transcriptional regulation. It is now understood that the study of interactions between cellular macromolecules is fundamental to the understanding of biological systems. Interactions between proteins have been studied through a number of high-throughput experiments and have also been predicted through an array of computational methods that leverage the vast amount of sequence data generated in the last decade. In this review, I discuss some of the important computational methods for the prediction of functional linkages between proteins. I then give a brief overview of some of the databases and tools that are useful for a study of protein–protein interactions. I also present an introduction to network theory, followed by a discussion of the parameters commonly used in analysing networks, important network topologies, as well as methods to identify important network components, based on perturbations.
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spelling pubmed-28346752010-03-09 Construction and analysis of protein–protein interaction networks Raman, Karthik Autom Exp Review Protein–protein interactions form the basis for a vast majority of cellular events, including signal transduction and transcriptional regulation. It is now understood that the study of interactions between cellular macromolecules is fundamental to the understanding of biological systems. Interactions between proteins have been studied through a number of high-throughput experiments and have also been predicted through an array of computational methods that leverage the vast amount of sequence data generated in the last decade. In this review, I discuss some of the important computational methods for the prediction of functional linkages between proteins. I then give a brief overview of some of the databases and tools that are useful for a study of protein–protein interactions. I also present an introduction to network theory, followed by a discussion of the parameters commonly used in analysing networks, important network topologies, as well as methods to identify important network components, based on perturbations. BioMed Central 2010-02-15 /pmc/articles/PMC2834675/ /pubmed/20334628 http://dx.doi.org/10.1186/1759-4499-2-2 Text en Copyright ©2010 Raman; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Raman, Karthik
Construction and analysis of protein–protein interaction networks
title Construction and analysis of protein–protein interaction networks
title_full Construction and analysis of protein–protein interaction networks
title_fullStr Construction and analysis of protein–protein interaction networks
title_full_unstemmed Construction and analysis of protein–protein interaction networks
title_short Construction and analysis of protein–protein interaction networks
title_sort construction and analysis of protein–protein interaction networks
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2834675/
https://www.ncbi.nlm.nih.gov/pubmed/20334628
http://dx.doi.org/10.1186/1759-4499-2-2
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