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Exploring biological network structure with clustered random networks

BACKGROUND: Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that...

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Detalles Bibliográficos
Autores principales: Bansal, Shweta, Khandelwal, Shashank, Meyers, Lauren Ancel
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2801686/
https://www.ncbi.nlm.nih.gov/pubmed/20003212
http://dx.doi.org/10.1186/1471-2105-10-405
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author Bansal, Shweta
Khandelwal, Shashank
Meyers, Lauren Ancel
author_facet Bansal, Shweta
Khandelwal, Shashank
Meyers, Lauren Ancel
author_sort Bansal, Shweta
collection PubMed
description BACKGROUND: Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. RESULTS: Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks) provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics. Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. CONCLUSION: ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in unraveling the functional consequences of the structural properties of empirical biological systems and uncovering the mechanisms that drive these systems.
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spelling pubmed-28016862010-01-05 Exploring biological network structure with clustered random networks Bansal, Shweta Khandelwal, Shashank Meyers, Lauren Ancel BMC Bioinformatics Software BACKGROUND: Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. RESULTS: Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks) provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics. Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. CONCLUSION: ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in unraveling the functional consequences of the structural properties of empirical biological systems and uncovering the mechanisms that drive these systems. BioMed Central 2009-12-09 /pmc/articles/PMC2801686/ /pubmed/20003212 http://dx.doi.org/10.1186/1471-2105-10-405 Text en Copyright ©2009 Bansal et al; 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 Software
Bansal, Shweta
Khandelwal, Shashank
Meyers, Lauren Ancel
Exploring biological network structure with clustered random networks
title Exploring biological network structure with clustered random networks
title_full Exploring biological network structure with clustered random networks
title_fullStr Exploring biological network structure with clustered random networks
title_full_unstemmed Exploring biological network structure with clustered random networks
title_short Exploring biological network structure with clustered random networks
title_sort exploring biological network structure with clustered random networks
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2801686/
https://www.ncbi.nlm.nih.gov/pubmed/20003212
http://dx.doi.org/10.1186/1471-2105-10-405
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