Dynamic properties of network motifs contribute to biological network organization.
Biological networks, such as those describing gene regulation, signal transduction, and neural synapses, are representations of large-scale dynamic systems. Discovery of organizing principles of biological networks can be enhanced by embracing the notion that there is a deep interplay between networ...
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Format: | Article |
Language: | English |
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Public Library of Science (PLoS)
2005-11-01
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Series: | PLoS Biology |
Online Access: | http://europepmc.org/articles/PMC1239925?pdf=render |
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author | Robert J Prill Pablo A Iglesias Andre Levchenko |
author_facet | Robert J Prill Pablo A Iglesias Andre Levchenko |
author_sort | Robert J Prill |
collection | DOAJ |
description | Biological networks, such as those describing gene regulation, signal transduction, and neural synapses, are representations of large-scale dynamic systems. Discovery of organizing principles of biological networks can be enhanced by embracing the notion that there is a deep interplay between network structure and system dynamics. Recently, many structural characteristics of these non-random networks have been identified, but dynamical implications of the features have not been explored comprehensively. We demonstrate by exhaustive computational analysis that a dynamical property--stability or robustness to small perturbations--is highly correlated with the relative abundance of small subnetworks (network motifs) in several previously determined biological networks. We propose that robust dynamical stability is an influential property that can determine the non-random structure of biological networks. |
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format | Article |
id | doaj.art-fb126de9eb7a4c5e9733c49528314228 |
institution | Directory Open Access Journal |
issn | 1544-9173 1545-7885 |
language | English |
last_indexed | 2024-12-19T10:36:40Z |
publishDate | 2005-11-01 |
publisher | Public Library of Science (PLoS) |
record_format | Article |
series | PLoS Biology |
spelling | doaj.art-fb126de9eb7a4c5e9733c495283142282022-12-21T20:25:36ZengPublic Library of Science (PLoS)PLoS Biology1544-91731545-78852005-11-01311e34310.1371/journal.pbio.0030343Dynamic properties of network motifs contribute to biological network organization.Robert J PrillPablo A IglesiasAndre LevchenkoBiological networks, such as those describing gene regulation, signal transduction, and neural synapses, are representations of large-scale dynamic systems. Discovery of organizing principles of biological networks can be enhanced by embracing the notion that there is a deep interplay between network structure and system dynamics. Recently, many structural characteristics of these non-random networks have been identified, but dynamical implications of the features have not been explored comprehensively. We demonstrate by exhaustive computational analysis that a dynamical property--stability or robustness to small perturbations--is highly correlated with the relative abundance of small subnetworks (network motifs) in several previously determined biological networks. We propose that robust dynamical stability is an influential property that can determine the non-random structure of biological networks.http://europepmc.org/articles/PMC1239925?pdf=render |
spellingShingle | Robert J Prill Pablo A Iglesias Andre Levchenko Dynamic properties of network motifs contribute to biological network organization. PLoS Biology |
title | Dynamic properties of network motifs contribute to biological network organization. |
title_full | Dynamic properties of network motifs contribute to biological network organization. |
title_fullStr | Dynamic properties of network motifs contribute to biological network organization. |
title_full_unstemmed | Dynamic properties of network motifs contribute to biological network organization. |
title_short | Dynamic properties of network motifs contribute to biological network organization. |
title_sort | dynamic properties of network motifs contribute to biological network organization |
url | http://europepmc.org/articles/PMC1239925?pdf=render |
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