Testing biological network motif significance with exponential random graph models

Abstract Analysis of the structure of biological networks often uses statistical tests to establish the over-representation of motifs, which are thought to be important building blocks of such networks, related to their biological functions. However, there is disagreement as to the statistical signi...

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Autores principales:	Alex Stivala, Alessandro Lomi
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Motif Biological network Exponential random graph model ERGM Applied mathematics. Quantitative methods T57-57.97
Acceso en línea:	https://doaj.org/article/de6d7857a1b0409ea623c158788b6ca0
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spelling	oai:doaj.org-article:de6d7857a1b0409ea623c158788b6ca02021-11-28T12:10:04ZTesting biological network motif significance with exponential random graph models10.1007/s41109-021-00434-y2364-8228https://doaj.org/article/de6d7857a1b0409ea623c158788b6ca02021-11-01T00:00:00Zhttps://doi.org/10.1007/s41109-021-00434-yhttps://doaj.org/toc/2364-8228Abstract Analysis of the structure of biological networks often uses statistical tests to establish the over-representation of motifs, which are thought to be important building blocks of such networks, related to their biological functions. However, there is disagreement as to the statistical significance of these motifs, and there are potential problems with standard methods for estimating this significance. Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs. ERGMs were first introduced into the bioinformatics literature over 10 years ago but have had limited application to biological networks, possibly due to the practical difficulty of estimating model parameters. Advances in estimation algorithms now afford analysis of much larger networks in practical time. We illustrate the application of ERGM to both an undirected protein–protein interaction (PPI) network and directed gene regulatory networks. ERGM models indicate over-representation of triangles in the PPI network, and confirm results from previous research as to over-representation of transitive triangles (feed-forward loop) in an E. coli and a yeast regulatory network. We also confirm, using ERGMs, previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features.Alex StivalaAlessandro LomiSpringerOpenarticleMotifBiological networkExponential random graph modelERGMApplied mathematics. Quantitative methodsT57-57.97ENApplied Network Science, Vol 6, Iss 1, Pp 1-27 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Motif Biological network Exponential random graph model ERGM Applied mathematics. Quantitative methods T57-57.97
spellingShingle	Motif Biological network Exponential random graph model ERGM Applied mathematics. Quantitative methods T57-57.97 Alex Stivala Alessandro Lomi Testing biological network motif significance with exponential random graph models
description	Abstract Analysis of the structure of biological networks often uses statistical tests to establish the over-representation of motifs, which are thought to be important building blocks of such networks, related to their biological functions. However, there is disagreement as to the statistical significance of these motifs, and there are potential problems with standard methods for estimating this significance. Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs. ERGMs were first introduced into the bioinformatics literature over 10 years ago but have had limited application to biological networks, possibly due to the practical difficulty of estimating model parameters. Advances in estimation algorithms now afford analysis of much larger networks in practical time. We illustrate the application of ERGM to both an undirected protein–protein interaction (PPI) network and directed gene regulatory networks. ERGM models indicate over-representation of triangles in the PPI network, and confirm results from previous research as to over-representation of transitive triangles (feed-forward loop) in an E. coli and a yeast regulatory network. We also confirm, using ERGMs, previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features.
format	article
author	Alex Stivala Alessandro Lomi
author_facet	Alex Stivala Alessandro Lomi
author_sort	Alex Stivala
title	Testing biological network motif significance with exponential random graph models
title_short	Testing biological network motif significance with exponential random graph models
title_full	Testing biological network motif significance with exponential random graph models
title_fullStr	Testing biological network motif significance with exponential random graph models
title_full_unstemmed	Testing biological network motif significance with exponential random graph models
title_sort	testing biological network motif significance with exponential random graph models
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/de6d7857a1b0409ea623c158788b6ca0
work_keys_str_mv	AT alexstivala testingbiologicalnetworkmotifsignificancewithexponentialrandomgraphmodels AT alessandrolomi testingbiologicalnetworkmotifsignificancewithexponentialrandomgraphmodels
_version_	1718408170099965952

Testing biological network motif significance with exponential random graph models

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