Scalable steady state analysis of Boolean biological regulatory networks.

<h4>Background</h4>Computing the long term behavior of regulatory and signaling networks is critical in understanding how biological functions take place in organisms. Steady states of these networks determine the activity levels of individual entities in the long run. Identifying all th...

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Autores principales:	Ferhat Ay, Fei Xu, Tamer Kahveci
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f14f4a7e07354df5839000747f8f9d97
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spelling	oai:doaj.org-article:f14f4a7e07354df5839000747f8f9d972021-11-25T06:27:42ZScalable steady state analysis of Boolean biological regulatory networks.1932-620310.1371/journal.pone.0007992https://doaj.org/article/f14f4a7e07354df5839000747f8f9d972009-12-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19956604/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Computing the long term behavior of regulatory and signaling networks is critical in understanding how biological functions take place in organisms. Steady states of these networks determine the activity levels of individual entities in the long run. Identifying all the steady states of these networks is difficult due to the state space explosion problem.<h4>Methodology</h4>In this paper, we propose a method for identifying all the steady states of Boolean regulatory and signaling networks accurately and efficiently. We build a mathematical model that allows pruning a large portion of the state space quickly without causing any false dismissals. For the remaining state space, which is typically very small compared to the whole state space, we develop a randomized traversal method that extracts the steady states. We estimate the number of steady states, and the expected behavior of individual genes and gene pairs in steady states in an online fashion. Also, we formulate a stopping criterion that terminates the traversal as soon as user supplied percentage of the results are returned with high confidence.<h4>Conclusions</h4>This method identifies the observed steady states of boolean biological networks computationally. Our algorithm successfully reported the G1 phases of both budding and fission yeast cell cycles. Besides, the experiments suggest that this method is useful in identifying co-expressed genes as well. By analyzing the steady state profile of Hedgehog network, we were able to find the highly co-expressed gene pair GL1-SMO together with other such pairs.<h4>Availability</h4>Source code of this work is available at http://bioinformatics.cise.ufl.edu/palSteady.html twocolumnfalse].Ferhat AyFei XuTamer KahveciPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 4, Iss 12, p e7992 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ferhat Ay Fei Xu Tamer Kahveci Scalable steady state analysis of Boolean biological regulatory networks.
description	<h4>Background</h4>Computing the long term behavior of regulatory and signaling networks is critical in understanding how biological functions take place in organisms. Steady states of these networks determine the activity levels of individual entities in the long run. Identifying all the steady states of these networks is difficult due to the state space explosion problem.<h4>Methodology</h4>In this paper, we propose a method for identifying all the steady states of Boolean regulatory and signaling networks accurately and efficiently. We build a mathematical model that allows pruning a large portion of the state space quickly without causing any false dismissals. For the remaining state space, which is typically very small compared to the whole state space, we develop a randomized traversal method that extracts the steady states. We estimate the number of steady states, and the expected behavior of individual genes and gene pairs in steady states in an online fashion. Also, we formulate a stopping criterion that terminates the traversal as soon as user supplied percentage of the results are returned with high confidence.<h4>Conclusions</h4>This method identifies the observed steady states of boolean biological networks computationally. Our algorithm successfully reported the G1 phases of both budding and fission yeast cell cycles. Besides, the experiments suggest that this method is useful in identifying co-expressed genes as well. By analyzing the steady state profile of Hedgehog network, we were able to find the highly co-expressed gene pair GL1-SMO together with other such pairs.<h4>Availability</h4>Source code of this work is available at http://bioinformatics.cise.ufl.edu/palSteady.html twocolumnfalse].
format	article
author	Ferhat Ay Fei Xu Tamer Kahveci
author_facet	Ferhat Ay Fei Xu Tamer Kahveci
author_sort	Ferhat Ay
title	Scalable steady state analysis of Boolean biological regulatory networks.
title_short	Scalable steady state analysis of Boolean biological regulatory networks.
title_full	Scalable steady state analysis of Boolean biological regulatory networks.
title_fullStr	Scalable steady state analysis of Boolean biological regulatory networks.
title_full_unstemmed	Scalable steady state analysis of Boolean biological regulatory networks.
title_sort	scalable steady state analysis of boolean biological regulatory networks.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/f14f4a7e07354df5839000747f8f9d97
work_keys_str_mv	AT ferhatay scalablesteadystateanalysisofbooleanbiologicalregulatorynetworks AT feixu scalablesteadystateanalysisofbooleanbiologicalregulatorynetworks AT tamerkahveci scalablesteadystateanalysisofbooleanbiologicalregulatorynetworks
_version_	1718413716704198656

Scalable steady state analysis of Boolean biological regulatory networks.

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