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Scalable Steady State Analysis of Boolean Biological Regulatory Networks
BACKGROUND: 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...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2009
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779454/ https://www.ncbi.nlm.nih.gov/pubmed/19956604 http://dx.doi.org/10.1371/journal.pone.0007992 |
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author | Ay, Ferhat Xu, Fei Kahveci, Tamer |
author_facet | Ay, Ferhat Xu, Fei Kahveci, Tamer |
author_sort | Ay, Ferhat |
collection | PubMed |
description | BACKGROUND: 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. METHODOLOGY: 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. CONCLUSIONS: 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. AVAILABILITY: Source code of this work is available at http://bioinformatics.cise.ufl.edu/palSteady.html twocolumnfalse] |
format | Text |
id | pubmed-2779454 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-27794542009-12-03 Scalable Steady State Analysis of Boolean Biological Regulatory Networks Ay, Ferhat Xu, Fei Kahveci, Tamer PLoS One Research Article BACKGROUND: 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. METHODOLOGY: 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. CONCLUSIONS: 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. AVAILABILITY: Source code of this work is available at http://bioinformatics.cise.ufl.edu/palSteady.html twocolumnfalse] Public Library of Science 2009-12-01 /pmc/articles/PMC2779454/ /pubmed/19956604 http://dx.doi.org/10.1371/journal.pone.0007992 Text en Ay et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Ay, Ferhat Xu, Fei Kahveci, Tamer Scalable Steady State Analysis of Boolean Biological Regulatory Networks |
title | 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_short | Scalable Steady State Analysis of Boolean Biological Regulatory Networks |
title_sort | scalable steady state analysis of boolean biological regulatory networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779454/ https://www.ncbi.nlm.nih.gov/pubmed/19956604 http://dx.doi.org/10.1371/journal.pone.0007992 |
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