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Algorithms and Complexity Analyses for Control of Singleton Attractors in Boolean Networks

A Boolean network (BN) is a mathematical model of genetic networks. We propose several algorithms for control of singleton attractors in BN. We theoretically estimate the average-case time complexities of the proposed algorithms, and confirm them by computer experiments. The results suggest the impo...

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Detalles Bibliográficos
Autores principales: Hayashida, Morihiro, Tamura, Takeyuki, Akutsu, Tatsuya, Zhang, Shu-Qin, Ching, Wai-Ki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3171397/
https://www.ncbi.nlm.nih.gov/pubmed/18795107
http://dx.doi.org/10.1155/2008/521407
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author Hayashida, Morihiro
Tamura, Takeyuki
Akutsu, Tatsuya
Zhang, Shu-Qin
Ching, Wai-Ki
author_facet Hayashida, Morihiro
Tamura, Takeyuki
Akutsu, Tatsuya
Zhang, Shu-Qin
Ching, Wai-Ki
author_sort Hayashida, Morihiro
collection PubMed
description A Boolean network (BN) is a mathematical model of genetic networks. We propose several algorithms for control of singleton attractors in BN. We theoretically estimate the average-case time complexities of the proposed algorithms, and confirm them by computer experiments. The results suggest the importance of gene ordering. Especially, setting internal nodes ahead yields shorter computational time than setting external nodes ahead in various types of algorithms. We also present a heuristic algorithm which does not look for the optimal solution but for the solution whose computational time is shorter than that of the exact algorithms.
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spelling pubmed-31713972011-09-13 Algorithms and Complexity Analyses for Control of Singleton Attractors in Boolean Networks Hayashida, Morihiro Tamura, Takeyuki Akutsu, Tatsuya Zhang, Shu-Qin Ching, Wai-Ki EURASIP J Bioinform Syst Biol Research Article A Boolean network (BN) is a mathematical model of genetic networks. We propose several algorithms for control of singleton attractors in BN. We theoretically estimate the average-case time complexities of the proposed algorithms, and confirm them by computer experiments. The results suggest the importance of gene ordering. Especially, setting internal nodes ahead yields shorter computational time than setting external nodes ahead in various types of algorithms. We also present a heuristic algorithm which does not look for the optimal solution but for the solution whose computational time is shorter than that of the exact algorithms. Springer 2008-06-12 /pmc/articles/PMC3171397/ /pubmed/18795107 http://dx.doi.org/10.1155/2008/521407 Text en Copyright © 2008 Morihiro Hayashida et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Hayashida, Morihiro
Tamura, Takeyuki
Akutsu, Tatsuya
Zhang, Shu-Qin
Ching, Wai-Ki
Algorithms and Complexity Analyses for Control of Singleton Attractors in Boolean Networks
title Algorithms and Complexity Analyses for Control of Singleton Attractors in Boolean Networks
title_full Algorithms and Complexity Analyses for Control of Singleton Attractors in Boolean Networks
title_fullStr Algorithms and Complexity Analyses for Control of Singleton Attractors in Boolean Networks
title_full_unstemmed Algorithms and Complexity Analyses for Control of Singleton Attractors in Boolean Networks
title_short Algorithms and Complexity Analyses for Control of Singleton Attractors in Boolean Networks
title_sort algorithms and complexity analyses for control of singleton attractors in boolean networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3171397/
https://www.ncbi.nlm.nih.gov/pubmed/18795107
http://dx.doi.org/10.1155/2008/521407
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