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Article: Simulation study in Probabilistic Boolean Network models for genetic regulatory networks

TitleSimulation study in Probabilistic Boolean Network models for genetic regulatory networks
Authors
Issue Date2007
PublisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijdmb
Citation
International Journal Of Data Mining And Bioinformatics, 2007, v. 1 n. 3, p. 217-240 How to Cite?
AbstractProbabilistic Boolean Network (PBN) is widely used to model genetic regulatory networks. Evolution of the PBN is according to the transition probability matrix. Steady-state (long-run behaviour) analysis is a key aspect in studying the dynamics of genetic regulatory networks. In this paper, an efficient method to construct the sparse transition probability matrix is proposed, and the power method based on the sparse matrix-vector multiplication is applied to compute the steady-state probability distribution. Such methods provide a tool for us to study the sensitivity of the steady-state distribution to the influence of input genes, gene connections and Boolean networks. Simulation results based on a real network are given to illustrate the method and to demonstrate the steady-state analysis.
Persistent Identifierhttp://hdl.handle.net/10722/75458
ISSN
2015 Impact Factor: 0.528
2015 SCImago Journal Rankings: 0.289
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZhang, SQen_HK
dc.contributor.authorChing, WKen_HK
dc.contributor.authorNg, MKen_HK
dc.contributor.authorAkutsu, Ten_HK
dc.date.accessioned2010-09-06T07:11:18Z-
dc.date.available2010-09-06T07:11:18Z-
dc.date.issued2007en_HK
dc.identifier.citationInternational Journal Of Data Mining And Bioinformatics, 2007, v. 1 n. 3, p. 217-240en_HK
dc.identifier.issn1748-5673en_HK
dc.identifier.urihttp://hdl.handle.net/10722/75458-
dc.description.abstractProbabilistic Boolean Network (PBN) is widely used to model genetic regulatory networks. Evolution of the PBN is according to the transition probability matrix. Steady-state (long-run behaviour) analysis is a key aspect in studying the dynamics of genetic regulatory networks. In this paper, an efficient method to construct the sparse transition probability matrix is proposed, and the power method based on the sparse matrix-vector multiplication is applied to compute the steady-state probability distribution. Such methods provide a tool for us to study the sensitivity of the steady-state distribution to the influence of input genes, gene connections and Boolean networks. Simulation results based on a real network are given to illustrate the method and to demonstrate the steady-state analysis.en_HK
dc.languageengen_HK
dc.publisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijdmben_HK
dc.relation.ispartofInternational Journal of Data Mining and Bioinformaticsen_HK
dc.subject.meshAlgorithmsen_HK
dc.subject.meshAnimalsen_HK
dc.subject.meshComputational Biology - methodsen_HK
dc.subject.meshComputer Simulationen_HK
dc.subject.meshEvolution, Molecularen_HK
dc.subject.meshGene Expressionen_HK
dc.subject.meshGene Expression Regulationen_HK
dc.subject.meshGene Regulatory Networksen_HK
dc.subject.meshGenes, Regulatoren_HK
dc.subject.meshHumansen_HK
dc.subject.meshModels, Geneticen_HK
dc.subject.meshModels, Statisticalen_HK
dc.subject.meshMonte Carlo Methoden_HK
dc.subject.meshNeural Networks (Computer)en_HK
dc.subject.meshProbabilityen_HK
dc.subject.meshSignal Transduction - geneticsen_HK
dc.subject.meshTranscription Factors - geneticsen_HK
dc.titleSimulation study in Probabilistic Boolean Network models for genetic regulatory networksen_HK
dc.typeArticleen_HK
dc.identifier.emailChing, WK:wching@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1504/IJDMB.2007.011610en_HK
dc.identifier.pmid18399072-
dc.identifier.scopuseid_2-s2.0-34547825448en_HK
dc.identifier.hkuros124968en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34547825448&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume1en_HK
dc.identifier.issue3en_HK
dc.identifier.spage217en_HK
dc.identifier.epage240en_HK
dc.identifier.isiWOS:000247735600001-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridZhang, SQ=10143093600en_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridNg, MK=34571761900en_HK
dc.identifier.scopusauthoridAkutsu, T=7102080520en_HK

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