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Conference Paper: A semi-tensor product approach for probabilistic boolean networks

TitleA semi-tensor product approach for probabilistic boolean networks
Authors
KeywordsBoolean Networks (BNs)
Semi-tensor Product Approach
Inverse Problem
Probabilistic Boolean Networks (PBNs)
Similar Matrices
Steady-state Distribution
Issue Date2014
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800515
Citation
The 8th International Conference on Systems Biology (ISB 2014), Qingdao, China, 24-27 October 2014. In Conference Proceedings, 2014, p. 85-90 How to Cite?
AbstractModeling genetic regulatory networks is an important issue in systems biology. Various models and mathematical formalisms have been proposed in the literature to solve the capture problem. The main purpose in this paper is to show that the transition matrix generated under semi-tensor product approach (Here we call it the probability structure matrix for simplicity) and the traditional approach (Transition probability matrix) are similar to each other. And we shall discuss three important problems in Probabilistic Boolean Networks (PBNs): the dynamic of a PBN, the steady-state probability distribution and the inverse problem. Numerical examples are given to show the validity of our theory. We shall give a brief introduction to semi-tensor and its application. After that we shall focus on the main results: to show the similarity of these two matrices. Since the semi-tensor approach gives a new way for interpreting a BN and therefore a PBN, we expect that advanced algorithms can be developed if one can describe the PBN through semi-tensor product approach.
Persistent Identifierhttp://hdl.handle.net/10722/207209
ISBN

 

DC FieldValueLanguage
dc.contributor.authorCheng, X-
dc.contributor.authorQiu, Y-
dc.contributor.authorHou, W-
dc.contributor.authorChing, WK-
dc.date.accessioned2014-12-19T03:22:17Z-
dc.date.available2014-12-19T03:22:17Z-
dc.date.issued2014-
dc.identifier.citationThe 8th International Conference on Systems Biology (ISB 2014), Qingdao, China, 24-27 October 2014. In Conference Proceedings, 2014, p. 85-90-
dc.identifier.isbn978-1-4799-7294-4-
dc.identifier.urihttp://hdl.handle.net/10722/207209-
dc.description.abstractModeling genetic regulatory networks is an important issue in systems biology. Various models and mathematical formalisms have been proposed in the literature to solve the capture problem. The main purpose in this paper is to show that the transition matrix generated under semi-tensor product approach (Here we call it the probability structure matrix for simplicity) and the traditional approach (Transition probability matrix) are similar to each other. And we shall discuss three important problems in Probabilistic Boolean Networks (PBNs): the dynamic of a PBN, the steady-state probability distribution and the inverse problem. Numerical examples are given to show the validity of our theory. We shall give a brief introduction to semi-tensor and its application. After that we shall focus on the main results: to show the similarity of these two matrices. Since the semi-tensor approach gives a new way for interpreting a BN and therefore a PBN, we expect that advanced algorithms can be developed if one can describe the PBN through semi-tensor product approach.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800515-
dc.relation.ispartofIEEE International Conference on Systems Biology-
dc.rightsIEEE International Conference on Systems Biology. Copyright © IEEE.-
dc.rights©2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBoolean Networks (BNs)-
dc.subjectSemi-tensor Product Approach-
dc.subjectInverse Problem-
dc.subjectProbabilistic Boolean Networks (PBNs)-
dc.subjectSimilar Matrices-
dc.subjectSteady-state Distribution-
dc.titleA semi-tensor product approach for probabilistic boolean networksen_US
dc.typeConference_Paperen_US
dc.identifier.emailChing, WK: wching@hku.hk-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ISB.2014.6990737-
dc.identifier.hkuros241887-
dc.identifier.spage85-
dc.identifier.epage90-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 141219-

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