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Article: On finite-horizon control of genetic regulatory networks with multiple hard-constraints

TitleOn finite-horizon control of genetic regulatory networks with multiple hard-constraints
Authors
Issue Date2010
PublisherBioMed Central Ltd.. The Journal's web site is located at http://www.biomedcentral.com/bmcsystbiol/
Citation
Bmc Systems Biology, 2010, v. 4 SUPPL. 2 How to Cite?
AbstractBackground: Probabilistic Boolean Networks (PBNs) provide a convenient tool for studying genetic regulatory networks. There are three major approaches to develop intervention strategies: (1) resetting the state of the PBN to a desirable initial state and letting the network evolve from there, (2) changing the steady-state behavior of the genetic network by minimally altering the rule-based structure and (3) manipulating external control variables which alter the transition probabilities of the network and therefore desirably affects the dynamic evolution. Many literatures study various types of external control problems, with a common drawback of ignoring the number of times that external control(s) can be applied.Results: This paper studies the intervention problem by manipulating multiple external controls in a finite time interval in a PBN. The maximum numbers of times that each control method can be applied are given. We treat the problem as an optimization problem with multi-constraints. Here we introduce an algorithm, the "Reserving Place Algorithm'', to find all optimal intervention strategies. Given a fixed number of times that a certain control method is applied, the algorithm can provide all the sub-optimal control policies. Theoretical analysis for the upper bound of the computational cost is also given. We also develop a heuristic algorithm based on Genetic Algorithm, to find the possible optimal intervention strategy for networks of large size. . Conclusions: Studying the finite-horizon control problem with multiple hard-constraints is meaningful. The problem proposed is NP-hard. The Reserving Place Algorithm can provide more than one optimal intervention strategies if there are. Moreover, the algorithm can find all the sub-optimal control strategies corresponding to the number of times that certain control method is conducted. To speed up the computational time, a heuristic algorithm based on Genetic Algorithm is proposed for genetic networks of large size. © 2010 Wai-Ki et al; licensee BioMed Central Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/124815
ISSN
2015 Impact Factor: 2.213
2015 SCImago Journal Rankings: 1.493
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
Greek Ministry of Education21768
NIHDK059389
Greek National Fellowship Foundation
Varigenix, Inc.
NCIU01CA105417
Funding Information:

TG was supported by grants from the Greek Ministry of Education (HPAK Lambda EITO Sigma grant 21768), while MEB was supported by NIH grant DK059389. PP was a recipient of a fellowship from the Greek National Fellowship Foundation. RES is supported by Varigenix, Inc. in his role as company president. RWW and GeneNetwork are supported by NCI grant U01CA105417. The authors acknowledge Dr. Sizhi Gao for preparation and validation of the P2P-R (1-760) plasmid construct and Drs. David Lonard and Bert O'Malley for performing selected confirmatory experiments.

References

 

DC FieldValueLanguage
dc.contributor.authorYang, Cen_HK
dc.contributor.authorWaiKi, Cen_HK
dc.contributor.authorNamKiu, Ten_HK
dc.contributor.authorHoYin, Len_HK
dc.date.accessioned2010-10-31T10:55:43Z-
dc.date.available2010-10-31T10:55:43Z-
dc.date.issued2010en_HK
dc.identifier.citationBmc Systems Biology, 2010, v. 4 SUPPL. 2en_HK
dc.identifier.issn1752-0509en_HK
dc.identifier.urihttp://hdl.handle.net/10722/124815-
dc.description.abstractBackground: Probabilistic Boolean Networks (PBNs) provide a convenient tool for studying genetic regulatory networks. There are three major approaches to develop intervention strategies: (1) resetting the state of the PBN to a desirable initial state and letting the network evolve from there, (2) changing the steady-state behavior of the genetic network by minimally altering the rule-based structure and (3) manipulating external control variables which alter the transition probabilities of the network and therefore desirably affects the dynamic evolution. Many literatures study various types of external control problems, with a common drawback of ignoring the number of times that external control(s) can be applied.Results: This paper studies the intervention problem by manipulating multiple external controls in a finite time interval in a PBN. The maximum numbers of times that each control method can be applied are given. We treat the problem as an optimization problem with multi-constraints. Here we introduce an algorithm, the "Reserving Place Algorithm'', to find all optimal intervention strategies. Given a fixed number of times that a certain control method is applied, the algorithm can provide all the sub-optimal control policies. Theoretical analysis for the upper bound of the computational cost is also given. We also develop a heuristic algorithm based on Genetic Algorithm, to find the possible optimal intervention strategy for networks of large size. . Conclusions: Studying the finite-horizon control problem with multiple hard-constraints is meaningful. The problem proposed is NP-hard. The Reserving Place Algorithm can provide more than one optimal intervention strategies if there are. Moreover, the algorithm can find all the sub-optimal control strategies corresponding to the number of times that certain control method is conducted. To speed up the computational time, a heuristic algorithm based on Genetic Algorithm is proposed for genetic networks of large size. © 2010 Wai-Ki et al; licensee BioMed Central Ltd.en_HK
dc.languageengen_HK
dc.publisherBioMed Central Ltd.. The Journal's web site is located at http://www.biomedcentral.com/bmcsystbiol/en_HK
dc.relation.ispartofBMC Systems Biologyen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsB M C Systems Biology. Copyright © BioMed Central Ltd..-
dc.titleOn finite-horizon control of genetic regulatory networks with multiple hard-constraintsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1752-0509&volume=4 suppl. 2&spage=S14&epage=&date=2010&atitle=On+finite-horizon+control+of+genetic+regulatory+networks+with+multiple+hard-constraints-
dc.identifier.emailNamKiu, T:nktsing@hku.hken_HK
dc.identifier.authorityNamKiu, T=rp00794en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1186/1752-0509-4-14en_HK
dc.identifier.pmid20840728-
dc.identifier.pmcidPMC2982688-
dc.identifier.scopuseid_2-s2.0-77956796916en_HK
dc.identifier.hkuros181189en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77956796916&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4en_HK
dc.identifier.issueSUPPL. 2en_HK
dc.identifier.spageS14-
dc.identifier.eissn1752-0509-
dc.identifier.isiWOS:000275908100001-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYang, C=36490506100en_HK
dc.identifier.scopusauthoridWaiKi, C=36490565300en_HK
dc.identifier.scopusauthoridNamKiu, T=6602663351en_HK
dc.identifier.scopusauthoridHoYin, L=36489541700en_HK

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