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Article: Analysis of the impact degree distribution in metabolic networks using branching process approximation

TitleAnalysis of the impact degree distribution in metabolic networks using branching process approximation
Authors
KeywordsBranching process
Cascading failure
Metabolic network
Power law
Issue Date2012
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/physa
Citation
Physica A: Statistical Mechanics And Its Applications, 2012, v. 391 n. 1-2, p. 379-387 How to Cite?
AbstractTheoretical frameworks to estimate the tolerance of metabolic networks to various failures are important to evaluate the robustness of biological complex systems in systems biology. In this paper, we focus on a measure for robustness in metabolic networks, namely, the impact degree, and propose an approximation method to predict the probability distribution of impact degrees from metabolic network structures using the theory of branching process. We demonstrate the relevance of this method by testing it on real-world metabolic networks. Although the approximation method possesses a few limitations, it may be a powerful tool for evaluating metabolic robustness. © 2011 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/143756
ISSN
2015 Impact Factor: 1.785
2015 SCImago Journal Rankings: 0.738
ISI Accession Number ID
Funding AgencyGrant Number
JST PRESTO
JSPS/INSERM
MEXT, Japan22650045
Funding Information:

This work was partially supported by the JST PRESTO program. TA, IT and JPV were partially supported by a JSPS/INSERM grant. TA was partially supported by Grant-in-Aid #22650045 from MEXT, Japan.

References

 

DC FieldValueLanguage
dc.contributor.authorTakemoto, Ken_HK
dc.contributor.authorTamura, Ten_HK
dc.contributor.authorCong, Yen_HK
dc.contributor.authorChing, WKen_HK
dc.contributor.authorVert, JPen_HK
dc.contributor.authorAkutsu, Ten_HK
dc.date.accessioned2011-12-21T08:53:42Z-
dc.date.available2011-12-21T08:53:42Z-
dc.date.issued2012en_HK
dc.identifier.citationPhysica A: Statistical Mechanics And Its Applications, 2012, v. 391 n. 1-2, p. 379-387en_HK
dc.identifier.issn0378-4371en_HK
dc.identifier.urihttp://hdl.handle.net/10722/143756-
dc.description.abstractTheoretical frameworks to estimate the tolerance of metabolic networks to various failures are important to evaluate the robustness of biological complex systems in systems biology. In this paper, we focus on a measure for robustness in metabolic networks, namely, the impact degree, and propose an approximation method to predict the probability distribution of impact degrees from metabolic network structures using the theory of branching process. We demonstrate the relevance of this method by testing it on real-world metabolic networks. Although the approximation method possesses a few limitations, it may be a powerful tool for evaluating metabolic robustness. © 2011 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/physaen_HK
dc.relation.ispartofPhysica A: Statistical Mechanics and its Applicationsen_HK
dc.subjectBranching processen_HK
dc.subjectCascading failureen_HK
dc.subjectMetabolic networken_HK
dc.subjectPower lawen_HK
dc.titleAnalysis of the impact degree distribution in metabolic networks using branching process approximationen_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.doi10.1016/j.physa.2011.08.011en_HK
dc.identifier.scopuseid_2-s2.0-80054041784en_HK
dc.identifier.hkuros197805en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80054041784&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume391en_HK
dc.identifier.issue1-2en_HK
dc.identifier.spage379en_HK
dc.identifier.epage387en_HK
dc.identifier.isiWOS:000297230700041-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridTakemoto, K=35270356700en_HK
dc.identifier.scopusauthoridTamura, T=13609056800en_HK
dc.identifier.scopusauthoridCong, Y=35185897700en_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridVert, JP=6603053445en_HK
dc.identifier.scopusauthoridAkutsu, T=7102080520en_HK

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