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Using graph theory to analyze biological networks

dc.creatorPavlopoulos, G. A.en
dc.creatorSecrier, M.en
dc.creatorMoschopoulos, C. N.en
dc.creatorSoldatos, T. G.en
dc.creatorKossida, S.en
dc.creatorAerts, J.en
dc.creatorSchneider, R.en
dc.creatorBagos, P. G.en
dc.date.accessioned2015-11-23T10:45:16Z
dc.date.available2015-11-23T10:45:16Z
dc.date.issued2011
dc.identifier10.1186/1756-0381-4-10
dc.identifier.issn1756-0381
dc.identifier.urihttp://hdl.handle.net/11615/32113
dc.description.abstractUnderstanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system.en
dc.source.uri<Go to ISI>://WOS:000208761200010
dc.subjectbiological network clustering analysisen
dc.subjectgraph theoryen
dc.subjectnode rankingen
dc.subjectPROTEIN-INTERACTION NETWORKSen
dc.subjectEVOLUTIONARY GENETICS ANALYSISen
dc.subjectTRANSCRIPTIONAL REGULATORY NETWORKSen
dc.subjectESCHERICHIA-COLI K-12en
dc.subjectSMALL-WORLDen
dc.subjectNETWORKSen
dc.subjectMETABOLIC NETWORKSen
dc.subjectSACCHAROMYCES-CEREVISIAEen
dc.subjectPATHWAYen
dc.subjectANALYSISen
dc.subjectMARKUP LANGUAGEen
dc.subjectMICROARRAY DATAen
dc.subjectMathematical & Computational Biologyen
dc.titleUsing graph theory to analyze biological networksen
dc.typejournalArticleen


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