title: Using graph theory to analyze biological networks.
abstract: Understanding 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.
authors.affiliation: Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia, 35100, Greece. pavlopou@embl.de.
authors.forename: Georgios A
authors.initials: GA
authors.lastname: Pavlopoulos
authors.forename: Maria
authors.initials: M
authors.lastname: Secrier
authors.forename: Charalampos N
authors.initials: CN
authors.lastname: Moschopoulos
authors.forename: Theodoros G
authors.initials: TG
authors.lastname: Soldatos
authors.forename: Sophia
authors.initials: S
authors.lastname: Kossida
authors.forename: Jan
authors.initials: J
authors.lastname: Aerts
authors.forename: Reinhard
authors.initials: R
authors.lastname: Schneider
authors.forename: Pantelis G
authors.initials: PG
authors.lastname: Bagos