Review
Using graph theory to analyze biological networks
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* Corresponding author: Georgios A Pavlopoulos pavlopou@embl.de
1 Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia, 35100, Greece
2 Faculty of Engineering - ESAT/SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001, Leuven-Heverlee, Belgium
3 Structural and Computational Biology Unit, EMBL, Meyerhofstrasse 1, 69117, Heidelberg, Germany
4 Department of Computer Engineering & Informatics, University of Patras, Rio, 6500, Patras, Greece
5 Bioinformatics & Medical Informatics Team, Biomedical Research Foundation, Academy of Athens, Soranou Efessiou 4, 11527, Athens, Greece
6 Life Biosystems GmbH, Belfortstrasse 2, 69117, Heidelberg, Germany
7 Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Limpertsberg, 162 A, avenue de la Faïencerie, L-1511 Luxembourg
BioData Mining 2011, 4:10 doi:10.1186/1756-0381-4-10
Published: 28 April 2011Abstract
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.