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Briefings in Bioinformatics Advance Access originally published online on July 30, 2006
Briefings in Bioinformatics 2006 7(3):243-255; doi:10.1093/bib/bbl022
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© The Author 2006. Published by Oxford University Press. For Permissions, please email: journals.permissions@oxfordjournals.org

Graph-based methods for analysing networks in cell biology

Tero Aittokallio and Benno Schwikowski

Corresponding author. Tero Aittokallio, Systems Biology Group, Institut Pasteur, 25-28 Rue du Dr Roux, FR-75724 Paris, France. Tel: +33 1 4061 3784; Fax: +33 1 4061 3704; E-mail: teanai{at}pasteur.fr

Availability of large-scale experimental data for cell biology is enabling computational methods to systematically model the behaviour of cellular networks. This review surveys the recent advances in the field of graph-driven methods for analysing complex cellular networks. The methods are outlined on three levels of increasing complexity, ranging from methods that can characterize global or local structural properties of networks to methods that can detect groups of interconnected nodes, called motifs or clusters, potentially involved in common elementary biological functions. We also briefly summarize recent approaches to data integration and network inference through graph-based formalisms. Finally, we highlight some challenges in the field and offer our personal view of the key future trends and developments in graph-based analysis of large-scale datasets.

Keywords: graph algorithms, data integration, cellular networks, protein–protein interactions, transcriptional regulatory networks, network modularity


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