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Briefings in Bioinformatics Advance Access published online on July 30, 2006
Briefings in Bioinformatics, 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
Received May 4, 2006
Accepted June 27, 2006

Original Papers

Graph-based methods for analysing networks in cell biology

Tero Aittokallio and Benno Schwikowski


  Abstract

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.

Tero Aittokallio is a postdoctoral researcher in the Systems Biology Group at Institut Pasteur. His research interests include mathematical modelling and machine learning methods, especially with applications to data mining and data integration problems.

Benno Schwikowski leads the Systems Biology Group at Institut Pasteur in Paris. He is interested in developing measurement technology and experimental-computational approaches to problems in cell biology and infectious disease.


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