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Bioinformatics 20(10) © Oxford University Press 2004; all rights reserved.

GCB Conference Paper

New methods for joint analysis of biological networks and expression data

Florian Sohler 1,*, Daniel Hanisch 2 and Ralf Zimmer 1

1 Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstraße 17, 80333 München, Germany and 2 Institute for Algorithms and Scientific Computing (SCAI), Fraunhofer Gesellschaft, Schloss Birlinghoven, 53754 Sankt Augustin, Germany

Received on October 12, 2003; accepted on February 3, 2004

Summary: Biological networks, such as protein interaction, regulatory or metabolic networks, derived from public databases, biological experiments or text mining can be useful for the analysis of high-throughput experimental data. We present two algorithms embedded in the ToPNet application that show promising performance in analyzing expression data in the context of such networks. First, the Significant Area Search algorithm detects subnetworks consisting of significantly regulated genes. These subnetworks often provide hints on which biological processes are affected in the measured conditions. Second, Pathway Queries allow detection of networks including molecules that are not necessarily significantly regulated, such as transcription factors or signaling proteins. Moreover, using these queries, the user can formulate biological hypotheses and check their validity with respect to experimental data. All resulting networks and pathways can be explored further using the interactive analysis tools provided by ToPNet program.

Contact: florian.sohler{at}ifi.lmu.edu

* To whom correspondence should be addressed.


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