Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation

Background: Biochemical investigations over the last decades have elucidated an increasingly complete image of the cellular metabolism. To derive a systems view for the regulation of the metabolism when cells adapt to environmental changes, whole genome gene expression profiles can be analysed. Moreover, utilising a network topology based on gene relationships may facilitate interpreting this vast amount of information, and extracting significant patterns within the networks. Results: Interpreting expression levels as pixels with grey value intensities and network topology as relationships between pixels, allows for an image-like representation of cellular metabolism. While the topology of a regular image is a lattice grid, biological networks demonstrate scale-free architecture and thus advanced image processing methods such as wavelet transforms cannot directly be applied. In the study reported here, one-dimensional enzyme-enzyme pairs were tracked to reveal sub-graphs of a biologic...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Gunnar Schramm, Marc Zapatka, Roland Eils, Rainer König
• Keywords: BMCBI 2007, Cellular Metabolism, Genome Gene Expression, Network Topology