%0 Journal Article
%T Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of Escherichia coli
%A Gunnar Schramm
%A Marc Zapatka
%A Roland Eils
%A Rainer K£¿nig
%J BMC Bioinformatics
%D 2007
%I BioMed Central
%R 10.1186/1471-2105-8-149
%X 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 biological interaction network which showed significant adaptations to a changing environment. As a case study, the response of the hetero-fermentative bacterium E. coli to oxygen deprivation was investigated. With our novel method, we detected, as expected, an up-regulation in the pathways of hexose nutrients up-take and metabolism and formate fermentation. Furthermore, our approach revealed a down-regulation in iron processing as well as the up-regulation of the histidine biosynthesis pathway. The latter may reflect an adaptive response of E. coli against an increasingly acidic environment due to the excretion of acidic products during anaerobic growth in a batch culture.Based on microarray expression profiling data of prokaryotic cells exposed to fundamental treatment changes, our novel technique proved to extract system changes for a rather broad spectrum of the biochemical network.Over the last decades our understanding of cellular metabolism has increased considerably [1], in particular for less complex organisms such as Escherichia coli [2-4]. The gained knowledge includes cellular adaptation programs that respond to changing environmental conditions such as nutrient excess and starvation [5]. Current microarray technology allows for the investigation of all genes of an organism under various conditions, resulting in the generation of a massive amount of expression data. One of the greatest challenge we are faced with is to then analyse the data as a whole and extract
%U http://www.biomedcentral.com/1471-2105/8/149