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

ABSTRACT:

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 architectu

SUBMITTER: Schramm G 

PROVIDER: S-EPMC1884177 | biostudies-literature | 2007 May

REPOSITORIES: biostudies-literature