Biology – Quantitative Biology – Molecular Networks
Scientific paper
2010-05-24
Biology
Quantitative Biology
Molecular Networks
14 pages, 8 figures, to be published in PLoS Computational Biology
Scientific paper
The set of regulatory interactions between genes, mediated by transcription factors, forms a species' transcriptional regulatory network (TRN). By comparing this network with measured gene expression data one can identify functional properties of the TRN and gain general insight into transcriptional control. We define the subnet of a node as the subgraph consisting of all nodes topologically downstream of the node, including itself. Using a large set of microarray expression data of the bacterium Escherichia coli, we find that the gene expression in different subnets exhibits a structured pattern in response to environmental changes and genotypic mutation. Subnets with less changes in their expression pattern have a higher fraction of feed-forward loop motifs and a lower fraction of small RNA targets within them. Our study implies that the TRN consists of several scales of regulatory organization: 1) subnets with more varying gene expression controlled by both transcription factors and post-transcriptional RNA regulation, and 2) subnets with less varying gene expression having more feed-forward loops and less post-transcriptional RNA regulation.
Hütt Marc-Thorsten
Liebovitch Larry S.
Marr Carsten
Theis Fabian J.
No associations
LandOfFree
Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-119617