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Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli.


ABSTRACT: 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 fewer 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.

SUBMITTER: Marr C 

PROVIDER: S-EPMC2895633 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli.

Marr Carsten C   Theis Fabian J FJ   Liebovitch Larry S LS   Hütt Marc-Thorsten MT  

PLoS computational biology 20100701


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 bacteri  ...[more]

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