Reconciling gene expression data with known genome-scale regulatory network structures.
|Title||Reconciling gene expression data with known genome-scale regulatory network structures.|
|Publication Type||Journal Article|
|Year of Publication||2003|
|Authors||Herrgård MJ, Covert MW, Palsson BØ|
|Date Published||2003 Nov|
|Keywords||Computational Biology, Escherichia coli, Gene Expression Profiling, Gene Expression Regulation, Bacterial, Gene Expression Regulation, Fungal, Genes, Bacterial, Genes, Fungal, Genome, Bacterial, Genome, Fungal, Models, Genetic, Saccharomyces cerevisiae, Transcription, Genetic|
The availability of genome-scale gene expression data sets has initiated the development of methods that use this data to infer transcriptional regulatory networks. Alternatively, such regulatory network structures can be reconstructed based on annotated genome information, well-curated databases, and primary research literature. As a first step toward reconciling the two approaches, we examine the consistency between known genome-wide regulatory network structures and extensive gene expression data collections in Escherichia coli and Saccharomyces cerevisiae. By decomposing the regulatory network into a set of basic network elements, we can compute the local consistency of each instance of a particular type of network element. We find that the consistency of network elements is influenced by both structural features of the network such as the number of regulators acting on a target gene and by the functional classes of the genes involved in a particular element. Taken together, the approach presented allows us to define regulatory network subcomponents with a high degree of consistency between the network structure and gene expression data. The results suggest that targeted gene expression profiling data can be used to refine and expand particular subcomponents of known regulatory networks that are sufficiently decoupled from the rest of the network.
|Alternate Title||Genome Res.|