Transcriptional regulatory networks via gene ontology and expression data.

Transcriptional regulatory network (TRN) discovery using information from a single source does not seem feasible due to lack of sufficient information, resulting in the construction of spurious or incomplete TRNs. A methodology, TRND, that integrates a preliminary TRN, gene expression data and gene ontology is developed to discover TRNs. The method is applied to a comprehensive set of expression data on B cell and a preliminary TRN that included 1,335 genes, 443 transcription factors (TFs) and 4032 gene/TF interactions. Predictions were obtained for 443 TFs and 9,589 genes. 14,616 of 4,247,927 possible gene/TF interactions scored higher than the imposed threshold. Results for three TFs, E2F-4, p130 and c-Myc, were examined in more detail to assess the accuracy of the integrated methodology. Although the training sets for E2F-4 and p130 were rather limited, the activities of these two TFs were found to be highly correlated and a large set of coregulated genes is predicted. These predictions were confirmed with published experimental results not used in the training set. A similar test was run for the c-Myc TF using the comprehensive resource www.myccancergene.org. In addition, correlations between expression of genes that encode TFs and TF activities were calculated and showed that the assumption of TF activity correlates with encoding gene expression might be misleading. The constructed B cell TRN, and scores for individual methodologies and the integrated approach are available at systemsbiology.indiana.edu/trndresults.

Transcriptional regulatory network discovery via multiple method integration: application to e. coli K12.

Transcriptional regulatory network (TRN) discovery from one method (e.g. microarray analysis, gene ontology, phylogenic similarity) does not seem feasible due to lack of sufficient information, resulting in the construction of spurious or incomplete TRNs. We develop a methodology, TRND, that integrates a preliminary TRN, microarray data, gene ontology and phylogenic similarity to accurately discover TRNs and apply the method to E. coli K12. The approach can easily be extended to include other methodologies. Although gene ontology and phylogenic similarity have been used in the context of gene-gene networks, we show that more information can be extracted when gene-gene scores are transformed to gene-transcription factor (TF) scores using a preliminary TRN. This seems to be preferable over the construction of gene-gene interaction networks in light of the observed fact that gene expression and activity of a TF made of a component encoded by that gene is often out of phase. TRND multi-method integration is found to be facilitated by the use of a Bayesian framework for each method derived from its individual scoring measure and a training set of gene/TF regulatory interactions. The TRNs we construct are in better agreement with microarray data. The number of gene/TF interactions we discover is actually double that of existing networks.