Inferring Regulatory Interactions between Transcriptional Factors and Genes by Propagating Known Regulatory Links

Determining transcriptional regulatory networks has been one of the most important goals in the field of functional genomics. Despite the recent advances in experimental techniques, complementary computational techniques have lagged behind. We introduce a novel computational methodology that uses DNA microarray data and known regulatory interactions to predict unknown regulatory interactions. Our method involves three steps: in the training stage, we utilize network component analysis (NCA) (Liao et al., 2003; Kao et al., 2004; Boscolo et al., 2005) to reconstruct the hidden activity profiles of transcriptional factors (TF); then we cluster TFs into functional modules according to the similarities of their reconstructed activity profiles; in the prediction stage, we infer additional TF-gene regulatory links by selecting TF profiles that best interpret genes expression profiles via a linear model. We applied the methodology to a gene expression dataset of bacterium Escherichia coli, whose partial TF-gene regulatory structure is obtained from RegulonDB (Salgado et al., 2004). Cross-validation results show that when the profiles of all TFs regulating a gene are reconstructed from NCA, we could identify 36% of the TF-gene interactions, and the prediction accuracy is 89%. And when the profiles of partial (50% or more) TFs regulating a gene can be reconstructed, we can identify 14% of the TF-gene interactions, and the accuracy rate is 69%. These represent some of the best known accuracy and coverage statistics reported in the literature so far