Mol Syst Biol. 2: 2006.0030 The effect of transcription factors on human gene expression can now be quantified owing to a new computational approach. The method was successfully applied to the cases of the cell cycle program, and for liver‐specific gene expression. Transcriptional regulation of gene expression plays a major role in the acquisition of cell identity during embryogenesis and shapes cellular response to various stimuli. Understanding how transcription regulatory networks are encoded in the genome represents one of the major challenges in contemporary genomics. The genome era opened the door to the investigation of a systems‐map of transcriptional regulation. A static view of the map is provided by the wiring scheme of the network, which is encoded by combinations of cis ‐regulatory sequences (or motifs) within genomic regulatory regions. On the other hand, probing the transcriptome with expression microarrays provides snapshots of the network output and reveals its dynamics. A pioneering study that systematically established a link between sequence and expression (Tavazoie et al , 1999) was based on the notion that coexpressed transcripts should also be coregulated. A search for common motifs in promoters of coclustered genes revealed shared sequence motifs among similarly expressed genes (Tavazoie et al , 1999). While very effective, the method has shortcomings: deciding on the number, size, and tightness of clusters is not straightforward since we do not know the degree of coherence of genes that belong to the same transcriptional program a priori . Consequently, correlation between clusters and motifs is not a one‐to‐one relationship (Bussemaker et al , 2001); often many genes in a cluster do not contain any known motif, and not all genes that contain a motif belong to the cluster from which it was derived. Furthermore, motif combinatorics could not be easily …