A mechanistic understanding of eukaryotic gene transcription is an important long-term goal in biology as it pertains to human health. Biotechnological advances have revealed heretofore unknown complexity of transcriptional regulation, challenging current models and raising new questions. The proposed projects address three such questions via novel methods and analysis, and promise to enhance our understanding of transcriptional control. (1) There are now a several known examples of network rewiring where a group of genes have conserved expression over long evolutionary distances but the transcriptional mechanisms underlying the expression of the genes has diverged. Understanding the mechanisms for such rewiring has implications for our understanding of evolvability and robustness of organisms. In the specific aim 1, we will develop computational methods to identify instances of transcriptional network rewiring and characterize the conditions facilitating the rewiring. (2) While traditionally, a particular transcription facto (TF) was believed to bind to a specific DNA motif, now it is becoming apparent that many TFs may recognize distinct motifs that modulate functionally distinct outcomes. In the specific aim 2, we will develop computational methods to discover and characterize functional subclasses of transcription factor binding sites. (3) Many important developmental enhancers act from a distance, up to a million nucleotides away from the target gene. How the enhancers accomplish their action-at-a-distance is not entirely clear and has implications for our understanding of developmental and tissue-specific gene regulation. In the specific aim 3 we will develop methods to map enhancers to their distal target genes. 1 PUBLIC HEALTH RELEVANCE: To address several questions pertaining to the mechanisms and evolution of transcriptional control, in the specific aim 1, we will develop computational methods to identify and characterize transcriptional network rewiring in yeast and in fly. In the specific aim 2, we will develop computational methods to discover and characterize functional subclasses of transcription factor binding sites. In the specific aim 3 we will develop methods to map enhancers to their distal target genes. 1