Advances in high-throughput technologies, such as ChIP-chip, and the completion of human and mouse genomic sequences now allow analysis of the mechanisms of gene regulation on a systems level. In this study, we have developed a computational genomics approach (termed ChIPModules), which begins with experimentally determined binding sites and integrates positional weight matrices constructed from transcription factor binding sites, a comparative genomics approach, and statistical learning methods to identify transcriptional regulatory modules. We began with E2F1 binding site information obtained from ChIP-chip analyses of ENCODE regions, from both HeLa and MCF7 cells. Our approach not only distinguished targets from nontargets with a high specificity, but it also identified five regulatory modules for E2F1. One of the identified modules predicted a colocalization of E2F1 and AP-2alpha on a set of target promoters with an intersite distance of <270 bp. We tested this prediction using ChIP-chip assays with arrays containing approximately 14,000 human promoters. We found that both E2F1 and AP-2alpha bind within the predicted distance to a large number of human promoters, demonstrating the strength of our sequence-based, unbiased, and universal protocol. Finally, we have used our ChIPModules approach to develop a database that includes thousands of computationally identified and/or experimentally verified E2F1 target promoters.

E2F1 is involved in both cell cycle and apoptosis, but we still fail to understand whether these are distinct E2F1 states and what controls the transition between states. Studies in cell cultures are often contradictory showing opposite effects of E2F1; even further, some phenotypes in animals lacking or overexpressing E2F1 would not be predicted from in vitro findings. In this review, we analyze current literature on the role of E2F1 in apoptosis and use engineering concepts and a systems biology approach to align the overwhelming amount of contradictory data on this gene.