Identifying the transcription factor interactions that are responsible for cell-specific gene expression programs is key to understanding the regulation of cell behaviors, such as self-renewal, proliferation, differentiation, and death. The rapidly increasing availability of microarray-derived global gene expression data sets, coupled with genome sequence information from multiple species, has driven the development of computational methods to reverse engineer and dynamically model genetic regulatory networks. An understanding of the architecture and behavior of transcriptional networks should lend insight into how the huge number of potential gene expression programs is constrained and facilitates efforts to direct or redirect cell fate.