The dysregulated activity of genetic regulatory networks contributes to neoplastic transformation by promoting aberrant expression of genes involved in regulating cell homeostasis. Therefore, characterization of regulatory networks in human cancer cells is a critical objective in understanding the molecular mechanisms of cell transformation. Modern high throughput technologies are providing the first window into regulatory processes on the genome scale, foretelling the ability of computational inference algorithms to produce models of regulatory networks that will revolutionize our understanding and treatment of cancer by 1) describing how genomic alterations cause functional disruptions in the network regulating cell homeostasis, leading to aberrant cell growth and cancer, and 2) predicting therapeutic interventions, in which critical components of the network can be targeted to revert the cancer phenotype. This book will develop methods that advance the current state of the art in inferring genetic regulatory networks from high throughput data, with specific application to both gene expression and ChIP-on-chip data.