Computational inference of genetic networks in humancancer cells

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The dysregulated activity of genetic regulatorynetworks contributes to neoplastic transformation by promoting aberrantexpression of genes involved in regulating cell homeostasis.Therefore, characterization of regulatory networks in humancancer cells is a critical objective in understanding the molecularmechanisms of cell transformation. Modern high throughput technologiesare providing the first window into regulatory processes on thegenome scale, foretelling the ability of computational inferencealgorithms to produce models of regulatory networks that willrevolutionize our understanding and treatment of cancer by 1)describing how genomic alterations cause functional disruptions inthe network regulating cell homeostasis, leading to aberrant cellgrowth and cancer, and 2) predicting therapeutic interventions, in which critical components of the network can be targeted to revertthe cancer phenotype. This book will develop methods thatadvance the current state of the art in inferring genetic regulatorynetworks from high throughput data, with specific application to bothgene expression and ChIP-on-chip data.