title: Principles and strategies for developing network models in cancer.
abstract: The flood of genome-wide data generated by high-throughput technologies currently provides biologists with an unprecedented opportunity: to manipulate, query, and reconstruct functional molecular networks of cells. Here, we outline three underlying principles and six strategies to infer network models from genomic data. Then, using cancer as an example, we describe experimental and computational approaches to infer "differential" networks that can identify genes and processes driving disease phenotypes. In conclusion, we discuss how a network-level understanding of cancer can be used to predict drug response and guide therapeutics.
authors.affiliation: Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, New York, NY 10027, USA. dpeer@biology.columbia.edu
authors.forename: Dana
authors.initials: D
authors.lastname: Pe'er
authors.forename: Nir
authors.initials: N
authors.lastname: Hacohen
mesh_terms: D000818:Animals; D006801:Humans; D008957:Models, Genetic; D009154:Mutation; D009369:Neoplasms / Q000235:genetics*