Principles and strategies for developing network models in cancer

Dana Pe'er; Nir Hacohen

doi:10.1016/j.cell.2011.03.001

Principles and strategies for developing network models in cancer

Cell. 2011 Mar 18;144(6):864-73. doi: 10.1016/j.cell.2011.03.001.

Authors

Dana Pe'er¹, Nir Hacohen

Affiliation

¹ Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, New York, NY 10027, USA. dpeer@biology.columbia.edu

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Animals
Humans
Models, Genetic*
Mutation
Neoplasms / genetics*

Abstract

Publication types

MeSH terms

Grants and funding