Differential network biology
Trey Ideker, Nevan J Krogan
Author Affiliations
Trey Ideker*,1,2 and Nevan J Krogan*,3,4,5
1 Departments of Medicine and Bioengineering, University of California San Diego, La Jolla, CA, USA
2 The Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
3 Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA
4 California Institute for Quantitative Biosciences, QB3, San Francisco, CA, USA
5 J David Gladstone Institutes, San Francisco, CA, USA
↵*Corresponding authors. Departments of Medicine and Bioengineering, University of California, 9500 Gilman Drive, San Diego, La Jolla, CA 92093, USA. Tel.: +1 858 822 4558; Fax: +1 858 822 4246; E-mail: tideker@ucsd.eduDepartment of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA 94158, USA. Tel.: +1 415 476 2980; Fax: +1 415 514 9736; E-mail: krogan@cmp.ucsf.edu
DOI 10.1038/msb.2011.99 | Published online 17.01.2012
Molecular Systems Biology (2012) 8, 565
Source/Fonte: Raman Lab
Abstract
Protein and genetic interaction maps can reveal the overall physical and functional landscape of a biological system. To date, these interaction maps have typically been generated under a single condition, even though biological systems undergo differential change that is dependent on environment, tissue type, disease state, development or speciation. Several recent interaction mapping studies have demonstrated the power of differential analysis for elucidating fundamental biological responses, revealing that the architecture of an interactome can be massively re‐wired during a cellular or adaptive response. Here, we review the technological developments and experimental designs that have enabled differential network mapping at very large scales and highlight biological insight that has been derived from this type of analysis. We argue that differential network mapping, which allows for the interrogation of previously unexplored interaction spaces, will become a standard mode of network analysis in the future, just as differential gene expression and protein phosphorylation studies are already pervasive in genomic and proteomic analysis.
Genetic Interactions Networks Protein Interactions
FREE PDF GRATIS: Mol Syst Biol. 8: 565
