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Molecular Systems Biology 8; Article number 565; doi:10.1038/msb.2011.99 Citation: Molecular Systems Biology 8:565 & 2012 EMBO and Macmillan Publishers Limited All rights reserved 1744-4292/12 www.molecularsystemsbiology.com

REVIEW

Differential network biology
Trey Ideker1,2,* and Nevan J Krogan3,4,5,*
1

Departments of Medicine and Bioengineering, University of California San Diego, LaJolla, 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 and 5 J David Gladstone Institutes, San Francisco, CA, USA * Corresponding author. T Ideker,Departments of Medicine and Bioengineering, University of California, 9500 Gilman Drive, San Diego, La Jolla, CA 92093, USA. Tel.: þ 1 858 822 4558; Fax: þ 1 858 822 4246; E-mail: tideker@ucsd.edu or NJ Krogan, Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA 94158, USA. Tel.: þ 1 415 476 2980; Fax: þ 1 415 514 9736; E-mail:krogan@cmp.ucsf.edu
Received 27.9.11; accepted 6.12.11

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 largescales 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 proteomicanalysis. Molecular Systems Biology 8: 565; published online 17 January 2012; doi:10.1038/msb.2011.99
Subject Categories: bioinformatics; functional genomics Keywords: genetic interactions; networks; protein interactions

Introduction
Physical and genetic interaction networks provide key insights into complex biological systems, from how different processes communicate to the function of individualresidues on a single protein (Beltrao et al, 2010). For instance, the systematic & 2012 EMBO and Macmillan Publishers Limited

identification of pairwise protein interactions (Stelzl et al, 2005; Tarassov et al, 2008; Yu et al, 2008; Consortium, 2011) or protein complexes (Butland et al, 2005; Gavin et al, 2006; Krogan et al, 2006; Sowa et al, 2009) has been a widely used strategy forunderstanding the physical architecture of the cell. Other types of physical interactions that are being mapped systematically include transcriptional protein–DNA interactions (Ren et al, 2000; Iyer et al, 2001) and kinase–substrate interactions (Ptacek et al, 2005; Linding et al, 2007). Genetic networks, in contrast, chart pairs of genetic mutations that in combination cause lethality or otherphenotype—information that complements the structural information provided by the physical network (Tong et al, 2001; Roguev et al, 2007; Butland et al, 2008; Typas et al, 2008; Horn et al, 2011). Large network databases such as BioGRID, HPRD, IntAct, DIP and GeneMania (Xenarios et al, 2002; Keshava Prasad et al, 2009; Aranda et al, 2010; Warde-Farley et al, 2010; Stark et al, 2011) record hundreds of...