This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Analysis of biological networks has exploded in recent years. A wide variety of technologies have been introduced for mapping networks of gene and protein interactions, including yeast-two-hybrid assays, affinity purification coupled to mass spectrometry, chromatin immunoprecipitation measurements, synthetic-lethal and suppressor networks, expression QTLs, and many others. These technologies have the potential to revolutionize the study of human health, by enabling the construction of large pathway maps which can pinpoint the molecular mechanisms underlying normal and disease states of biological systems. However, the enormous variety and number of new molecular interaction measurements necessitate new algorithms, conceptual frameworks, and software to integrate, query, visualize, and interpret the resulting network data.
The aim of the National Resource for Network Biology (NRNB) is to provide a freely available, open-source suite of software technology that broadly enables network-based visualization, analysis, and biomedical discovery for NIH-funded researchers. Our overall objectives are to assemble large-scale biological data into models of networks and pathways and to use these networks to better understand how biological systems operate under normal conditions ahd how they fail in disease. These goals will be carried out in four Technology Research and Development projects: A. Network-based biomarkers for disease classification;B. Recognizing trend motifs and dynamics in social networks;C. Network visualization and representation;and D. Predictive modeling and network inference. The centerpiece of our software development and support efforts will be Cytoscape, currently one of the most functional and widely used network visualization and analysis tools. Now in its sixth year of development, Cytoscape is co-developed by a multi-disciplinary team of institutions known as the Cytoscape Consortium, who also comprise the host institutions of our proposed resource.

National Institute of Health (NIH)
National Center for Research Resources (NCRR)
Biotechnology Resource Grants (P41)
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Special Emphasis Panel (ZRG1-BST-N (40))
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University of California San Diego
Internal Medicine/Medicine
Schools of Medicine
La Jolla
United States
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Gross, Andrew M; Orosco, Ryan K; Shen, John P et al. (2014) Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss. Nat Genet 46:939-43
Reimand, Jüri; Bader, Gary D (2013) Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Mol Syst Biol 9:637
Karagiannis, George S; Weile, Jochen; Bader, Gary D et al. (2013) Integrative pathway dissection of molecular mechanisms of moxLDL-induced vascular smooth muscle phenotype transformation. BMC Cardiovasc Disord 13:4
Gao, Jianjiong; Aksoy, Bülent Arman; Dogrusoz, Ugur et al. (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6:pl1
Gwinner, Frederik; Acosta-Martin, Adelina E; Boytard, Ludovic et al. (2013) Identification of additional proteins in differential proteomics using protein interaction networks. Proteomics 13:1065-76
Reimand, Jüri; Wagih, Omar; Bader, Gary D (2013) The mutational landscape of phosphorylation signaling in cancer. Sci Rep 3:2651
Bark, Steven J; Wegrzyn, Jill; Taupenot, Laurent et al. (2012) The protein architecture of human secretory vesicles reveals differential regulation of signaling molecule secretion by protein kinases. PLoS One 7:e41134
Saito, Rintaro; Smoot, Michael E; Ono, Keiichiro et al. (2012) A travel guide to Cytoscape plugins. Nat Methods 9:1069-76
Cerami, Ethan; Gao, Jianjiong; Dogrusoz, Ugur et al. (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2:401-4
Ideker, Trey; Dutkowski, Janusz; Hood, Leroy (2011) Boosting signal-to-noise in complex biology: prior knowledge is power. Cell 144:860-3

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