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Cytoscape Springs Forward: Re-architecture for Version 3.0 Allan Kuchinsky1,3, Keiichiro Ono2, Michael Smoot2, Trey Ideker2,Annette Adler1 1 Agilent Technologies, 5301 Stevens Creek Blvd., Santa Clara, CA 95051 USA 2 School of Medicine, University of California, San Diego, CA 92093 USA 3Gladstone Institute of Cardiovascular Disease, San Francisco, CA, 94158-2261, USA email: allan_kuchinsky@agilent.com June, 2009 June 2009
Outline ,[object Object]
Overview of Cytoscape
Motivating factors for re-architecture
Architectural/design principles for Cytoscape version 3.0
IssuesJune 2009
June 2009 Page 3 Modern -omics technologies yield “snapshots” of discrete parts of the biological process. High throughput quantification of: ,[object Object]
proteins
metabolitesHigh throughput characterization of: ,[object Object]
chromatin structure
nucleotide sequence
post-translational modificationThe value of looking only at results from individual disciplines is limited.
June 2009 Page 4 The real biological story is in the relationshipsamongst the biological entities, rather than the entities themselves.   Interpretive value comes from integrating diverse measurements within their biological context. “Systems biology is about putting together rather than taking apart, integration rather than reduction..” D. Noble, The Music of life. Biology beyond the genome Oxford University Press 2006. ISBN 0199295735, ISBN 978-0199295739 Roth et al, n engl j med 356;1 www.nejm.org january 4, 2007) show the molecular mechanisms by which certain drugs may induce Valvular Heart Disease.
Cytoscape is a collaboration between University of California, San Diego Institute for Systems Biology Memorial Sloan-Kettering Cancer Center Institute Pasteur Agilent Technologies University of Toronto Gladstone Institute for Cardiovascular Disease University of California, San Francisco Unilever Cytoscape is an open source software platform for integrating, analyzing, and visualizing measurement data in their biological context. National Center for Integrative  Biomedical Informatics freely available at http://www.cytoscape.org ,[object Object]
   Currently 75 registered plugins, developed by leading research groups,  freely available
   Community development of plugins strongly encouraged and actively supported by core development team.

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Cytoscape 3.0: Re-architected for Modularity

Notas del editor

  1. Gene Function Prediction –basic but important, examining genes (proteins) in a network context shows connections to sets of genes/proteins involved in same biological process that are likely to function in that processDetection of protein complexes/other modular structures – although interaction networks are based on pair-wise interactions, there is clear evidence for modularity & higher order organization (motifs, feedback loops)Network evolution – biological process(s) conservation across species (PathBLAST, NetworkBLAST to align p-p interaction networks & clusters)Prediction of new interactions and functional associations – Statistically significant domain-domain correlations in protein interaction network suggest that certain domain (and domain pairs) mediate protein binding. Machine learning extends this to suggest to predict protein-protein or genetic interaction through integration of diverse types of evidence for interaction
  2. Ties beuatifully into cnvsnp work at agilent.Identification of disease subnetworks– identification of disease network subnetworks that are transcriptionally active in disease. These suggest key pathway components in disease progression and provide leads for further study and potential therapeutic targetsSubnetwork-based diagnosis – subnetworks also provide a rich source of biomarkers for disease classification, based on mRNA profiling integrated with protein networks to identify subnetwork biomarkers (interconnected genes whose aggregate expression levels are predictive of disease state)Subnetwork-based gene association – molecular networks will provide a powerful framework for mapping common pathway mechanisms affected by collection of genotypes (SNP, CNV)