1 / 20

Biomarkers as networks, not individual loci

Biomarkers as networks, not individual loci October 28, 2010 Trey Ideker UCSD BioEng and Med Genetics. Some Grand Challenges in Biology. Develop a global map of cellular machinery which is descriptive and predictive of cellular function

tia
Download Presentation

Biomarkers as networks, not individual loci

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Biomarkers as networks, not individual loci October 28, 2010Trey Ideker UCSD BioEng and Med Genetics

  2. Some Grand Challenges in Biology • Develop a global map of cellular machinery which is descriptive and predictive of cellular function • Demonstrate key uses of this map in virtually every aspect of healthcare

  3. Computer chip design and manufacture is a multi-billion dollar industry. Given modern microchips can have > 1 billion transistors, this industry relies heavily on computer-aided design & manufacturing tools. Popular design tools and languages are Cadence, Verilog, VHDL, Spice, etc. Why can’t drug developmentand healthcare do this?

  4. Shannon et al. Genome Research 2003Cline et al. Nature Protocols 2007 www.cytoscape.org • OPEN SOURCE Java platform for integration of systems biology data • Layout and query of networks (physical, genetic, social, functional) • Visual and programmatic integration of network state data (attributes) • The ultimate goal is to provide tools to facilitate all aspects of network assembly, annotation, and simulation. • RECENT NEWS • Version 2.7 released March 2010 • Cytoscape ® Registered Trademark • The Cytoscape Consortium is a 501(c)3 non-for-profit in the State of California • Centerpiece of the new National Resource for Network Biology, $7 million from NCRR • Downloaded approximately 3000 times per month

  5. Proliferation of CytoscapePlugins

  6. Assembling Networks for Use in the Clinic Network evolutionary comparison / cross-species alignment to identify conserved modules Network-based disease diagnosis / prognosis The Working Map Projection of molecular profiles on protein networks to reveal active modules Rational drug targeting, identification of drug mode of action, ADME/Tox profile Moving from genome-wide association studies (GWAS) to network-wide “pathway” association (NWAS) Integration of transcriptional interactions with causal or functional links Manipulation of cell fates during development Alignment of physical and genetic networks Assembly of network maps of the cell through genomics Network model-based study of disease and development

  7. Cross-comparison of networks: • Conserved regions in the presence vs. absence of stimulus • Conserved regions across different species Suthram et al. Nature 2005 Sharan et al. RECOMB 2004 Kelley et al. PNAS 2003 Sharan & Ideker Nat. Biotech. 2006 Scott et al. RECOMB 2005 Ideker & Sharan Gen Res 2008

  8. Plasmodium: a network apart? Plasmodium-specificprotein complexes Conserved Plasmodium / Saccharomyces protein complexes Suthram et al. Nature 2005La Count et al. Nature 2005

  9. Disease aggression(Time from Sample Collection SCto Treatment TX) Disease aggression(Time from Sample Collection SCto Treatment TX) Predictive gene markers: ZAP-70 CD38 Beta 2 microglobulin etcetera CLL biomarkers via molecular profiles Chuang et al. MSB 2007

  10. Disease aggression(Time from Sample Collection SCto Treatment TX) Moving to Network-based biomarkers T. Kipps, HY Chuang

  11. The Mammalian Cell Fate Map:Can we predict tissue type using expression, networks, etc? Gilbert Developmental Biology 4th Edition

  12. An Atlas of Combinatorial Interactions Among Transcription Factors (TFs) • Mammalian Two Hybrid System • Both Human and Mouse TFs • Approximately 1200 TFs assayed • 1200x1200 matrix tested for interaction • 762 TF-TF interactions in human • 877 TF-TF interactions in mouse • qRT-PCR measurements of TF abundance across 34 adult tissues Tim Ravasi, Harukazu Suzuki, RIKEN Ravasi et al., Cell, 2010

  13. Human vs. Mouse TF-TF Networks in Brain

  14. Interaction coherence within a tissue class r = 0.9 A B Endoderm r = 0.0 A B Mesoderm r = 0.2 A B Ectoderm (incl. CNS) Ravasi et al. Cell 2010

  15. Protein interactions, not levels, dictate tissue specification

  16. “Population” epistatic interactions also run between physical complexes and pathways Physical Interactions Genetic Interactions supported by gene linkage studies Hannum, Srivas et al. PLoS Genetics 2009

  17. Sponsors NIGMS NIEHSNCRRNIMH NSFPackard Found.Agilent Collaborators(UCSD) Richard Kolodner Tom KippsLorraine Pillus Collaborators(external) Nevan Krogan (UCSF) Richard Karp (UC Berkeley) Roded Sharan (Tel Aviv) Bas van Steensel (NKI) Sumit Chanda (Burnham) Michael-Christopher Keogh (Einstein) The Cytoscape Consortium

More Related