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Friend harvard 2013-01-30

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NRNB Annual Report 2011
NRNB Annual Report 2011
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Friend harvard 2013-01-30

  1. 1. "Harnessing the power of teams to build better models of disease in real time" If not
  2. 2. Examples: Expression Profiles
  3. 3. 2000
  4. 4. Examples: DNA Alterations
  5. 5. Examples: Proteomics
  6. 6. Examples: Synthetic Lethal Screens
  7. 7. Examples: Network Models
  8. 8. Examples: Drugs and Trials
  9. 9. PARP IGF1-R m-TOR VEGF-R Wee-1
  10. 10. Reality: Overlapping Pathways
  11. 11. • alchemist
  12. 12. Examples Mutations
  13. 13. How often are we hurt by going from the particular to the general in very complex systems driven by context? Is this going from the particular to the general a central problem in Hypothesis Driven Biomedical Research? How often do we inappropriately praise findings that go on to have awkward adjacencies?
  14. 14. .
  15. 15. TENURE FEUDAL STATES
  16. 16. What could be done by us?
  17. 17. BUILDING PRECISION MEDICINE Extensions of Current Institutions Proprietary Short term Solutions Open Systems of Sharing in a Commons
  18. 18. Overview Technology Software Collabs Outreach Plans NRNB Investigators Trey Ideker, PhD Principal Investigator, NRNB Gary Bader, PhD Departments of Medicine and Bioengineering Assistant Professor, Terrence Donnelly Centre University of California, San Diego for Cellular & Biomolecular Research Dr. Ideker uses genome-scale measurements to University of Toronto construct network models of DNA damage Dr. Bader works on biological network analysis response and cancer. He was the 2009 recipient and pathway information resources. of the Overton Prize from the International Society for Computational Biology. James Fowler, PhD Alex Pico, PhD Associate Professor, CalIT2 Center for Wireless & Executive Director, NRNB Population Health Systems and Political Science Gladstone Institute of Cardiovascular Disease University of California, San Diego Staff Research Scientist Dr. Fowler’s research concerns social networks, University of California, San Francisco behavioral economics, evolutionary game theory, Dr. Pico develops software tools and resources and genopolitics (the study of the genetic basis of that help analyze, visualize and explore political behavior). His research on social networks biomedical data in the context of these networks has been featured in Time’s Year in Medicine. Chris Sander, PhD Chair, Computational Biology Center, Benno Schwikowski, PhD Tri-Institutional Professor Chef du Laboratoire/Group Leader Memorial Sloan-Kettering Cancer Center Pasteur Institute Dr. Sander’s research focuses on Computational Dr. Schwikowski’s expertise lies in and Systems Biology of molecules, pathways, and combinatorial algorithms for Computational processes. and Systems Biology.
  19. 19. The National Resource for Network Biology: Integrating genomes & networks to understand health & disease NIH NCRR / NIGMS P41 GM103504 Draft Network Assembly Patient genotype Genome sequencing Phenotype Disease diagnosis Response to therapy/drug Side effects Developmental outcome 1) How to assemble and visualize Rate of aging, etc. Gene expression & network models of the cell? other large scale molecular state measurements 2) How to use networks in healthcare?
  20. 20. Now possible to generate massive amount of human “omic’s” data
  21. 21. Network Modeling Approaches for Diseases are emerging
  22. 22. IT Infrastructure and Cloud compute capacity allows a generative open approach to solving problems
  23. 23. Nascent Movement for patients to Control Sensitive information allowing sharing
  24. 24. Open Social Media allows citizens and experts to use gaming to solve problems
  25. 25. 1- Now possible to generate massive amount of human “omic’s” data 2-Network Modeling Approaches for Diseases are emerging 3- IT Infrastructure and Cloud compute capacity allows a generative open approach to biomedical problem solving 4-Nascent Movement for patients to Control Sensitive information allowing sharing 5- Open Social Media allows citizens and experts to use gaming to solve problems A HUGE OPPORTUNITY -- A HUGE RESPONSIBILITY
  26. 26. We focus on a world where biomedical research is about to fundamentally change. We think it will be often conducted in an open, collaborative way where teams of teams far beyond the current guilds of experts will contribute to making better, faster, relevant discoveries
  27. 27. Governance Technology Platform Impactful Models Better Models of Disease: KNOWLEDGE NETWORK Rewards/Challenges
  28. 28. Two recurring problems in Alzheimer’s disease research Ambiguous pathology Are disease-associated molecular systems & genes destructive, adaptive, or both? Bottom line: We need to identify causal factors vs correlative or adaptive features of disease. Diverse mechanisms How do diverse mutations and environmental factors combine into a core pathology? Bottom line: There is no rigorous / consistent global framework that integrates diverse disease factors. 40
  29. 29. Identifying key disease systems and genes- Gaiteri et al. 1.) Identify groups of genes that move together – coexpressed “modules” - correlated expression of multiple genes across many patients - coexpression calculated separately for Disease/healthy groups - these gene groups are often coherent cellular subsystems, enriched in one or more GO functions Example “modules” of coexpressed genes, color-coded
  30. 30. Identifying key disease systems and genes 1.) Identify groups of genes that move together – coexpressed “modules” 2.) Prioritize the disease-relevance of the modules by clinical and network measures Prioritize modules through expression synchrony with clinical measures or tendency too reconfigure themselves in disease vs
  31. 31. Identifying key disease systems and genes 1.) Identify groups of genes that move together – coexpressed “modules” 2.) Prioritize the disease-relevance of the modules by clinical and network measures 3.) Incorporate genetic information to find directed relationships between genes Infer directed/causal relationships Prioritize modules through expression and clear hierarchical structure by synchrony with clinical measures or tendency too reconfigure themselves in disease incorporating eSNP information (no hair-balls here) vs
  32. 32. Example network finding: microglia activation in AD Module selection – what identifies these modules as relevant to Alzheimer’s disease? The eigengene of a module of ~400 probes correlates with Braak score, age, cognitive disease severity and cortical atrophy. Members of this module are on average differentially expressed (both up- and down-regulated). Evidence these modules are related to microglia function The members of this module are enriched with GO categories (p<.001) such as “response to biotic stimulus” that are indicative of immunologic function for this module. The microglia markers CD68 and CD11b/ITGAM are contained in the module (this is rare – even when a module appears to represent a specific cell-type, the histological markers may be lacking). Numerous key drivers (SYK, TREM2, DAP12, FC1R, TLR2) are important elements of microglia signaling . Alzgene hits found in co-regulated microglia module:
  33. 33. Figure key: Five main immunologic families found in Alzheimer’s-associated module Square nodes in surrounding network denote literature-supported nodes. Node size is proportional to connectivity in the full module. Core family members are shaded. (Interior circle) Width of connections between 5 immune families are linearly scaled to the number of inter-family connections. Labeled nodes are either highly connected in the original network, implicated by at least 2 papers as associated with Alzheimer’s disease, or core members of one of the 5 immune families.
  34. 34. Transforming networks into biological hypotheses
  35. 35. Testing network-based hypotheses
  36. 36. Design-stage AD projects at Sage Fusing our expertise in… Gene regulatory networks Diffusion Spectrum Imaging Feedback Microcircuits & neuronal diversity Join us in uniting genes, circuits and regions to build multi-scale biophysical disease models. Contact chris.gaiteri@sagebase.org
  37. 37. PORTABLE LEGAL CONSENT Control of Private information by Citizens allows sharing weconsent.us John Wilbanks John Wilbanks • Online educational wizard TED Talk • Tutorial video • Legal Informed Consent Document “Let’s pool our medical data” • Profile registration weconsent.us • Data upload
  38. 38. two approaches to building common scientific knowledge Every code change versioned Every issue tracked Text summary of the completed project Every project the starting point for new work Assembled after the fact All evolving and accessible in real time Social Coding
  39. 39. Synapse is GitHub for Biomedical Data • Every code change versioned • Every issue tracked • Every project the starting point for new work • Data and code versioned • Social/Interactive Coding • Analysis history captured in real time • Work anywhere, and share the results with anyone • Social/Interactive Science
  40. 40. Data Analysis with Synapse Run Any Tool On Any Platform Record in Synapse Share with Anyone
  41. 41. “Synapse is a nascent compute platform for transparent, reproducible, and modular collaborative research.”
  42. 42. Currently at 16K+ datasets and ~1M models
  43. 43. Download analysis and meta-analysis Download another Cluster Result Download Evaluation and view more stats • Perform Model averaging • Compare/contrast models • Find consensus clusters • Visualize in Cytoscape
  44. 44. Pancancer collaborative subtype discovery
  45. 45. Objective assessment of factors influencing model performance (>1 million predictions evaluated) Sanger CCLE Cross validation prediction accuracy (R2) Prediction accuracy improved by… Not discretizing data Including expression data Elastic net regression 130 compounds In Sock Jang 24 compounds
  46. 46. Sage-DREAM Breast Cancer Prognosis Challenge #1 Building better disease models together Caldos/Aparicio breast cancer data 154 participants; 27 countries 334 participants; >35 countries Sep 26 Status Challenge Launch: July 17 >500 models posted to Leaderboard
  47. 47. How to accelerate and make affordable the efforts required to build better models of disease ?
  48. 48. THE FEDERATION Schadt Ideker Friend Haussler) Nolan Vidal (Nolan and Califano
  49. 49. How to incent the joint evolution of ideas in a rapid learning space- prepublication? How to fund where data generators and analysts are not always the same people- repeatedly? Should we consider Centralized Guilds vs Distributed Dynamic Teams?
  50. 50. If not

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