Friend AACR 2013-01-16


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Stephen Friend, Jan 16, 2013. AACR Systems Biology Think Tank, Philadelphia, PA

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Friend AACR 2013-01-16

  1. 1. If  not        
  2. 2. Examples:    Expression  Profiles    
  3. 3. 2000  
  4. 4. Examples:    DNA  Altera9ons    
  5. 5. Examples:    Proteomics  
  6. 6. Examples:    Synthe9c  Lethal  Screens  
  7. 7. Examples:  Network  Models  
  8. 8. Genomic Literature Protein-­‐Protein  Complexes Transcriptional SignalingThe Evolution of Systems Biology Mol. Profiles Structure Model Evolution Disease Models Physiologic / Model Topology Pathologic Phenotype Model Dynamics Regulation Regulatory Network: Mesenchymal Signature of High-grade Glioma
  9. 9. Examples:    Drugs  and  Trials  
  10. 10. PARP      IGF1-­‐R    m-­‐TOR    VEGF-­‐R  
  11. 11. Reality: Overlapping Pathways
  12. 12. •  alchemist  
  13. 13. Examples  Muta9ons  
  14. 14. Examples  Muta9ons  
  15. 15. How  oNen  are  we  hurt  by  going  from     the  par9cular  to  the  general   in  very  complex  systems  driven  by  context?     Is  this  going  from  the  par9cular  to  the  general      a  central  problem  in     Hypothesis  Driven  Biomedical  Research?     How  oNen  do  we  inappropriately  praise  findings  that  go  on  to  have  awkward  adjacencies?  
  16. 16. What  could  be  done  by  us?  
  17. 17.   BUILDING  PRECISION  MEDICINE       Extensions  of  Current  Ins9tu9ons       Proprietary  Short  term  Solu9ons      Open  Systems  of  Sharing  in  a  Commons  
  18. 18. Overview    Technology      So@ware      Collabs    Outreach      Plans  NRNB  Inves*gators   Trey  Ideker,  PhD   Principal  Inves*gator,  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  informa9on  resources.   of  the  Overton  Prize  from  the  Interna9onal     Society  for  Computa9onal  Biology.   James  Fowler,  PhD   Alex  Pico,  PhD   Associate  Professor,  CalIT2  Center  for  Wireless  &   Execu*ve  Director,  NRNB   Popula9on  Health  Systems  and  Poli9cal  Science   Gladstone  Ins9tute  of  Cardiovascular  Disease   University  of  California,  San  Diego   Staff  Research  Scien9st   Dr.  Fowler’s  research  concerns  social  networks,   University  of  California,  San  Francisco   behavioral  economics,  evolu9onary  game  theory,   Dr.  Pico  develops  soNware  tools  and  resources   and  genopoli9cs  (the  study  of  the  gene9c  basis  of   that  help  analyze,  visualize  and  explore   poli9cal  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,  Computa9onal  Biology  Center,   Benno  Schwikowski,  PhD   Tri-­‐Ins9tu9onal  Professor   Chef  du  Laboratoire/Group  Leader   Memorial  Sloan-­‐Kecering  Cancer  Center   Pasteur  Ins9tute   Dr.  Sander’s  research  focuses  on  Computa9onal   Dr.  Schwikowski’s  exper9se  lies  in   and  Systems  Biology  of  molecules,  pathways,  and   combinatorial  algorithms  for  Computa9onal   processes.   and  Systems  Biology.      
  19. 19. The  Na9onal  Resource  for  Network  Biology:   Integra9ng  genomes  &  networks  to  understand  health  &  disease   NIH  NCRR  /  NIGMS  P41  GM103504   Dra@  Network  Assembly   Pa*ent  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. We  focus  on  a  world  where  biomedical  research  is  about  to  fundamentally  change.  We  think  it  will  be  oNen  conducted  in  an  open,  collabora*ve  way  where  teams  of  teams  far  beyond  the  current  guilds  of  experts  will  contribute  to  making  becer,  faster,  relevant  discoveries  
  21. 21. Governance Technology PlatformImpactful Models Better Models of Disease: KNOWLEDGE NETWORK Rewards/Challenges
  22. 22.    PORTABLE  LEGAL  CONSENT   Control  of  Private  informa9on  by  Ci9zens  allows  sharing   John  Wilbanks    John  Wilbanks   •  Online  educa9onal  wizard  TED  Talk   •  Tutorial  video   •   Legal  Informed  Consent  Document  “Let’s  pool  our  medical  data”   •   Profile  registra9on   •   Data  upload        
  23. 23. two approaches to building common scientific knowledge Every code change versioned Every issue trackedText summary of the completed project Every project the starting point for new workAssembled after the fact All evolving and accessible in real time Social Coding
  24. 24. 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
  25. 25. Data Analysis with SynapseRun Any ToolOn Any PlatformRecord in SynapseShare with Anyone
  26. 26. “Synapse  is  a  nascent  compute  plakorm  for  transparent,  reproducible,  and  modular  collabora9ve  research.”  
  27. 27. Currently  at  16K+  datasets  and  ~1M  models  
  28. 28. Download analysis and meta-analysisDownload another Cluster Result Download Evaluation and view more stats •  Perform Model averaging •  Compare/contrast models •  Find consensus clusters •  Visualize in Cytoscape
  29. 29. Pancancer collaborative subtype discovery
  30. 30. Objective assessment of factors influencing modelperformance (>1 million predictions evaluated) Sanger   CCLE  Cross  valida*on  predic*on  accuracy  (R2)   Predic9on  accuracy   improved  by…   Not  discre9zing   data   Including   expression  data   Elas9c  net   regression   130  compounds   In  Sock  Jang   24  compounds  
  31. 31. Sage-­‐DREAM  Breast  Cancer  Prognosis  Challenge  #1      Building  becer  disease  models  together   Caldos/Aparicio breast  cancer  data   154  par9cipants;  27  countries     334  par9cipants;  >35  countries     Sep  26  Status   Challenge  Launch:  July  17   >500  models  posted  to  Leaderboard  Sage  Bionetworks-­‐DREAM  Breast  Cancer  Prognosis  Challenge    Phase  2  Best  Performing  Team:  Acractor  Metagenes    Team  Members:  Wei-­‐Yi  Cheng,  Tai-­‐Hsien  Ou  Yang,  and  Dimitris  Anastassiou    
  32. 32. How  to  accelerate  and  make  affordable     the  efforts  required  to  build  becer   models  of  disease  ?        
  33. 33. THE  FEDERATION    Schadt        Ideker        Friend    Haussler)  Nolan        Vidal         (Nolan  and      Califano        
  34. 34. How  to  incent  the  joint  evolu9on  of  ideas  in  a  rapid   learning  space-­‐  prepublica9on?    How  to  fund  where  data  generators  and  analysts  are   not  always  the  same  people-­‐  repeatedly?     Should  we  consider   Centralized  Guilds  vs  Distributed  Dynamic  Teams?  
  35. 35. If  not