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"Harnessing the power of teams to build
better models of disease in real time"




               If not
Examples:

Expression Profiles
2000
Examples:

DNA Alterations
Examples:

Proteomics
Examples:

Synthetic Lethal Screens
Examples:
Network Models
Examples:

Drugs and Trials
PARP

IGF1-R

m-TOR

VEGF-R

Wee-1
Reality: Overlapping Pathways
• alchemist
Examples
Mutations
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?
.
TENURE   FEUDAL STATES
What could be done by us?
BUILDING PRECISION MEDICINE


  Extensions of Current Institutions

   Proprietary Short term Solutions


Open Systems of Sharing in a Commons
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.
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?
Now possible to generate massive amount of human “omic’s” data
Network Modeling Approaches for Diseases are emerging
IT Infrastructure and Cloud compute capacity allows
a generative open approach to solving problems
Nascent Movement for patients to Control Sensitive information allowing sharing
Open Social Media allows citizens and experts to use gaming to solve problems
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
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
Governance




                                       Technology Platform
Impactful Models
                   Better Models of
                       Disease:
                    KNOWLEDGE
                     NETWORK

               Rewards/Challenges
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
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
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
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
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:
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.
Transforming networks into biological hypotheses
Testing network-based hypotheses
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
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
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
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
Data Analysis with Synapse


Run Any Tool



On Any Platform


Record in Synapse


Share with Anyone
“Synapse is a nascent compute
platform for transparent, reproducible,
and modular collaborative research.”
Currently at 16K+ datasets and ~1M models
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
Pancancer collaborative subtype discovery
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
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
How to accelerate and make affordable
  the efforts required to build better
          models of disease ?
THE FEDERATION

Schadt Ideker Friend Haussler) Nolan Vidal
         (Nolan and Califano
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?
If not

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Friend harvard 2013-01-30

  • 1. "Harnessing the power of teams to build better models of disease in real time" If not
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  • 23. 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?
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  • 25. TENURE FEUDAL STATES
  • 26. What could be done by us?
  • 27. BUILDING PRECISION MEDICINE Extensions of Current Institutions Proprietary Short term Solutions Open Systems of Sharing in a Commons
  • 28. 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.
  • 29. 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?
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  • 32. Now possible to generate massive amount of human “omic’s” data
  • 33. Network Modeling Approaches for Diseases are emerging
  • 34. IT Infrastructure and Cloud compute capacity allows a generative open approach to solving problems
  • 35. Nascent Movement for patients to Control Sensitive information allowing sharing
  • 36. Open Social Media allows citizens and experts to use gaming to solve problems
  • 37. 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
  • 38. 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
  • 39. Governance Technology Platform Impactful Models Better Models of Disease: KNOWLEDGE NETWORK Rewards/Challenges
  • 40. 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
  • 41. 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
  • 42. 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
  • 43. 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
  • 44. 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:
  • 45. 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.
  • 46. Transforming networks into biological hypotheses
  • 48. 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
  • 49. 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
  • 50. 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
  • 51. 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
  • 52. Data Analysis with Synapse Run Any Tool On Any Platform Record in Synapse Share with Anyone
  • 53. “Synapse is a nascent compute platform for transparent, reproducible, and modular collaborative research.”
  • 54. Currently at 16K+ datasets and ~1M models
  • 55. 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
  • 57. 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
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  • 59. 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
  • 60. How to accelerate and make affordable the efforts required to build better models of disease ?
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  • 63. THE FEDERATION Schadt Ideker Friend Haussler) Nolan Vidal (Nolan and Califano
  • 64. 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?