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Stephen Friend, Jan 30, 2013. At HMS Genetics Center for Cancer Systems Biology, Boston, MA
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"Harnessing the power of teams to build better models of disease in real time"
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
NRNB Investigators Trey Ideker, PhD Principal Investigator, NRNB Departments of Medicine and Bioengineering University of California, San Diego
Dr. Ideker uses genome-scale measurements to construct network models of DNA damage response and cancer. He was the 2009 recipient of the Overton Prize from the International Society for Computational Biology.
Gary Bader, PhD Assistant Professor, Terrence Donnelly Centre for Cellular & Biomolecular Research University of Toronto
Dr. Bader works on biological network analysis and pathway information resources.
Alex Pico, PhD Executive Director, NRNB Gladstone Institute of Cardiovascular Disease Staff Research Scientist University of California, San Francisco
Dr. Pico develops software tools and resources that help analyze, visualize and explore biomedical data in the context of these networks
James Fowler, PhD Associate Professor, CalIT2 Center for Wireless & Population Health Systems and Political Science University of California, San Diego
Dr. Fowler’s research concerns social networks, behavioral economics, evolutionary game theory, and genopolitics (the study of the genetic basis of political behavior). His research on social networks has been featured in Time’s Year in Medicine.
Chris Sander, PhD Chair, Computational Biology Center, Tri-Institutional Professor Memorial Sloan-Kettering Cancer Center
Dr. Sander’s research focuses on Computational and Systems Biology of molecules, pathways, and processes.
Benno Schwikowski, PhD Chef du Laboratoire/Group Leader Pasteur Institute
Dr. Schwikowski’s expertise lies in combinatorial algorithms for Computational and Systems Biology.
Overview Technology Software Collabs Outreach Plans
The National Resource for Network Biology: Integrating genomes & networks to understand health & disease
Patient genotype Genome sequencing
1) How to assemble and visualize network models of the cell?
2) How to use networks in healthcare?
Phenotype Disease diagnosis
Response to therapy/drug Side effects
Developmental outcome Rate of aging, etc.
Gene expression & other large scale molecular state measurements
NIH NCRR / NIGMS P41 GM103504
Draft Network Assembly
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
Better Models of
Disease:
KNOWLEDGE
NETWORK
Techn
olo
gy P
latform
Rewards/Challenges
Imp
actf
ul M
od
els
Governance
Two recurring problems in Alzheimer’s disease research
40
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.
Identifying key disease systems and genes- Gaiteri et al.
Example “modules” of coexpressed genes, color-coded
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
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
Infer directed/causal relationships and clear hierarchical structure by incorporating eSNP information (no hair-balls here)
vs
Prioritize modules through expression synchrony with clinical measures or tendency too reconfigure themselves in disease
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
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.
(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.
Core family members are shaded.
Transforming networks into biological hypotheses
Testing network-based hypotheses
Design-stage AD projects at Sage
Fusing our expertise in…
Join us in uniting genes, circuits and regions to build multi-scale biophysical disease models. Contact [email protected]
Diffusion Spectrum Imaging
Microcircuits & neuronal diversity
Gene regulatory networks
Feed
back
PORTABLE LEGAL CONSENT Control of Private information by Citizens allows sharing
weconsent.us
John Wilbanks
• Online educational wizard • Tutorial video • Legal Informed Consent Document • Profile registration • Data upload
John Wilbanks TED Talk “Let’s pool our medical data” weconsent.us
two approaches to building common scientific knowledge
Text summary of the completed project
Assembled after the fact
Every code change versioned
Every issue tracked
Every project the starting point for new work
All evolving and accessible in real time
Social Coding
Synapse is GitHub for Biomedical Data
• Data and code versioned
• Analysis history captured in real time
• Work anywhere, and share the results with anyone
• Social/Interactive Science
• Every code change versioned
• Every issue tracked
• Every project the starting point for new work
• Social/Interactive Coding
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 Prediction accuracy
improved by…
Not discretizing data
Including expression data
Elastic net regression
130 compounds 24 compounds
Cro
ss v
alid
atio
n p
red
icti
on
acc
ura
cy (
R2)
In Sock Jang
Sage-DREAM Breast Cancer Prognosis Challenge #1 Building better disease models together
154 participants; 27 countries
334 participants; >35 countries
>500 models posted to Leaderboard
breast cancer data
Challenge Launch: July 17
Sep 26 Status
Caldos/Aparicio
How to accelerate and make affordable the efforts required to build better
models of disease ?
(Nolan and Haussler)
THE FEDERATION
Schadt Ideker Friend Califano Nolan Vidal
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