If not

"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 chris.gaiteri@sagebase.org

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