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

1. If
not

2. Examples:
Expression
Profiles

3. 2000

5. Examples:
DNA
Altera9ons

8. Examples:
Proteomics

11. Examples:
Synthe9c
Lethal
Screens

14. Examples:
Network
Models

15. Genomic Literature Protein-­‐Protein
Complexes
Transcriptional
Signaling
The 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

16. Examples:
Drugs
and
Trials

17. PARP
IGF1-­‐R
m-­‐TOR
VEGF-­‐R

18. Reality: Overlapping Pathways

21. • alchemist

22. Examples
Muta9ons

23. Examples
Muta9ons

24. 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?

25. What
could
be
done
by
us?

26.
BUILDING
PRECISION
MEDICINE
Extensions
of
Current
Ins9tu9ons
Proprietary
Short
term
Solu9ons
Open
Systems
of
Sharing
in
a
Commons

27. 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.

28. 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?

32. 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

33. Governance
Technology Platform
Impactful Models
Better Models of
Disease:
KNOWLEDGE
NETWORK
Rewards/Challenges

34.
PORTABLE
LEGAL
CONSENT
Control
of
Private
informa9on
by
Ci9zens
allows
sharing
weconsent.us
John
Wilbanks
John
Wilbanks
• Online
educa9onal
wizard
TED
Talk
• Tutorial
video
•
Legal
Informed
Consent
Document
“Let’s
pool
our
medical
data”
•
Profile
registra9on
weconsent.us
•
Data
upload

35. 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

36. 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

37. Data Analysis with Synapse
Run Any Tool
On Any Platform
Record in Synapse
Share with Anyone

38. “Synapse
is
a
nascent
compute
plakorm
for
transparent,
reproducible,
and
modular
collabora9ve
research.”

39. Currently
at
16K+
datasets
and
~1M
models

40. 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

41. Pancancer collaborative subtype discovery

42. Objective assessment of factors influencing model
performance (>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

44. 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

45. How
to
accelerate
and
make
affordable
the
efforts
required
to
build
becer
models
of
disease
?

48. THE
FEDERATION
Schadt
Ideker
Friend
Haussler)
Nolan
Vidal
(Nolan
and
Califano

49. 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?

50. If
not