Transcript Slide 1

Translational Science
on the Cloud
An Experiment in Translational Science
Peter J. Tonellato
Dennis Wall
Center for Biomedical Informatics
Harvard Medical School
pa⋅lav⋅er
/pəˈlævər, -ˈlɑvər/ noun
1. long parley usually between persons of
different cultures or levels of sophistication
2. conference, discussion
3. idle talk
4. misleading or beguiling speech
A few ambitious goals –
•
Host a cross-disciplinary geographically distributed palaver using webcasting technology.
•
Collaborate on a complex set of high caliber scientific and computationally complex
projects.
•
Provide a thematically consistent set of lectures by a world-class collection of lecturers.
•
Implement and test the activities on a new technology never previously used for scientific
exploration.
Project Objectives
• Scientific
• Computational-BioMedical Informatic
• “Cloud”
– Manage Resources, reduce complexity and costs
• “Translational”
– Research -> Examination of Clinical Potential
– Potential -> Clinical Efficacy
– Clinical Efficacy -> Clinical Use
Gartner Warnings
Best to avoid Peaks
and Troughs if
Possible.
Participation
• ‘I like to watch’
– attend or watch recorded lectures
• ‘I like to watch - a lot’
– same as above and attend (skype, webex or in person) project
discussions
* To Doug MacFadden for
• ‘I like to more than watch’
– above and join active project team
– contribution to project objectives
noting the “Being There”
connection.
Collaborators
• Kurt Messersmith, Terry Wise, Jinesh Varia, and the AWS group
• Josh Fraser, Ed Goldberg, and the RightScale group
• Sushil Kumar, William Hodak and Oracle group
Participants (incomplete list)
• Laboratory for Personalized Medicine
– Peter Tonellato, Vincent Fusaro, Prasad Patil, Rimma Pivovarov, Peter Kos
• Wall Lab
– Dennis Wall, Parul Kudtarkar, Joy Poulo, Matt Hyuck
• Church Lab
– Alexander Wait
• Thomson Lab
– Victor Ruotti, Ron Stewart
• University of Wisconsin – Milwaukee
– Peter Kos, Dave Petering, Tom Hansen, David Stack, Joseph Bockhorst
Participants (incomplete list)
• Tokyo Medical and Dental University
– Kumiko Oohashi, Takako Takai, Yutaka Fukuoka
• Recombinant Data
– Dan Housman
• Great Lakes WATER Institute
– Michael Caravan, Rick Goetz
• Medical College of Wisconsin
– Simon Twigger
• Marquette University
– Craig Strubble
Acknowledgements
Laboratory for Personalized Medicine
Peter J. Tonellato, Ph.D.
Vincent Fusaro
Prasad Patil
Peter Kos
Zhitao Wang
Dan Chen
Haiping Xia
Sumana Ramayanam
Wall Lab:
Dennis Wall, Ph.D.
Tom Monaghan
Amazon:
Tenesha Gleason
Ford Harris
Laboratory of Personalized Medicine
CBMI, Harvard Medical School
Established in 2008 to Develop:
• Clinical-genetic mathematical models
• Translational science simulation paradigm and
• Personalized Medicine (PM) Web applications
and create a facilitated pathway from
genetic discovery to clinical enterprise
Project Objectives
• Scientific: Modeling and Prediction of Clinical Avatars and
Pharmacogenetic Dosing
• Computational-BioMedical Informatic: Accuracy of
Simulations, mashup, Webapplication
• “Cloud”
– Manage Resources, reduce complexity and costs
• “Translational”
– Research -> Examination of Clinical Potential
– Potential -> Clinical Efficacy
– Clinical Efficacy -> Clinical Use
Oracle in the Cloud
Posted: May 6, 2008 10:43 AM PDT
TimeLine
Here at Oracle, we have been keeping track of the great strides being made by the Amazon
Web Services team in enabling a Cloud Computing platform. We are looking to talk
with people who are interested in utilizing Oracle technologies within the AWS
platform. Please contact me directly at my email address below if you would like to
share your thoughts on how Oracle technologies can help your AWS projects or if you
are interested in simply sharing your experiences with AWS.
I look forward to hearing from you!
Bill Hodak
Senior Product Manager - Oracle Corporation
[email protected]
Fitting the Pieces Together
User
Application
Linux
Server
Amazon
EC2
Instances
Oracle
AMI
Amazon
S3
Amazon Web Services (AWS)
HPC
AMI
Math Modeling and Simulation
HPC Cloud Service
Simulation as Service Options R Benefits:
–
–
–
–
–
Matlab
Mathematica
R
SAS
S-PLUS
– Fast computation and
statistical analysis
– Large mathematical and
statistical library
– Open source
– Highly extensible
– Supportive user
community
OpenXava
Business
Components
+
Controllers
=
Application Ready for
Production
• Deployable on Java Application Server or any Servlet
Container, or on a Portal (Liferay, JetSpeed or WebSphere)
“Clouded” Translational Science
• Web application framework is flexible
• Robust technologies
– Oracle and AWS cloud services in concert with R, OpenXava, Ruby
• Extreme Implementation: LPM team no previous collaboration
• Cloud Development Service inventory growing rapidly.
- Subversion - i2b2
- R/S/Splus
- Research Data
- Development Platform:
- OpenXava and dependecies
- Ruby-on-Rails and dependencies
- Clinical Trial simulation service,
Oracle in the Cloud
Posted: May 6, 2008 10:43 AM PDT
TimeLine
From: Tonellato, Peter
Sent: Tuesday, June 24, 2008 12:09 PM
We have successfully launched the personalized medicine translational research platform on AWS. …
P
Peter J. Tonellato, Ph.D.
Center for Biomedical Informatics
Harvard Medical School
Children's Hospital of Boston
617.432.7185
866.771.2566 (fax)
Footnote:
The team never met together and more
than half had never worked together.
Warfarin Pharmacogenetic
Simulation Service Application
Goals
– Predict dosage to achieve rapid therapeutic dosing
– Create clinical ‘avatar’ patient-base – reflects real data
– Identify patients-types or sub-populations who may experience
difficulty achieving therapeutic Warfarin level
– Create flexible and extensible modular framework as the basis for
future translational science studies
Dosage/INR Prediction Overview
Models used for generating initial dosage:
Anderson et. Al.1 :
Dose = 1.64 + exp[3.984 + c(x) + v(x) + g(x) - age*(0.009) + weight*(0.003)]
{ 0 if genotype = CYP2C9*1/*1
{-0.197 if genotype = CYP2C9*1/*2
c(x) = {-0.360 if genotype = CYP2C9*1/*3
{-0.947 if genotype = CYP2C9*2/*3
{-0.265 if genotype = CYP2C9*2/*2
{-1.892 if genotype = CYP2C9*3/*3
{ 0 if VKORC1 1173 genotype = C/C
v(x) = {-0.304 if VKORC1 1173 genotype = C/T
{-0.569 if VKORC1 1173 genotype = T/T
CYP2C9 genotype
elements in this
algorithm are derived
from the CYP2C9
gene/allele generic
hash map
g(x) = { 0 if gender = female
{ 0.094 if gender = male
1. Anderson JL, Horne BD, Stevens SM, Grove AS, Barton S, Nicholas ZP, et al. Randomized trial of
genotype-guided versus standard warfarin dosing in patients initiating oral anticoagulation.
Circulation 2007 Nov 27;116(22):2563-2570.
Gage et. Al 2:
Dose = exp[0.9751 − 0.3238 × v(y) + (0.4317 × BSA) - 0.4008
× c_3(y) − (0.00745 × age) − 0.2066 × c_2(y) + (0.2029
× target INR) − (0.2538 x amiodarone) + (0.0922 ×smokes)
- (0.0901 × African-American race) + (0.0664 × DVT/PE)]
{ 0 if VKORC1 -1639 genotype = G/G
v(y) = { 1 if VKORC1 -1639 genotype = G/A
{ 2 if VKORC1 -1639 genotype = A/A
{ 0 if CYP2C9*2 genotype = C/C
c_2(y) = { 1 if CYP2C9*2 genotype = C/T
{ 2 if CYP2C9*2 genotype = T/T
{ 0 if CYP2C9*3 genotype = A/A
c_3(y) = { 1 if CYP2C9*3 genotype = A/C
{ 2 if CYP2C9*3 genotype = C/C
2. Gage B, Eby C, Johnson J, Deych E, Rieder M, Ridker P, et al. Use of Pharmacogenetic and Clinical Factors
to Predict the Therapeutic Dose of Warfarin. Clin.Pharmacol.Ther. 2008 Feb 27.
Variation of CYP2C9 Genotype (Gage Model)
G/G
12
10
4
2
0
A/A
G/A
G/G
A/A
G/A
*2/*2
*2/*3
*3/*3
G/G
10
0
2
4
6
Dosage (mg)
8
10
0
2
4
6
Dosage (mg)
8
10
8
6
4
2
G/A
VKORC1 Genotype
G/G
12
VKORC1 Genotype
12
VKORC1 Genotype
0
A/A
6
Dosage (mg)
8
10
0
2
4
6
Dosage (mg)
8
10
8
6
Dosage (mg)
4
2
0
G/A
VKORC1 Genotype
12
A/A
Dosage (mg)
*1/*3
12
*1/*2
12
*1/*1
A/A
G/A
VKORC1 Genotype
G/G
A/A
G/A
VKORC1 Genotype
G/G
Dosage vs. WSI by CYP2C9 Genotype
(20,000 patients)
Current Results
• LPM Warfarin Web App Completed in two months
• 100 Million clinical avatar and dosing simulations
• Translational Science paradigm supports clinical trial simulation,
incidentalome testing, and leads to new metrics for clinical efficacy
• New Metrics for Clinical Efficacy e.g. Warfarin ‘Sensitive’
Participants
We have demonstrated the value and flexibility of Cloud Services and
Framework for future projects.
Acknowledgements
Laboratory for Personalized Medicine
Peter J. Tonellato, Ph.D.
Vincent Fusaro
Rimma Pivovarov
Prasad Patil
Peter Kos
Zhitao Wang
Dan Chen
Haiping Xia
Sumana Ramayanam
Amazon:
Terry Wise
Kurt Messinger
Tenesha Gleason
Ford Harris
Projects
• Network Analysis for Disease Genetics
• The Translational Variome
• Next Generation Sequence Analysis
– DNA
– RNA
• i2b2
• Pharmacogenetics - with Clinical Avatars
• Cloud Computational Center
About i2b2 and Recombinant
i2b2: Informatics for
Integrating Biology and
the Bedside
“The i2b2 Center is developing a
scalable informatics framework that
will bridge clinical research data and
the vast data banks arising from
basic science research in order to
better understand the genetic bases
of complex diseases.”
http://www.i2b2.org
Service based “i2b2 Hive” open source framework
Recombinant Data Corp. (http://www.recomdata.com)
– Translational Research Open Source implementation and support
– i2b2 deployments: UMass, Johnson and Johnson, Wash U./UCSF/UC Davis collaboration
– Clinical data warehousing & integration services
i2b2 Running on Amazon Cloud
Objectives
• Establish an i2b2 AMI
• Test the AMI with clinical avatar data sets
• Create a model/QA environment for federated queries
using SHRINE
• Benchmark query performance with large SNP and gene
expression data sets
• Define a security model/requirements for deploying
sensitive clinical data in the cloud
• Investigate relevant implementation of high-compute
“cloud” models for correlation analysis
Rimma Pivovarov
February 22, 2009
The HiveMind of Mechanical Turks
(The Translational Variome)
Can crowdsourcing be used to solve common biomedical
information processing dilemmas?
Laboratory of Personalized Medicine
What is Mechanical Turk?
Database Annotation
Can the Turks extract variant data from dbSNP?
How much understanding of biology is necessary?
How long will this take?
How accurate will they be?
Accession Number
DNA Change
Amino Acid Change
HIT Design
10 RS Numbers = 10 tasks
3 individual Turks perform each task
10 x 3 = 30 Human Intelligence Tasks (HITs)
Results
Number of HITs Completed Over
Time
Total Number of HITs Completed
30
25
20
15
10
9
8
7
6
5
4
3
2
1
0
Correct
Incorrect
DNA Change
10
Number Correct
Accession ID
5
AA Change
% Correct
0
1/14/09 21:21 1/14/09 23:45 1/15/09 2:09 1/15/09 4:33 1/15/09 6:57 1/15/09 9:21
Time
DNA Change
100%
Accession ID
100%
Amino Acid Change
•
Time elapsed: 11.5 hours
•
Total Cost: 33 cents
•
7 Individual Turks Participated
Average
90%
96.6%
Abstract Interpretation
SHP2
HSP70
"The Src homology phosphotyrosyl phosphatase, SHP2, is a positive effector of EGFR
signaling. However, the molecular mechanism and biological functions of SHP2 regulation
are still not completely known. To better understand the cellular processes in which SHP2
participates, we carried out mass spectrometry to find SHP2 binding proteins. FLAG-SHP2
complexes were isolated by affinity purification, and associated proteins were identified by
in-gel trypsin digestion followed by LC/MS/MS mass spectrometry. Among the identified
proteins, we focus in this report on the heat shock protein 70 (HSP70). Physical
interactions of SHP2 with HSP70 were confirmed in vivo. Further experiments
demonstrate that EGF does not activate binding of SHP2 with HSP70 rather the binding
appears to be constitutive. However, the formation of an HSP70/SHP2 complex affected
the binding of SHP2 with EGFR and (or) GAB1. These data suggest that binding of HSP70
with SHP2 regulates to some extent the EGF signaling pathway. In addition,
immunostaining experiments indicated that SHP2 and HSP70 co-localized in the cell
membrane region after EGF treatment. Our findings propose a possible involvement of
HSP70 in the regulation of EGF signaling pathway by SHP2."
Turkers Response
Cloud Computing Center
AIM: Understand how to properly launch and configure AWS servers,
monitor performance and cost, and manage large volumes of data on
the cloud for a mixture of simultaneous start-up projects.
Lead by an Individual who does not know what he is doing.
Maintaining 'virtual' computing centers for each of the Palaver project
teams.
– Typical setting, launching 6-10 significant computing projects with
diverse hardware, software, flexibility and resource needs would take
some time (months?).
– We will attempt to manage startup needs in a matter of days and manage
them going forward with minimal effort ('minimal' to be determined!).
– Resource requirements implemented on AWS using RightScale
Cloud Computing Center
• Amazon is sponsoring resources
• Vince Fusaro will wrangle resources
• Each Project lead will predict needs and coordinate
through Vince
• “Special” requests will be managed directly with AWS –
and must be justified, ….
• William Crawford will conduct meta-analysis of use and
implementation. Please interact with him as needed.
• RightScale is interested in our experiences
RightScale
• Manage virtual
servers
• Monitor usage
statistics
Palaver WebSite
Website created and managed by
Rimma Pivovarov
Website designed by
Kristian St. Gabriel
Logistics
• Monday 3-5 pm from now on….
• Monitor the Web site for updates
• Review the Project sites in the next week or so and confirm your level of
participation
• Technical glitches …
• Rimma on lecture/ Website issues
• Vince on Project Computational Center issues.
• Project Team Leaders –
• Will refine Project statement, Coordinate participants, project (skype)
meetings, project logistics
• Palaver Day – May 6th
• Other??
Translational Science
on the Cloud
Amazon Web Services: A Clouded Architecture
Jinesh Varia
Amazon