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5 Φεβ 2013 (πριν από 4 χρόνια και 8 μήνες)

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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



‘I like to more than watch’


above and join active project team


contribution to project objectives

* To Doug MacFadden for
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


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

Participants

(incomplete list)

Acknowledgements

Vincent Fusaro

Prasad Patil

Peter Kos

Zhitao Wang

Dan Chen

Haiping Xia

Sumana Ramayanam


Laboratory for Personalized Medicine

Peter J. Tonellato, Ph.D.

Amazon:


Tenesha Gleason


Ford Harris


Wall Lab:


Dennis Wall, Ph.D.


Tom Monaghan


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



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

bill.hodak@oracle.com

TimeLine

Amazon Web Services (AWS)

HPC

AMI

Amazon

S3

Oracle
AMI

Amazon

EC2

Instances

User
Application

Linux

Server

Fitting the Pieces Together

Math Modeling and Simulation

HPC Cloud Service

Simulation as Service Options


Matlab


Mathematica


R


SAS


S
-
PLUS

R Benefits:


Fast computation and
statistical analysis


Large mathematical and
statistical library


Open source


Highly extensible


Supportive user
community


OpenXava

Application Ready for
Production

Business
Components

Controllers

+

=



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



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)

TimeLine

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


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.

CYP2C9 genotype
elements in this
algorithm are derived
from the CYP2C9
gene/allele generic
hash map

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)

A/A
G/A
G/G
0
2
4
6
8
10
12
*1/*1
VKORC1 Genotype
Dosage (mg)
A/A
G/A
G/G
0
2
4
6
8
10
12
*1/*2
VKORC1 Genotype
Dosage (mg)
A/A
G/A
G/G
0
2
4
6
8
10
12
*1/*3
VKORC1 Genotype
Dosage (mg)
A/A
G/A
G/G
0
2
4
6
8
10
12
*2/*2
VKORC1 Genotype
Dosage (mg)
A/A
G/A
G/G
0
2
4
6
8
10
12
*2/*3
VKORC1 Genotype
Dosage (mg)
A/A
G/A
G/G
0
2
4
6
8
10
12
*3/*3
VKORC1 Genotype
Dosage (mg)
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

Vincent Fusaro

Rimma Pivovarov

Prasad Patil

Peter Kos

Zhitao Wang

Dan Chen

Haiping Xia

Sumana Ramayanam


Laboratory for Personalized Medicine

Peter J. Tonellato, Ph.D.

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

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

“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

i2b2: Informatics for
Integrating Biology and
the Bedside

Service based “i2b2 Hive” open source framework

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

The
HiveMind

of Mechanical Turks

(The Translational
Variome
)

Laboratory of Personalized Medicine

Can crowdsourcing be used to solve common biomedical
information processing dilemmas?

Rimma Pivovarov






February 22, 2009

What is Mechanical Turk?

Database Annotation

DNA Change

Amino Acid Change

Accession Number

How much understanding of biology is necessary?

How long will this take?

How accurate will they be?


Can the Turks extract variant data from dbSNP?


10 RS Numbers = 10 tasks

3 individual Turks perform each task

10 x 3 = 30 Human Intelligence Tasks (HITs)

HIT Design

Results

Total Number of HITs Completed

0
5
10
15
20
25
30
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
Number Correct

% Correct

DNA Change

100%

Accession ID

100%

Amino Acid Change

90%

Average

96.6%



Time elapsed: 11.5 hours



Total Cost: 33 cents



7 Individual Turks Participated





Time


Number of HITs Completed Over
Time


0
1
2
3
4
5
6
7
8
9
10
Correct
Incorrect
DNA Change

Accession ID

AA Change

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 6
th




Other??

Translational Science

on the Cloud


Amazon Web Services: A Clouded Architecture

Jinesh Varia


Amazon