09 02 15 Palaver PJT

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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,-ˈl vər/ noun
               ɑ

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
                                                noting the “Being There”
                                                connection.
• „I like to more than watch‟
   – above and join active project team
   – contribution to project objectives
                         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
                                            Wall Lab:
Prasad Patil                                  Dennis Wall, Ph.D.
Peter Kos                                     Tom Monaghan
Zhitao Wang
Dan Chen                                     Amazon:
Haiping Xia                                    Tenesha Gleason
                                               Ford Harris
Sumana Ramayanam
          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
bill.hodak@oracle.com
Fitting the Pieces Together

      User
    Application


      Linux
      Server
                   Oracle     Amazon    HPC
                    AMI         S3      AMI
     Amazon
      EC2
    Instances

            Amazon Web Services (AWS)
         Math Modeling and Simulation
                     HPC Cloud Service
Simulation as Service Options R Benefits:
   –   Matlab                    – Fast computation and
   –   Mathematica                 statistical analysis
   –   R                         – Large mathematical and
   –   SAS                         statistical library
   –   S-PLUS                    – Open source
                                 – Highly extensible
                                 – Supportive user
                                   community
                        OpenXava

Business                              Application Ready for
Components      +   Controllers   =   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                  Footnote:
Harvard Medical School                             The team never met together and more
Children's Hospital of Boston                      than half had never worked together.
617.432.7185        866.771.2566 (fax)
                  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                                CYP2C9 genotype
           {-0.947 if genotype = CYP2C9*2/*3
           {-0.265 if genotype = CYP2C9*2/*2
                                                                             elements in this
           {-1.892 if genotype = CYP2C9*3/*3                                 algorithm are derived
                                                                             from the CYP2C9
           { 0 if VKORC1 1173 genotype = C/C                                 gene/allele generic
     v(x) = {-0.304 if VKORC1 1173 genotype = C/T
           {-0.569 if VKORC1 1173 genotype = T/T
                                                                             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)
                              *1/*1                                            *1/*2                                            *1/*3
              12




                                                               12




                                                                                                                12
              10




                                                               10




                                                                                                                10
              8




                                                               8




                                                                                                                8
Dosage (mg)




                                                 Dosage (mg)




                                                                                                  Dosage (mg)
              6




                                                               6




                                                                                                                6
              4




                                                               4




                                                                                                                4
              2




                                                               2




                                                                                                                2
              0




                                                               0




                                                                                                                0
                   A/A        G/A          G/G                      A/A        G/A          G/G                      A/A        G/A          G/G

                         VKORC1 Genotype                                  VKORC1 Genotype                                  VKORC1 Genotype



                              *2/*2                                            *2/*3                                            *3/*3
              12




                                                               12




                                                                                                                12
              10




                                                               10




                                                                                                                10
              8




                                                               8




                                                                                                                8
Dosage (mg)




                                                 Dosage (mg)




                                                                                                  Dosage (mg)
              6




                                                               6




                                                                                                                6
              4




                                                               4




                                                                                                                4
              2




                                                               2




                                                                                                                2
              0




                                                               0




                                                                                                                0
                   A/A        G/A          G/G                      A/A        G/A          G/G                      A/A        G/A          G/G

                         VKORC1 Genotype                                  VKORC1 Genotype                                  VKORC1 Genotype
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                          Amazon:
Dan Chen                               Terry Wise
Haiping Xia                            Kurt Messinger
Sumana Ramayanam                       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 Correct
                                 Number of HITs Completed Over
                                 30
                                                                                               10
                                             Time                                               9
Total Number of HITs Completed




                                                                                                8
                                 25
                                                                                                7
                                                                                                6
                                                                                                5                                             Correct
                                 20
                                                                                                4                                             Incorrect
                                                                                                3
                                                                                                2
                                 15
                                                                                                1
                                                                                                0
                                                                                                    DNA Change   Accession ID   AA Change
                                 10



                                  5
                                                                                                                                % Correct
                                  0                                                           DNA Change                                    100%
            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
                                                                                              Accession ID                                  100%
                                                                 Time
                                                                                              Amino Acid Change                              90%
                                      •   Time elapsed: 11.5 hours
                                                                                                                 Average                    96.6%
                                      •   Total Cost: 33 cents
                                      •   7 Individual Turks Participated
                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

				
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