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  1. 1. Translational Science on the Cloud An Experiment in Translational Science Peter J. Tonellato Dennis Wall Center for Biomedical Informatics Harvard Medical School
  2. 2. pa lav er⋅ ⋅    /p læv r, l v r/ nounəˈ ə ‑ˈ ɑ ə 1. long parley usually between persons of different cultures or levels of sophistication 2. conference, discussion 3. idle talk 4. misleading or beguiling speech
  3. 3. 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.
  4. 4. 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
  5. 5. Gartner Warnings Best to avoid Peaks and Troughs if Possible.
  6. 6. 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.
  7. 7. 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
  8. 8. 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
  9. 9. • 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)
  10. 10. 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
  11. 11. 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
  12. 12. 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
  13. 13. 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 TimeLine
  14. 14. Amazon Web Services (AWS)Amazon Web Services (AWS) HPC AMI HPC AMI Amazon S3 Amazon S3 Oracle AMI Oracle AMI Amazon EC2 Instances Amazon EC2 Instances User Application User Application Linux Server Linux Server Fitting the Pieces Together
  15. 15. 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
  16. 16. 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)
  17. 17. “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,
  18. 18. 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.
  19. 19. 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
  20. 20. 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
  21. 21. 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.
  22. 22. Variation of CYP2C9 Genotype (Gage Model) A/A G/A G/G 024681012 *1/*1 VKORC1 Genotype Dosage(mg) A/A G/A G/G 024681012 *1/*2 VKORC1 Genotype Dosage(mg) A/A G/A G/G 024681012 *1/*3 VKORC1 Genotype Dosage(mg) A/A G/A G/G 024681012 *2/*2 VKORC1 Genotype Dosage(mg) A/A G/A G/G 024681012 *2/*3 VKORC1 Genotype Dosage(mg) A/A G/A G/G 024681012 *3/*3 VKORC1 Genotype Dosage(mg)
  23. 23. Dosage vs. WSI by CYP2C9 Genotype (20,000 patients)
  24. 24. 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.
  25. 25. 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
  26. 26. Projects • Network Analysis for Disease Genetics • The Translational Variome • Next Generation Sequence Analysis – DNA – RNA • i2b2 • Pharmacogenetics - with Clinical Avatars • Cloud Computational Center
  27. 27. About i2b2 and Recombinant Recombinant Data Corp. ( –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.” i2b2: Informatics for Integrating Biology and the Bedside Service based “i2b2 Hive” open source framework
  28. 28. 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
  29. 29. 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
  30. 30. What is Mechanical Turk?
  31. 31. 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?
  32. 32. 10 RS Numbers = 10 tasks 3 individual Turks perform each task 10 x 3 = 30 Human Intelligence Tasks (HITs) HIT Design
  33. 33. Results TotalNumberofHITsCompleted 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
  34. 34. 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."
  35. 35. Turkers Response
  36. 36. 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
  37. 37. 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
  38. 38. RightScale • Manage virtual servers • Monitor usage statistics
  39. 39. Palaver WebSite Website created and managed by Rimma Pivovarov Website designed by Kristian St. Gabriel
  40. 40. 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??
  41. 41. Translational Science on the Cloud Amazon Web Services: A Clouded Architecture Jinesh Varia Amazon