1. Quantitative medicine A “killer app” for grid Ian Foster Computation Institute Argonne National Lab & University of Chicago

2. Thanks in particular to … Carl Stephan Steve Ravi Jonathan Kesselman Erberich Tuecke Madduri Silverstein

3. Quantitative medicine is the key to reducing healthcare costs and improving healthcare outcomes Patients with same diagnosis

4. Quantitative medicine is the key to reducing healthcare costs and improving healthcare outcomes Patients with same diagnosis Misdiagnosed Non-responders, toxic responders Non-toxic responders

5. Major drugs ineffective for many… Asthma Drugs 40-70% Beta-2-agonists Hypertension Drugs 10-30% ACE Inhibitors Heart Failure Drugs 15-25% Beta Blockers Anti Depressants 20-50% SSRIs Cholesterol Drugs 30-70% Statins Source: Amy Miller, Personalized Medicine Coalition

6. Same clinical disease, but different response to same chemotherapy, depending on gene expression profile Patient ID Number Danenberg Tumor Profile Scale Colorectal cancer: clinical trial data Salonga et al. Clin Cancer Res 2000; 6: 1322-1327.

8. Personalized medicine is quantitative The right treatment for the right person at the right time Trial and Error Personalized Medicine Current Practice One size fits all Trial and error Source: Amy Miller, Personalized Medicine Coalition

9. To realize the promise of quantitative medicine, we must break down barriers to information sharing … Discovering effective personalized treatments Determining the right treatment for the individual … and deliver new analytical tools to make sense of large quantities of data

12. Virtual organizations Grids and SOA

13. Children’s Oncology Group Grid Globus

14. Childrens’ Oncology Grid clinical imaging trials (Erberich)

20. As of Oct 19 , 2008: 122 participants 105 services 70 data 35 analytical

22. Outsourcing analysis: caBIG’s geWorkbench/TeraGrid interface R. Madduri, U.Chicago, Taverna team

24. Functional BIRN (fBIRN) information integration vision Multi-Site User Query Data Provenance Information Derived data processing FIPS Results FMRI/MRI Images Processing Pipelines HIDB(s) (Distributed) Data Grid Clinical Data Input DICOM, NIFTI fMRI Scanner

25. FBIRN multi-site study, 2006 UNM UMN UI UCI BWH MGH UCLA UCSD Stanford Duke/ UNC Yale = 3 or 4T site = 1.5T site = Development site

27. Health informatics services model Analysis Management Integration Publication Policy and Security Decision Support Radiology Medical Records Labs Pathology Genomics Applications Source: Carl Kesselman

28. Decision support for HIV drug ranking (Peter Sloot et. al)

29. Clinical Parameters: -weight - opportunistic infections and tumors -survival Molecular Dynamics Binding Affinity Protein Structure & Binding Affinity VIROLAB DRUG RANKING DECISION SUPPORT Text Mining  Drugranking  1 st order logic Complex Networks Epidemics Agent-Based Entry Simulation Phenotype CA Based Immune Response Protease and RT mutations

30. Virolab: DSS Virtual Laboratory Experiment developer Scientist Clinical Virologist Experiment Planning Environment Experiment scenario ViroLab Portal Virtual Laboratory runtime components (Required to select resources and execute experiment scenarios) Computational services (WS, WSRF, components, jobs) Data services (DAS data sources, standalone databases) Grids (EGEE), Clusters, Computers, Network Users Interfaces Runtime Services Infrastructure Drug Ranking Scenario

31. Many many tasks: Identifying potential drug targets 2M+ ligands Protein x target(s) (Mike Kubal, Benoit Roux, and others)

32. start report DOCK6 Receptor (1 per protein: defines pocket to bind to) ZINC 3-D structures ligands complexes NAB script parameters (defines flexible residues, #MDsteps) Amber Score: 1. AmberizeLigand 3. AmberizeComplex 5. RunNABScript end BuildNABScript NAB Script NAB Script Template Amber prep: 2. AmberizeReceptor 4. perl: gen nabscript FRED Receptor (1 per protein: defines pocket to bind to) Manually prep DOCK6 rec file Manually prep FRED rec file 1 protein (1MB) PDB protein descriptions For 1 target: 4 million tasks 500,000 cpu-hrs (50 cpu-years) 6 GB 2M structures (6 GB) DOCK6 FRED ~4M x 60s x 1 cpu ~60K cpu-hrs Amber ~10K x 20m x 1 cpu ~3K cpu-hrs Select best ~500 ~500 x 10hr x 100 cpu ~500K cpu-hrs GCMC Select best ~5K Select best ~5K

34. NAE Grand Challenges

35. Thank you! Computation Institute www.ci.uchicago.edu www.ci.anl.gov