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Quantitative Medicine Feb 2009
- 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
- 35. Thank you! Computation Institute www.ci.uchicago.edu www.ci.anl.gov
Notes de l'éditeur
- Roberto Barrera – impressive set of contributions John Delaney – persuaded you that you should all start working on ocean observation I have worked for many years with some incredible people in the physical sciences, working to understand some fascinating phenomena, such as the nature of mass and the causes and likely effects of climate change. These people have been the early leaders in developing and applying grid technology, via such projects as the LHC Computing Grid and the Earth System Grid. Jonathan: 1) Evidence-based medicine is use of carefully evaluating the results (called outcomes) of different diagnostic or therapeutic procedures to determine the best choice for a population of patients. Then a physician, with this information at his/her disposal (a complicated problem to have that happen in itself) can make the best decision for the individual patient by looking at the characteristics of the studied patients and his/her own patient (N of 1) and can make recommendations (patients, not doctors, make choices) 2. personalized medicine (in a nutshell here - purists might disagree) is2:58using much finer distinguishing characteristics to do the same thing such as specific genomic studies that ensure that the N of one patient is precisely matched with the same sub-sub-sub population of patients2:59Thus, the distinction at some level, blurs, when we have enough examples of personalized medicine (it becomes the evidence-based medicine of the future) but for now all we have is evidence-based medicine (a much more blunt instrument) with the same goal