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Health-grid essentials

Health-grid essentials. Peter Sloot University of Amsterdam. Medical. Genomics. Proteomics. Immunology. DNA. Proteins. Cellular. Pharma- ceutical. Treatment. Mutations. Protease Reverse Transcriptase. CD-4 Experssion # RNA particles. Vivo- Vitro- Experimentation Silico-.

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Health-grid essentials

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  1. Health-grid essentials Peter Sloot University of Amsterdam

  2. Medical Genomics Proteomics Immunology DNA Proteins Cellular Pharma- ceutical Treatment Mutations Protease Reverse Transcriptase CD-4 Experssion # RNA particles Vivo- Vitro- Experimentation Silico- Time 10-14 sec Years Space 10-10 m 10-1 m Molecule Man From Molecule to Man

  3. Analytic Molecular Dynamics Monte Carlo Mesoscopic Time 10-14 sec Years Space 10-10 m 10-1 m From Molecule to Man… • First Principle Modeling • Genetic Regulatory Networks • Metabolic Networks • Immunological Networks • … Silicon Cell • Hierarchical data Modeling • G-P-M & Patient Dbases AI – GA’s, NN’s, Fuzzy L. Molecule Man

  4. Time 10-14 sec Years Space 10-10 m 10-1 m From Molecule to Man…GRID • High Performance Computing => Mesoscopic Simulation • High Throughput Computing => Parameter Space Exploration • Data Disclosure => Dbase Federation and Integration • Data Fusion => Parameter Transfer • Access => Visualization/VR && Roaming and Remote &&PDA

  5. X#

  6. What DRIVeS the X# ? • Distributed • Real-time • Interactive • Visualization • (E) • Simulation

  7. Goal of WP 1 • Applications in health and environment • Data gathering, processing and interpretation in geographically distributed locations • Fast, interactive decision making • Interactive access to distributed • Databases • Super computers and High Performance Clusters • Visualisation engines • Medical scanners • Environmental data input devices

  8. CrossGrid structure The applications are rooted in the underlying common environment. The more they share, the firmer the stand. Distinct Applications (WP 1) Application programming environment (WP 2) New grid services (WP 3) General grid services (a.o. Globus)

  9. -Links with European National efforts - Links with US projects (GriPhyN, PPDG, iVDGL,…) Applications EGSO CROSSGRID GRIA EUROGRID DATAGRID GRIP GRIDLAB DAMIEN Middleware & Tools DATATAG Underlying Infrastructures Industry / business Science A new IST Grid project space (Kyriakos Baxevanidis)

  10. Three central functionalities • Data gathering • Data generators and data bases geographically distributed • Selected on demand • Processing • Needs large processing capacity on demand • Interactive • Presentation • Complex data require versatile 3D visualisation • Support interaction and feedback to other components

  11. WP 1 structure Task 1.1 Surgery planning & visualisation Task 1.2 Flooding control MIS Task 1.3 HEP data analysis Task 1.4 weather & pollution modelling Task 1.0 Co-ordination & management

  12. A Case study from biomedicine...

  13. In Vitro In Vivo In Silico Changing the Paradigm

  14. In Vitro In Vivo In Silico Changing the Paradigm

  15. In Vitro In Vivo In Silico Changing the Paradigm

  16. Diagnosis & Planning Observation Current Situation Treatment

  17. WP 1.1 • Nature March 2002

  18. Some Key Data for WP 1.1 • New Scanners 1024 x 1024 • 128 slices of 2 byte depth==> 256 MByte • 10 images per systole= 1 per second

  19. Design Considerations • High Quality presentation • High Frame rate • Intuitive interaction • Real-time response • Interactive Algorithms • High performance computing and networking… • Distributed Resources and Data

  20. Runtime Support • Need generic framework to support modalities • Need interoperability • High Level Architecture (HLA): • data distribution across heterogeneous platforms • flexible attribute and ownership mechanisms • advanced time management

  21. Provoking a bit… Progress in natural sciences comes from taking things apart ... Progress in the X# comes from bringing things together...

  22. Monolith, Cluster Cave, Wall, PC, PDA MRI, PET Experimental set-up

  23. Immersive Environments

  24. 3D Information and Interaction

  25. GRIDWARE Architecture

  26. Simulation Based Planning and Treatment • Diagnostic Findings • Occluded right iliac artery • 75% stenosis in left iliac artery • Occluded left SFA • Diffuse disease in right SFA

  27. Methods - MR Imaging MR Scan of Abdomen MR Scan of Legs

  28. Methods - Geometric Models

  29. VR-Interaction

  30. Alternate Treatments Preop AFB w/ E-S Prox.Anast. AFB w/ E-E Prox.Anast. Angio w/Fem-Fem Angio w/ Fem-Fem & Fem-Pop Courtesy Prof. C. Taylor

  31. Flow through complex geometry • After determining the vascular structure simulate the blood-flow and pressure drop… • Conventional CFD methods might fail: • Complex geometry • Numerical instability wrt interaction • Inefficient shear-stress calculation

  32. 10 cm/sec 0 cm/sec Velocity Magnitude

  33. Peak Systolic Pressures – Rest 150 mmHg 50 mmHg Preop AFB w/ E-S Prox.Anast. AFB w/ E-E Prox.Anast. Angio w/Fem-Fem Angio w/ Fem-Fem & Fem-Pop

  34. T.S. Elliot ‘How much wisdom has been lost in knowledge and how much knowledge has been lost in information...’ How much Information has been lost in Data!!

  35. Fast, High-throughput Low Latency Internet High Performance Super Computing New Possibilities • Time and Space Independence • 3D Information • Simulation based planning • Surgeon ‘in the loop’ X#

  36. The silicon cell: towards computing living cells

  37. From cells to molecules

  38. Mesoscopic modelling of living cells • Models of genetic regulatory networks and spatio-temporal gene expression • Models of biochemical pathways and reactivity at complex-shaped membranes • models of spatial structures: membranes, cytoskeleton, chromatin, aggregates of cells,.. • ...

  39. Development of morphogen gradients in time (left) and regulatory network for segment specification in Drosophila (Carroll,2001)

  40. Model for a genetic network and the formation of patterns of morphogens in linear row of cells(Salazar-Ciudad, 2001)

  41. The glucose phosphotransferase system (PTS) in E. coli

  42. Example: modelling uptake and metabolism of glucose by PTS at the membrane

  43. Towards computing living cells • Van Kampen, Amsterdam Medical Centre • Westerhoff , Free University of Amsterdam • Blom & Peletier, Centre for Mathematics and Computer Science, Amsterdam

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