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From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
From molecule to man by Peter V. Coveney
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From molecule to man by Peter V. Coveney

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  • 1. From Molecule to Man: the VirtualPhysiological Human, ComputationalBiology & the Future of BiomedicinePeter V. CoveneyCentre for Computational ScienceUniversity College London
  • 2. VPH and Computational Biology Issues Presentation Outline •  Context •  VPH Initiative –  VPH Projects –  Role of VPH NoE •  Centre for Computational Science Research •  UCL/Yale Partnership •  “Digital Me” – a vision of future healthcare provision
  • 3. VPH/Physiome HistoryHuman Genome Project Systems Biology Grid ComputingFinite ElementsMicrocomputers/home FP7 call 2computers Objective ICT-2007.5.3:Molecular Biology Virtual Physiological Human 1993 1997 2005 2006 2007 2008 2009
  • 4. VPH and Computational Biology Issues Integrative and systems biology •  Data produced by sequencing  10TB per day per machine •  Integrating over all relevant length and timescales •  Requires integration of suitable compute and data facilities, connected by high performance networks. •  VPH seen as paradigm case for putative BBSRC Digital Organisms programme. •  Exploiting ‘big data’ – seizing new opportunities to generate knowledge and impact as we enter an era of data intensive science. •  Tools, resources and facilities – providing the tools, technologies and infrastructures that are essential for 21st century bioscience. •  Increase the uptake of systems-based approaches in the private sector.
  • 5. VPH and Computational Biology Issues Funding: Budget for research supported by the European Commission ICT - Health unit in FP7 Overall € 340 M over 4 years (2007-2010) Challenge 5 •  Predictive Medicine–Virtual Physiological Human (VPH Initiative) –  Modelling/simulation of human physiology and disease € 72 M in 2007 - € 5 M in 2009 - € 68 M in 2010 •  Personalisation of Healthcare –  Personal Health Systems (PHS), € 72 M in 2007 - € 63 M in 2009 •  Patient safety (PS) - avoiding medical errors –  € 30 M in 2007 - € 30 M in 2009
  • 6. VPH and Computational Biology Issues The VPH Initiative is currently in its 3rd Call. It focuses on: •  Keep the overall VPH vision: –  Early diagnostics & Predictive medicine –  Personalised (Patient-specific) healthcare solution •  By means of: –  Modelling & simulation of human physiology and disease related processes –  Emphasis on tools/infrastructure for bio-med researchers+clinicians •  VPH in 2010 –  Call 6 opens November 2009 , closing 13 April 2010 –  Budget € 63M
  • 7. VPH and Computational Biology Issues Overall planning for the elaboration of the EU Workprogramme 2011-12: •  Orientation paper developed by Christmas 2009 •  First draft of the Workprogramme by February 2010 •  Final draft of the Workprogramme by May 2010
  • 8. VPH and Computational Biology Issues •  Elaboration of WP2011-12: –  Drafted by the Commission –  Based on community inputs (research/clinical/industry) •  The NoE Vision Document is an essential input for the VPH –  It presents the view of the Community at large on gaps, challenges and future direction and informs EC policy •  There will be annual updates with open consultation in the community through the NoE website. •  Don’t miss this opportunity to contribute!
  • 9. VPH and Computational Biology IssuesThe VPH Initiative (VPH – I) & VPH Network of Excellence•  Collaborative projects within the call meet objectives associated with specific challenges•  VPH NoE connects all of these projects, and must focus on addressing issues of common concern that affect VPH-I projects collectively –  research infrastructure –  training –  dissemination
  • 10. Virtual Physiological Human•  Funded under EU FP 7 (call 2)•  15 projects: 1 NoE, 3 IPs, 9 STREPs, 2 CAs, and new projects in negotiation phase “a methodological and technological framework that, once established, will enable collaborative investigation of the human body as a single complex system ... It is a way to share observations, to derive predictive hypotheses from them, and to integrate them into a constantly improving understanding of human physiology/pathology, by regarding it as a single system.” VPH Call 6 currently open – deadline13th April 2010
  • 11. VPH- I FP7 current projects Industry Parallel VPH projects Grid access CA CV/ Atheroschlerosis IP Liver surgery STREP Breast cancer/ diagnosis Heart/ LVD surgery STREP STREP Osteoporosis Oral cancer/ BM IP D&T STREP Networking Heart /CV disease NoE Cancer STREP STREP Vascular/ AVF & Liver cancer/RFA haemodialysis STREP therapy STREP Heart /CV disease Alzheimers/ BM & STREP diagnosis STREP Security and Privacy in VPH CA Other Clinics
  • 12. VPH and Computational Biology Issues VPH NoE •  VPH NoE - Virtual Physiological Human Network of Excellence •  Primary purpose to strengthen the VPH community and provide tools and services for researchers in the field •  Support VPH-I projects directly Start Date 
 2008-06-01
 End Date 
 2012-11-30
 Project Funding 
 7.99M€ EU funding) © VPH NoE, 2008
  • 13. VPH and Computational Biology Issues Specifically, the VPH NoE will: •  Identify user needs, define standards, ontologies and applications, and develop VPH ToolKit •  Develop VPH training activities and materials: Joint advanced degree programme, interdisciplinary study groups, focused journal issues, textbook •  Provide research/news dissemination services and international EU/international networking Project Coordinators: Vanessa Díaz-Zuccarini / Miriam Mendes (UCL) Scientific Coordinators: Peter Coveney (UCL), Peter Kohl (Oxford) http://www.vph-noe.eu
  • 14. VPH and Computational Biology Issues •  14 Core Partners –  4 UK (UCL, UOXF, UNOTT, USFD) –  3 France (CNRS, INRIA, ERCIM) –  2 Spain (UPF, IMIM) –  1 Germany (EMBL [EBI]) –  1 Sweden (KI) –  1 Belgium (ULB) –  1 New Zealand (UOA) –  1 Italy (IOR) •  Associate / General Members –  38 General (academic) and Associate (Industrial) Members –  … and growing
  • 15. VPH and Computational Biology Issues Future results and impacts •  Create a more cohesive VPH research community, both within and beyond the EU. •  Enhance recognition at national level of importance of modelling and simulation in biomedicine •  Increase Industrial and Clinical awareness of VPH modelling •  Increase emphasis on interdisciplinary training in biological and biomedical/engineering physics curricula
  • 16. VPH and Computational Biology Issues Project Structure WP4: WP5: INTEGRATION SPREADING AND TRAINING ACTIVIES EXCELLENCE WP2 - Exemplar Projects Vertical integration WP1 - Management WP3 - VPH ToolKit
  • 17. VPH and Computational Biology Issues Project Structure WP2 – Exemplar ProjectsWP5 - Networking/Communication andSpreading Excellence within the VPHNoE/VPH-I WP1 - Management WP4 – Integration & WP3 – VPH Toolkit Training activities
  • 18. WP1VPH NoE General Assembly - Key Legislative Body(Includes all membership types)Annual Meetings (coinciding with Project Meetings)Delegates Executive Powers to Steering Committee Steering Committee: Clinical Advisory Board Key Executive Body Includes Consortium Agreement Signatories Industry Quarterly meetings to lead implementation of NoE WPs Advisory Board Takes advice from Advisory Boards May set up Task Forces on Policy Issues (such as on IP, Ethics, Gender) Scientific Delegates day-to-day project management to WP1 - Management Advisory Board Project Office UCL European Research & Development Office EC Project Officer Contract management and project implementation WP2 WP3 WP4 WP5Other VPH Other VPH Other VPH Other VPH Other VPHProjects Projects Projects Projects Projects
  • 19. VPH and Computational Biology Issues WP2 EPs work towards integration amongst VPH researchers, in order to address specific research problems or challenges. •  Provide solid examples of horizontal and vertical model/ data integration •  Two-way symbiosis with the development of VPH ToolKit (WP3), guiding ToolKit development •  WP2 activity began with preparation of VPH Research Strategy Document, which pinpoints priority research areas for calls © VPH NoE, 2008
  • 20. VPH and Computational Biology Issues WP2 •  2008-2009: five integrative research projects carried out by VPH core members - seed Exemplar Projects •  2009+: annual open call for EP proposals from NoE core membership •  Individual EP support given as 6-12-month grant to support personnel costs only •  Reviewed by a WP2 Board with advice from Scientific, Clinical and Industrial Advisory Boards •  2010 call will be issued shortly to partners and general members. Focus on sustainability of the WP3 Toolkit © VPH NoE, 2008
  • 21. VPH and Computational Biology Issues WP3 The VPH ToolKit aims to become the technical and methodological framework to support and enable VPH research. •  The toolkit will be a shared and mutually accessible source of research equipment, managerial and research infrastructures, facilities and services •  Other VPH projects, including the Exemplar Projects (EPs), will add to and draw capacity from it •  Can only be achieved through the integration of disparate knowledge and research infrastructure © VPH NoE, 2008
  • 22. VPH and Computational Biology Issues WP3 •  Activities include: •  Creation of mark up languages for model and data encapsulation •  VPH software tools and GUIs •  Model and data repositories •  Grid access using VPH ToolKit •  Workflow environments & middleware © VPH NoE, 2008
  • 23. VPH and Computational Biology Issues WP4 Development of a portfolio of interdisciplinary training activities including a formal consultation on, and assessment of, VPH careers •  Training initiatives in integrative and systems biology, both event-based (Study Groups) and allowing rotation between labs within the VPH NoE (early- and in-career) •  Development of distance learning/training resources for VPH and associated virtual/interactive communication methods, possible joint (inter-university) degree programme •  Development of VPH publishing (textbook and journal special issues) © VPH NoE, 2008
  • 24. VPH and Computational Biology Issues WP4 •  Address training and career development for both early and in-career VPH researchers •  Make use of existing studies for interdisciplinary Europe- wide Study programme –  Marie Curie programme •  Identify and quantify demand for career paths –  Healthcare/clinical scientist –  Industry –  Academia •  Ultimate goal: design and implement actions directed at the development of VPH research education and careers © VPH NoE, 2008
  • 25. VPH and Computational Biology Issues WP5 Development of an International VPH Gateway to facilitate interaction and co-operation between VPH initiative projects and practitioners •  Development of the VPH NoE website (public and private faces) •  Publish annual scientific roadmaps which chart Network progress/assist in ongoing planning of activities •  Publish VPH special issues in world-class journals •  Design, prepare and produce a wide array of VPH dissemination materials and events, including 6 monthly VPH-I newsletter © VPH NoE, 2008
  • 26. VPH and Computational Biology Issues WP5 Activities •  VPH NoE website development with dedicated WP and VPH I pages. •  Coordination of the consultation for the ‘Vision Document’ to inform future EC VPH calls and programs •  VPH special issues published in Phil Trans R Soc A •  Creation of VPH-I working group and Scientific, Industrial and Clinical Boards. •  Dissemination strategy includes continual updates on developments in WP2, WP3 and WP4 and VPH-I projects via the VPH NoE website, internet sites, mailing lists and newsletters •  Organisation of workshops to foster engagement with Industry •  VPH 2010 – 30 September/ 1st October 2010, Brussels First conference in a new VPH series of high profile conferences. Call for papers open, please visit website. © VPH NoE, 2008
  • 27. VPH201030 September – 1 October 2010Brussels, Belgium•  Dedicated to the VPH Initiative•  Representatives from VPH groups, Clinics and Industry•  Open to all (register online) Abstracts by 1st May 2010 http://www.vph-noe.eu/VPH2010 First of a series of VPH conferences supported by the European Commission (DG-INFSO) © VPH NoE, 2008
  • 28. Centre for Computational Science Advancing science through computers •  Computational Science •  Algorithms, code development & implementation •  High performance & distributed computing •  Visualisation & computational steering •  Condensed matter physics & chemistry, materials & life sciences •  Translational medicine, e-Health & VPH
  • 29. CCS – Accessing Federated ResourcesTeraGrid NGS UK NGS HPCx Globus Leeds Manchester Globus Oxford RAL AHE Yale resources DEISA GridSAM UNICORE
  • 30. Biomedical application have“non-standard” requirements •  Ability to co-reserve resources (HARC) •  Launch emergency simulations (SPRUCE) •  Uniform interface for federated access •  Access to back end nodes: steering, visualisation •  Lightpath network connections •  Cross site simulations (MPIg) •  Support for software (ReG steering toolkit etc)
  • 31. These requirements impact resourceprovider policies •  TeraGrid, NGS & HPCx starting to support advanced reservation with HARC •  DEISA evaluating HARC deployment on their systems •  Some TeraGrid sites support emergency jobs with SPRUCE •  Lightpath connections in place between Manchester - Oxford NGS nodes and Manchester - UCL •  MPIg and RealityGrid steering deployed on NGS and TeraGrid resources
  • 32. CCS -HIV drug design/drug treatment HIV-1 Protease is a common target for HIV drug therapy•  Enzyme of HIV responsible for Monomer B Monomer A protein maturation 101 - 199 1 - 99•  Target for Anti-retroviral Inhibitors Flaps•  Example of Structure Assisted Drug Glycine - 48, 148 Design•  9 FDA inhibitors of HIV-1 protease SaquinavirSo what’s the problem?•  Emergence of drug resistant mutations in protease•  Render drug ineffective P2 Subsite Catalytic Aspartic•  Drug resistant mutants have Acids - 25, 125 emerged for all FDA inhibitors Leucine - 90, 190 C-terminal N-terminal
  • 33. CCS - Determination of protein-drug binding affinities Binding of saquinavir to wildtype andSimulation and calculation workflow resistant HIV-1 proteases L90M and G48V/L90M Applications used include: NAMD, CHARMM, AMBER… Thermodynamic decomposition •  explains the distortions in enthalpy/entropy Rapid and accurate prediction of binding free energies for balance caused by the L90M and G48V saquinavir-bound HIV-1 proteases. mutations Stoica I, Sadiq SK, Coveney PV. J Am Chem Soc. 2008 Feb 27;130(8):2639-48. Epub •  absolute drug binding energies are in excellent 2008 Jan 29. agreement (1 – 1.5kcal/mol) with experimental  Doable in days values
  • 34. Automation of binding affinity calculation •  Eventual aim is to provide tools that allow simulations to be used in a clinical context •  Require large number of simulations to be constructed and run automatically –  To investigate generalisation –  Automation is critical for clinical use •  Turn-around time scale of around a week is required •  Trade off between accuracy and simulation turn around time Binding Affinity Calculator (BAC) A distributed automated high throughput binding affinity calculator for HIV-1 proteases with relevant drugs “Automated molecular simulation based binding affinity calculator for ligand-bound HIV-1 proteases”. Sadiq SK, Wright D, Watson SJ, Zasada SJ, Stoica I, Coveney PV. J Chem Inf Model. 2008 48(9):1909-19.
  • 35. Grid enabled neurosurgical imaging using simulationThe GENIUS project aims to model large scale patient specific cerebral blood flow inclinically relevant time framesObjectives:•  To study cerebral blood flow using patient-specific image-based models.•  To provide insights into the cerebral blood flow & anomalies.•  To develop tools and policies by means of which users can better exploit the ability to reserve and co-reserve HPC resources.•  To develop interfaces which permit users to easily deploy and monitor simulations across multiple computational resources.•  To visualize and steer the results of distributed simulations in real timeYields patient-specific information which helps plan embolisation of arterio-venousmalformations, aneurysms, etc.
  • 36. Arterio-venous malformations (AVM)Example neuropathology
  • 37. Modelling vascular blood flow - HemeLB Efficient fluid solver for modelling brain bloodflow •  Uses the lattice-Boltzmann method •  Efficient algorithm for sparse geometries •  Machine-topology aware graph growing partitioning technique, to help minimise the issue of cross-site latencies •  Optimized inter- and intra-machine communications •  Full checkpoint capabilities. M. D. Mazzeo and P. V. Coveney, "HemeLB: A high performance parallel lattice-Boltzmann code for large scale fluid flow in complex geometries", Computer Physics Communications, 178, (12), 894-914, (2008).
  • 38. CCS - Haemodynamic simulation and visualisation •  First step is the conversion of MRA or 3DRA data (DICOM format) to a 3D model, vasculature is of high contrast, 200 - 400 µm resolution, 5003 - 10003 voxels •  3DRA - 3-dimensional rotational angiography, vasculature is obtained using digital subtraction imaging with a high-contrast x-ray absorbing fluid. •  Each voxel is a solid (vascular wall), fluid, fluid next to a wall, a fluid inlet or a fluid outlet •  Arterial simulations typically have 1-3 inlets and ~50 outlets, •  We apply oscillating pressures at inlet and oscillating or constant one at the outlets, for example. •  Real-time in-situ visualisation of the data using streamlines, iso-surfacing or volume rendering (stress, velocity or pressure). •  New insights into physiology of human brain Reconstruction and boundary condition Velocity field obtained with set-up; fluid sites, inlet and outlet sites in our ray tracer red, black and green respectively; HemeLB 38
  • 39. Translational ImpactBook computing resources in advance or have a system by which simulations can be run urgently.Move imaging data around quickly over high-bandwidth low-latency dedicated links.Interactive simulations and real-time visualization for immediate feedback. 15-20 minute turnaround 39
  • 40. Yale – UCL CollaborationUCL-Yale Computational Biomedicine collaboration •  Aims: Pool the computational, information management and analysis resources of two of the world’s greatest universities and their associated hospitals: –  Yale University Hospital –  UCL Partners (Great Ormond Street, Moorfields Eye Hospital, Royal Free Hospital and UCL Hospitals Trust). •  break-through science and speed up its translation to better medicine. •  Far-reaching transformative consequences for UK and US science, medicine and industry.
  • 41. Yale – UCL Collaboration •  Overview of Yale- UCL Collaboration •  World Ranking Yale 3, UCL 4. •  Framework Agreement signed 8 Oct 2009 by Malcolm Grant & the President of Yale Richard Levin & leaders of associated hospitals •  Joint resources committed to research & education with aims: –  Enhance scope & quality of research both basic & clinical –  Attract the best students to a joint PhD programme –  Drive translation within the Collaboration –  Lead improvement in patient care
  • 42. Yale – UCL CollaborationGovernance •  Joint Strategy Committee •  Scientific Advisory Board •  Strategic Advisory Board
  • 43. Yale – UCL CollaborationAchievements (first 12 months) Bottom up approach Basic research: 10 joint projects started Clinical research: 2 UCL research clinics started in Yale Hospital, 1 Yale clinic in UCLP. (1st NIH grant $4.7m). Bioinformatics: “The Coveney Plan” Drug Discovery: 3 projects (1 patent) First into Man: First project completed Hospitals: Exchange of managers
  • 44. Yale – UCL CollaborationFuture•  Enthusiasm across Biomedicine & Life Sciences•  Establish translation with Collaborative•  Joint fund raising•  Split PhD starts September 2011•  Joint Hospital Management Institute•  Governance running
  • 45. Yale – UCL CollaborationUK, EU and Global Benefits of the Collaboration •  Major contribution to UK Digital Economy - positioning the UK at the centre of globally integrated healthcare data management •  Strengthening UK expertise in information management –  Expanding market, skills and employment opportunities in information management •  New paradigm will transform healthcare in the UK –  Improving health and well being of UK citizens through better patient data management –  Bespoke treatments and specific drugs for the patient using digital information and clinical expertise –  Will help contain health costs and improve patient wellbeing •  Develop and invest in these future technologies now –  No unified management of & funding mechanisms for data, network and computing –  Current funding is fragmented - networks (JISC), computing (EPSRC, STFC) and data (BBSRC) –  Investment case in point: EBI was placed in the UK owing to superior networking technology which was invested in ahead of time –  UK HPC likely to be embedded within EU via DEISA/PRACE –  UCL is in an excellent position with strong links to EBI, NIH, Sanger, Wellcome, MRC & BBSRC (including planned Digital Organism programme)
  • 46. Yale – UCL Collaboration UCL-Yale integrated technological platform •  Current and future biomedical research that the UCL-Yale Collaboration will tackle all share the common themes of being compute- and data-intensive –  The effective integration of distributed data, computing and networks is central to success •  Data: Standards, transparency, security and ethics •  Computing: Access to high-performance computational resources in UK, EU and USA •  Networks: Establishment of persistent high-bandwidth, high QoS connections between: –  Data producers (e.g. Sanger, EBI, …) –  Researchers (UCL, Yale) –  Clinicians (UCLP, Yale Hospital) –  Compute resources (DEISA, TeraGrid e.g.), initially at 10Gbs, rising to 100Gb/s and to 1Tb/s by 2020
  • 47. JA.NET (UK) 40Gb network Transatlantic 10Gb linkTeraGrid 40Gb backbone DEISA 10Gb network
  • 48. Yale – UCL Collaboration UCL-Yale Collaboration in Biomedicine •  The Collaboration has already established joint projects which are totally integrated •  Cardiovascular science is the first discipline to have been integrated over the last year •  First joint grant was awarded from the NIH in September 2009 (genetics of congenital heart disease) •  Women’s health, cancer, neuroscience and surgery will be integrated during 2010
  • 49. Yale – UCL Collaboration UCL-Yale Collaboration in Biomedicine: Examples •  Cardiovascular clinical genetics - whole genome high throughput analysis at Yale is being applied to UCL patient databases. •  Cardiovascular imaging – Combined power of multiple imaging modalities available within the Collaboration, for patients, volunteers and large and small animals, is world-leading. •  Cardiovascular research clinics –  UCL staff have established clinics for congenital heart disease and familial sudden death at Yale –  Yale staff have established a program for treatment for chronic total occlusion of coronary arteries within UCLP.
  • 50. Digital Me What role will integration play in medicine? •  Up to 1990: anecdote-based medicine (based on personal experience of the physician) •  1990-2010: evidence-based medicine (based on consensus treatment protocols derived from population studies) •  2020: truly personalised medicine, based on models derived from individual patient profiles (genotypes and phenotypes) •  Making decisions from these rich data will require huge amounts of computation, as well as integration of network, computing, and data resources – globally.
  • 51. Digital MeA vision of future healthcare •  Personalised, Predictive, Integrative... •  An integrated anatomical, physiological, biochemical, genotypical model of me •  Integrates my health “chronicle” with models derived from populations with similar phenotypes •  Lets me understand and take control of my own life and health – “$2000 genome” in 3-5 years? •  Used for: –  Patient education •  “what is happening to me?” •  “what are you going to do to me?” –  Management of chronic diseases –  Clinical decision support
  • 52. Digital Me Population Information Personal Information Epidemi- Therapy Genotypes ology outcomes Lifestyle Visualisations Devices Digital Me Genotype Sensors Predictive Models Clinical Many of the individual components of this figure already exist and work, however they need to 52 be integrated
  • 53. Digital MeBetween now and 2020What developments will make Digital Me possible? •  Desk-side supercomputing with 1,000s or even 1,000,000s of cores willprovide the necessary power to run patient specific simulations at low cost •  Distributed, ‘grid’, ‘cloud’ or ‘utility’ computing could make the computational power required for large scale simulations available transparently, and integrated in to the clinical workflow •  Computational models could be routinely constructed and stored with a patient’s EHR (electronic health records) to use when needed –  A radical evolution of healthcare –  Encourages individual’s responsibility for own health management of chronic diseases –  Reduction of need to access central resources

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