From Molecule to Man: the VirtualPhysiological Human, ComputationalBiology & the Future of BiomedicinePeter V. CoveneyCentre for Computational ScienceUniversity College London
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
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.
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
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
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
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!
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
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
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
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
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
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
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
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
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
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)
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
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
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
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.
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.
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).
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
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
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.
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
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
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
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)
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
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
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.
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.
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
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
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