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Overview of Talk

Integrative Biology Using Grid Technology to tackle two Grand Challenges – the in-silico modelling of heart failure and of cancer Sharon Lloyd Computing Laboratory University of Oxford. Overview of Talk. Project overview and rationale What is e-Science? Project background

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Overview of Talk

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  1. Integrative BiologyUsing Grid Technology to tackle two Grand Challenges – the in-silico modelling of heart failure and of cancerSharon LloydComputing LaboratoryUniversity of Oxford

  2. Overview of Talk • Project overview and rationale • What is e-Science? • Project background • The scientific challenge • The e-Scientific challenge • Heart Modelling • Cancer modelling • Technology challenges • Project status

  3. What is e-Science? • The UK e-Science Programme is predicated on the belief that the scientific research that will drive future innovation will increasingly be • Large scale • International • Interdisciplinary • Collaborative • Dependent upon advanced technologies • The UK e-Science Programme has invested ~£250M over two funding rounds to develop the IT infrastructure to support this research effort • The IB project is one of two second-round EPSRC “Pilot” projects – there were five such large projects in the first round

  4. What is e-Science? The infrastructure utilises a service-based architecture and provides component-based inter-operable toolkits to enable user-friendly access to • High Performance Computing (HPC) facilities • Large scale and high-throughput experimental facilities • Data storage facilities • Virtual research and learning environments providing • Data aggregation, synthesis, and analysis tools • Modelling tools • Workflow engines

  5. The Integrative Biology Objectives • To build upon the first round of e-Science projects and the expanding global Grid infrastructure to build an international “Virtual Research Environment” which places the applications scientist “within” the Grid allowing fully integrated and collaborative use of: • HPC resources (capacity and capability) • Computational steering, performance control and visualisation • Storage and data-mining of very large data sets • Easy incorporation of experimental data • User- and science-friendly access => Predictive in-silico models to guide experiment and, ultimately, design of novel drugs and treatment regimes

  6. New Orleans UCSD Graz, Austria £2.5 million 3.5 years Commenced Feb 2004

  7. The Integrative Biology Challenge • To build an Integrative Biology Grid to support applications scientistsaddressing the key post-genomic aim of determining biological function • To use this Grid to begin to tackle the two major research areas: the in-silico modelling of heart failure and of cancer.

  8. Scientific challenge: Two major research areas • Modelling of the whole-heart • Modelling of the formation and development of malignant tumours • Heart disease and cancer together cause 61% of all deaths in the UK

  9. Multiscale modelling of the heart MRI image of a beating heart Fibre orientation ensures correct spread of excitation Contraction of individual cells Current flow through ion channels

  10. Heart modelling • Typically solving coupled systems of PDEs (tissue level) and non-linear ODE’s (cellular level) for the electrical potential • Complex three-dimensional geometries • Up to 60 variables

  11. Multiscale modelling of cancer

  12. Cancer modelling • Focusing on avascular tumours in colorectal cancer • Current models range from discrete population-based models and cellular automata, to non-linear ode systems and complex systems of non-linear PDEs • Key goal is the coupling (where necessary) of these models into an integrated system which can be used to gain insight into experimental findings, to help design new experiments, and ultimately to test novel approaches to cancer detection, and new drugs and treatment regimes • Need to provide middleware to support these goals (expecting to build on GEODISE and Matlab)

  13. Summary of the scientific challenge Modelling and coupling phenomena which occur on many different length and time scales • 1m person • 1mm tissue morphology • 1mm cell function • 1nm pore diameter of a membrane protein • Range = 109 • 109 s (years) human lifetime • 107 s (months) cancer development • 106 s (days) protein turnover • 103 s (hours) digest food • 1 s heart beat • 1 ms ion channel gating • 1 ms Brownian motion • Range = 1015 • Estimated max compute time to investigate arrhythmia ~107s – • approximately 1 day on HPCx

  14. Technology Challenges • Ability to carry out reliably and resiliently large scale distributed coupled HPC simulation • Ability to co-schedule Grid resources based on a agreed standards • Use of Grid Services for data virtualisation • Secure data management and access-control in a Grid environment • Grid services for computational steering (conforming to an agreed standard) • Grid Services to support distributed collaborative working and visualisation • User-friendly interface to using Grid resources which understands and supports effectively the science context of the project • Support for workflow management, particularly re-use of workflows • Tools to support mathematical modelling in a Grid environment

  15. Feasibility? • Stable, persistent, usable? • Our user communities are NOT e-Scientists – they are scientists! • Question: Will the middleware remain stable enough over the next 3-4 years to allow us to persuade them of the benefits of the Grid? • The project is intending to produce a long term (~10 year) production environment based on the Grid to support what we expect to become a major scientific growth area (see the IUPS Physiome Project at http://www.bioeng.auckland.ac.nz )

  16. Current Status • Official project start date 1/2/04, recruitment of staff now complete • Initial project structure defined and agreed, initial iteration of requirements gathering and security policy exercises undertaken • Survey of capabilities of existing middleware continuing and design and workplan for initial prototype agreed • Heart-modelling workshop held in Oxford in May and collaboration now extended to groups in the US – particularly New Orleans and UCSD • Cancer modelling group established

  17. Summary • Science-driven project that aims to build on existing Grid middleware to (begin to) prove the benefits of the e-Science approach for complex systems biology – i.e. to do some novel science • Excellent initial buy-in from the user community • Challenge is to develop sufficiently robust and usable tools to maintain that interest

  18. New Orleans UCSD Graz, Austria Thank You!

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