1. ICT-Bio 2006 Report on Parallel Session 1 Multilevel Modelling and Simulation of Human Physiology and Disease Related Processes ‘The Virtual Physiological Human’ Rod Hose, Medical Physics, University of Sheffield

2. Speakers Marco Viceconti Instituti Ortopedici Rizzoli Peter Hunter University of Auckland Catrin Bludszuweit-Philipp ASD Hans Gregerson University of Aalborg Nicholas Ayache INRIA

3. Overview What is the Virtual Physiological Human ? Consistent messages from our speakers –Illustration of concepts What we need Impact Consistent questions Conclusion

4. © 2006 STEP Consortium Virtual Physiological Human Descriptive –a framework within which observations made in the laboratories, in the hospitals, and in the field all over the world can be collected, catalogued, organised, shared and combined in any possible way. Integrative –a framework that allows experts to collaboratively analyse these observations and develop scientific hypotheses that involve the knowledge of multiple scientific disciplines Predictive –a framework that makes possible to interconnect predictive models defined at different scale, with different methods, and with different levels of detail, into systematic networks that provide concretisation to those systemic hypotheses, and make possible to verify their validity by comparison with other clinical or laboratory observations

5. Consistent Messages The VPH is a massive undertaking but it has enormous potential impact We are not alone…… IUPS Physiome… Multidisciplinary … and multicultural –Encompasses research themes traditionally supported by separate communities: eg. DG Infso and DG Research…… Heterogeneous models Multiscale: Space and Time –‘There is no privileged level of causality’ Denis Noble, keynote address, ICT-Bio 2006

6. Consistent Messages Requires strong and robust infrastructure We are starting to see penetration into clinical and industrial application of elements of the VPH: all of our speakers had illustrations… and so did the keynote speaker in his opening address. We are learning fast: cycle time for new model generation is reducing as the infrastructure develops

7. The Challenge: spatial and temporal scales The diversity of experimental models bacterial modelsstructural biology murine modelsfunctional genomics large animal models physiology humanclinical MRI, CT, etc Requires a hierarchy of inter-related models pathway models ODEs stochastic models PDEs (continuum models) gene reg. networks MD/CG models 1 mperson 1 mmelectrical length scale of cardiac tissue 1 m mcardiac sarcomere spacing 1 nmpore diameter in a membrane protein Space 10 9 10 9 s (70 yrs) human lifetime 10 6 s (10 days) protein turnover 10 3 s (1 hour) digest food 1 sheart beat 1 msion channel HH gating 1 m sBrownian motion Time 10 15

8. © 2006 STEP Consortium Peritoneum Prevertebral ganglion Sympathetic trunc Nonspecific, slow gut afferents Specific, fast peritoneal afferents Silent gut afferents Pain Non-painful sensations Vagal afferents Extrinsic mechanosensitive nerve supply of the gut

9. © 2006 STEP Consortium PET-CT Combining structural and functional information CT PETPET-CT T Taxt, ER Gruner et al.

10. European Needs Specific advanced computational models of major physiological systems at the right scales Specific advanced image analysis and data assimilation methods to build patient-specific models Large databases with biomedical images and genetics Grid-enabled methods to exploit models on distributed databases with high computing power

11. Genome Muscle tissue Nerve tissue Connective tissue Epithelial tissue Circulatory system Respiratory system Musculo-skeletal system Skin (integument) Digestive system Central nervous system Endocrine system Lymphoid system Male reproductive system Female reproductive system Special sense organs Organism (7) OrganSystem(6) Organ (5) Tissue (4) Cell (3) Molecule (2) Atom (1) GenBank EMBL, DDBJ TIGR dbEST dSTS PIR SwissProt Prosite PDB SCOPs OMIN, Medline PubMed SNPbase, … DNA RNA Protein Structure TissueML FieldML AnatML CellML www.cellml.org Poul Nielsen 4. Physiome MLs, tools & databases

12. Conclusion The Physiome project is about: Models that capture patient-specific geometry (link to clinical imaging) Multi-physics models based on biophysical mechanisms (can use power of physical constraints) Multi-scale models that link to proteins, carbohydrates & lipids (can link to disease & drug action) We need: Open source software Markup language standards for encoding models Freely accessible model databases Clinically driven applications Industry partnerships

13. © 2006 STEP Consortium VPH Impact Economic –Industry: reduced product cycle time, reduced risk, Pharmaceutical, medical device, automotive, aerospace, defence, sport/leisure, entertainment,...... –Improved healthcare produces cost savings Health –Supports quantitative evidence-based medicine –Personalised healthcare Societal –Huge data resource to support epidemiological research –Reduces animal experimentation –Reduces ’reinvent the wheel syndrome –Increases public visibility of research investement

14. Personalized Physiological Human Coupling physiological models with biomedical images and signals raises fascinating scientific challenges requires large scale effort (typ. European) Huge potential impact towards more quantitative and predictive medicine Visible HumanPersonalized Physiological Human

15. © Copyright ASD GmbH 2006 Simulations for medical devices mimic reality closely benefit treatment reduce costs and time-to-market understand needs and demands from industry and medical community: Two main tasks for simulation experts: give insights and motivations for use of modern technologies

16. © Copyright ASD GmbH 2006 Multilevel endovascular device analysis Haemodynamics Vascular wall mechanics Device design Device mechanics Biological response Thrombosis Chemical processes Drug delivery Genetics Cardiovascular systems Patient-specific anatomies Multilevel stent / coil analysis

17. Consistent Questions/Observations ! The VPH is a massive undertaking –How will it be organised …. divided by organ ? –Will it be a snapshot in time ? –Will we try to include ‘everything’? … neurological models, coupling to database information on genome and on molecular processes? –How can we best exploit the huge quantity of ‘biological’ knowledge already generated? –Will ‘it’ be open access, open source ? –Will ‘it’ be usable by non-experts ?