Collaborative Modeling and Simulation: The Virtual Physiological Human Vision (Keynote)

The Virtual Physiological Human (VPH) is an international research initiative, which aims to develop a framework of methods and technologies enabling an integrative investigation of the human physiology and pathology. The term integrative indicates the need to overcome the limitations of the reductionist approach, attempting to capture the emergences due to systemic interaction between space-time scales, organ systems, but also academic disciplines. In this brief presentation of the VPH initiative and of its early results we argument that the VPH is first and foremost an attempt to develop an effective environment for collaborative modeling and simulation.

[1]  Yi Han,et al.  Overview of Artificial Neural Networks , 2009, Artificial Neural Networks.

[2]  Alfio Quarteroni,et al.  A vision and strategy for the virtual physiological human in 2010 and beyond , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[3]  Marco Viceconti,et al.  An accurate estimation of bone density improves the accuracy of subject-specific finite element models. , 2008, Journal of biomechanics.

[4]  Hervé Delingette,et al.  Sharing and reusing cardiovascular anatomical models over the Web: a step towards the implementation of the virtual physiological human project , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[5]  O. Johnell,et al.  FRAX™ and the assessment of fracture probability in men and women from the UK , 2008, Osteoporosis International.

[6]  J B Bassingthwaighte Design and strategy for the Cardionome Project. , 1997, Advances in experimental medicine and biology.

[7]  Mihajlo D. Mesarovic,et al.  Systems Theory and Biology , 1968 .

[8]  M Viceconti,et al.  A new software tool for 3D motion analyses of the musculo-skeletal system. , 2006, Clinical biomechanics.

[9]  M. Viceconti,et al.  Strain distribution in the proximal human femoral metaphysis , 2009, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[10]  Fabio Baruffaldi,et al.  Multiscale investigation of the functional properties of the human femur , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[11]  M Viceconti,et al.  Microindentation on cortical human bone: Effects of tissue condition and indentation location on hardness values , 2009, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[12]  Fabio Baruffaldi,et al.  Mechanical testing of cancellous bone from the femoral head: experimental errors due to off-axis measurements. , 2007, Journal of biomechanics.

[13]  Nigel J. B. McFarlane,et al.  Multimodal fusion of biomedical data at different temporal and dimensional scales , 2011, Comput. Methods Programs Biomed..

[14]  Marco Viceconti,et al.  Osteon Classification in Human Fibular Shaft by Circularly Polarized Light , 2009, Cells Tissues Organs.

[15]  Pascal Mamassian,et al.  Bayesian modeling of dynamic motion integration , 2007, Journal of Physiology-Paris.

[16]  Marco Viceconti,et al.  Structural behaviour and strain distribution of the long bones of the human lower limbs. , 2010, Journal of biomechanics.

[17]  Fabio Baruffaldi,et al.  Anisotropy and inhomogeneity of the trabecular structure can describe the mechanical strength of osteoarthritic cancellous bone. , 2010, Journal of biomechanics.

[18]  A. J. Wirth,et al.  Mechanical competence of bone-implant systems can accurately be determined by image-based micro-finite element analyses , 2010 .

[19]  M Viceconti,et al.  Dependence of mechanical compressive strength on local variations in microarchitecture in cancellous bone of proximal human femur. , 2008, Journal of biomechanics.

[20]  Ralph Müller,et al.  In silico biology of bone modelling and remodelling: adaptation , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[21]  Rajarshi Guha Flexible Web Service Infrastructure for the Development and Deployment of Predictive Models , 2008, J. Chem. Inf. Model..

[22]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[23]  Marco Viceconti,et al.  PhysiomeSpace: digital library service for biomedical data , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[24]  Catherine M Lloyd,et al.  CellML: its future, present and past. , 2004, Progress in biophysics and molecular biology.

[25]  Peter J. Hunter,et al.  FieldML: concepts and implementation , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[26]  A. Cappello,et al.  Comparison of logistic and Bayesian classifiers for evaluating the risk of femoral neck fracture in osteoporotic patients , 2001, Medical and Biological Engineering and Computing.

[27]  Fabio Baruffaldi,et al.  Multiscale modelling of the skeleton for the prediction of the risk of fracture. , 2008, Clinical biomechanics.

[28]  Robert Rosen,et al.  Systems Theory and Biology. Proceedings of the 3rd Systems Symposium, Cleveland, Ohio, Oct. 1966. M. D. Mesarović, Ed. Springer-Verlag, New York, 1968. xii + 403 pp., illus. $16 , 1968 .