Biomedical research and clinical practice are struggling to cope with the growing complexity that the progress of healthcare involves. The most challenging diseases, those with the largest 40 socioeconomic impact (cardiovascular conditions, musculoskeletal conditions, cancer, metabolic, immunity and neurodegenerative conditions) are all characterised by a complex genotype/phenotype interaction, and in general by a ÒsystemicÓ nature that the traditional reductionist approach struggle to cope with. In May 2005 a small group of researchers with different backgrounds met in Barcelona to discuss how the vision of computational 45 physiology promoted by the Physiome Project could be translated into the clinical practice.In that meeting the term Virtual Physiological Human was formally proposed. We know a lot about these diseases, but our knowledge is fragmentary as it is associated with molecular and cellular processes on the one hand, and with tissue and organ phenotype changes (related to clinical symptoms of disease conditions) on the other. The problem could be solved if we 50 could capture all these fragments of knowledge into predictive models and then compose them into hypermodels that help us to tame the complexity that such systemic behaviour involves. In 2005 this was simply not possible -the necessary methods and technologies were not available. Now, ten years later, it seems the right time to reflect on the original vision, the results achieved so far, and what remains to be done. 55
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