The integration of clinical and molecular sciences with advanced engineering sciences is moving the world towards a new generation of life science where physiological and pathological information from the living human body can be quantitatively described in silico via biocomputing across multiple scales of time and size and through diverse hierarchies of organization – from molecules to cells and organs to individuals. The development of the Virtual Patient will change conventional medicine, which has been based upon experience and expectation, into “predictive medicine” that will have the capacity to develop solutions based upon prior understanding of the dynamic mechanisms and the quantitative logic of human physiology. Drug discovery, medical and welfare apparatus, and clinical trials in silico will improve the development of products with higher efficiency, reliability and safety while reducing cost. They will also impact upon knowledge-intensive industries. Such program aims at playing a key role in this new area, by sharing and generating solutions as well as human resources contributing to establishment of “in silico medicine” as a basis of the predictive medicine within an international framework. In the long-term, computational physiological models will be refined, linked and validated until they are capable of providing essential predictions to clinicians when healthcare decisions need to be made. As the amount of data reinforcing the models grows, predictions will become more and more patient-centred, with models migrating from statistical, average models to physiological and mechanistic models informed by the unique characteristics of the patient. Systems Patientomics will propose new ways of combining this rich patient information space in a highly visual, coherent, meaningful way and of generating new clinical information by blending and fusing existing information, ultimately creating a “Patient Avatar” capable of supporting the medical professional by producing new clinical knowledge emerging from the integration of patient- and population-specific information. Read more. . .