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Nov 17, 2015

The new era of Computational Biomedicine

In recent years, the increasing convergence between nanotechnology, biomedicine and health informatics have generated massive amounts of data, which are changing the way healthcare research, development, and applications are done.

Clinical data integrate physiological data, enabling detailed descriptions of various healthy and diseased states, progression, and responses to therapies. Furthermore, mobile and home-based devices monitor vital signs and activities in real-time and communicate with personal health record services, personal computers, smartphones, caregivers, and health care professionals.

However, our ability to analyze and interpret multiple sources of data lags far behind today’s data generation and storage capacity. Consequently, mathematical and computational models are increasingly used to help interpret massive biomedical data produced by high-throughput genomics and proteomics projects. Advanced applications of computer models that enable the simulation of biological processes are used to generate hypotheses and plan experiments.

The emerging discipline of computational biomedicine is concerned with the application of computer-based techniques and particularly modelling and simulation to human health. Since almost ten years, this vision is at the core of an European-funded program called “Virtual Physiological Human”. The goal of this initiative is to develop next-generation computer technologies to integrate all information available for each patient, and generated computer models capable of predicting how the health of that patient will evolve under certain conditions.

In particular, this programme is expected, over the next decades, to transform the study and practice of healthcare, moving it towards the priorities known as ‘4P's’: predictive, preventative, personalized and participatory medicine. Future developments of computational biomedicine may provide the possibility of developing not just qualitative but truly quantitative analytical tools, that is, models, on the basis of the data available through the system just described. Information not available today (large cohort studies nowadays include thousands of individuals whereas here we are talking about millions of records) will be available for free. Large cohorts of data will be available for online consultation and download. Integrative and multi-scale models will benefit from the availability of this large amount of data by using parameter estimation in a statistically meaningful manner. At the same time distribution maps of important parameters will be generated and continuously updated. Through a certain mechanism, the user will be given the opportunity to express his interest on this or that model so to set up a consensus model selection process. Moreover, models should be open for consultation and annotation. Flexible and user friendly services have many potential positive outcomes. Some examples include simulation of case studies, tests, and validation of specific assumptions on the nature or related diseases, understanding the world-wide distribution of these parameters and disease patterns, ability to hypothesize intervention strategies in cases such as spreading of an infectious disease, and advanced risk modeling.