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Research Article

EJMCM. 2014; 2(1): 1-4

Systems Patientomics: The virtual in-silico patient

D.V. Dimitrov.

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.
Focal points:
Quite a few experimental biologists, functional and statistical genomics researchers, involved in
developing new measurement technology for biology, and even molecular systems biologists, feel
that that computational methods are not relevant for their own research goals. For the lion’s share of
those cases where these research goals are rationalized by their potential value for predictive,
preventive and participatory medicine this is a misconception.
Systems computational approaches should be a routine part of the clinical arsenal for the diagnosis,
planning and executing of therapeutic interventions. This must include the incorporation of relevant
training in medical school curricula.
Collaborations to be developed across the breadth of the stakeholder groups involved in public health, from
public health providers and patient groups, to researchers, scientists, funders and industry.
Development of an ethical and legal framework (establish common rules and principles for data
acquisition/sharing/integration/reduction to practice, define how to achieve patient consent, to
respond to fears of misuse of provided data, define solutions for data protection/open innovation).

Key words: Computational modelingElectronic health recordOmicsMedical appsBig dataPrecision medicine

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