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Integrative multilevel modular approach to modeling the cardiac physiome

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If you have a question about this talk, please contact Mustapha Amrani.

The Cardiac Physiome Project

The Physiome is the quantitative description of the functional behavior of the physiological state of an individual of a species. Personalized medicine will depend on a combination of a generalized representation of a patient augmented by patient-specific information. Predictive medicine depends on knowing the quantitative relationships between variables and the effects of therapeutic interventions. Integrative models of genetic, cellular and physiological systems are necessarily multiscalar and hierarchical. Following the precepts of Claude Bernard, one uses an integrative viewpoint, or model, to reconcile contradictions and maximize descriptive and biophysical, biochemical accuracy. Practicality is facilitated by a modular approach to quantitative multiscale model construction, for it allows the coordination of expertise from different fields, encompassing the specific expertise in specific modules while bringing diverse modules together into an integrated system defining the whole. Individual modules can be simplified to gain computational speed and facilitating their use as mind expanders, but then their range of coverage of the physiological conditions is compromised and the robustness of the model system reduced. To retain predictability in the face of needs to simplify computation, it is important to design model systems to allow automated substitution of one module for another as the real system goes through changes of state in an intensive care unit or through long term responses to therapy, aging, or progressive disease. Such flexibility in integrative modeling is a difficult challenge, but is needed to bring physiomic understanding to practical utility from the current stage of being diagnostically helpful to a stage of providing therapeutic advice or control. Practical models and the JSim simulation analysis platform are available at (Supported by NIH grants RO1 -HL73598 and T!5-HL088516 and NSF 0506477 ).

This talk is part of the Isaac Newton Institute Seminar Series series.

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