From pharmacogenomics and systems biology to personalized care: A framework of systems and dynamical medicine

Abstract

With the integration of pharmacogenomics and systems biology, personalized medicine would be possible by switching the gear from the reductionism-based and disease-focused medical system toward a dynamical systems-based and human-centric health care. Comprehensive models are needed to represent the properties of complex adaptive systems (CASs) to elucidate the complexity in health and diseases, including the features of emergence, nonlinearity, self-organization, and adaptation. As all diseases have the dynamical elements, nonlinear time-series analyses are necessary to characterize the system dynamics at various levels to elucidate the physiological and pathological rhythms, oscillations, and feedback loops. Such analyses can help detect patterns across multiple scales in both the spatial (e.g., from molecules to cells, from organisms to psychosocial environments) and the temporal (e.g., from nanoseconds to hours, from years to decades) dimensions. Based on such understanding, systems and dynamical medicine can be developed with the emphasis on the whole systems that change over time to address the nonlinearity and interconnectivity toward a holistic and proactive care. Accurate and robust biomarkers with predictive values can be discovered to reflect the systemic conditions and disease stages. Network and dynamical models may support individualized risk analysis, presymptomatic diagnosis, precise prognosis, and integrative interventions. Systems and dynamical medicine may provide the root for the achievement of predictive, preventive, personalized, and participatory (P4) medicine.