Yan Qing
PharmTao, 4601 Lafayette Street, 5672, Santa Clara, CA, 95056-5672, USA,
Methods Mol Biol. 2014;1175:3-17. doi: 10.1007/978-1-4939-0956-8_1.
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.
随着药物基因组学与系统生物学的整合，通过将基于还原论和以疾病为中心的医疗系统转变为基于动态系统和以人类为中心的医疗保健，个性化医疗将成为可能。需要综合模型来表征复杂适应系统（CASs）的特性，以阐明健康和疾病中的复杂性，包括涌现、非线性、自组织和适应等特征。由于所有疾病都具有动态元素，因此有必要进行非线性时间序列分析，以表征各个层面的系统动力学，从而阐明生理和病理节律、振荡及反馈回路。此类分析有助于在空间（例如从分子到细胞，从生物体到社会心理环境）和时间（例如从纳秒到小时，从年到数十年）维度上检测多个尺度的模式。基于这种理解，可以发展系统与动态医学，重点关注随时间变化的整个系统，以应对非线性和相互关联性，实现全面且积极主动的医疗。可以发现具有预测价值的准确且可靠的生物标志物，以反映全身状况和疾病阶段。网络和动态模型可能支持个性化风险分析、症状前诊断、精确预后和综合干预。系统与动态医学可能为实现预测性、预防性、个性化和参与性（P4）医学奠定基础。
