The development of "omic" technologies and deep phenotyping may facilitate a systems biology approach to understanding anxiety disorders. Systems biology approaches incorporate data from multiple modalities (e.g., genomic, neuroimaging) with functional analyses (e.g., animal and tissue culture models) and mathematical modeling (e.g., machine learning) to investigate pathological biophysical networks at various scales. Here we review: i) the neurobiology of anxiety disorders; ii) how systems biology approaches have advanced this work; and iii) the clinical implications and future directions of this research. Systems biology approaches have provided an improved functional understanding of candidate biomarkers and have suggested future potential for refining the diagnosis, prognosis, and treatment of anxiety disorders. The systems biology approach for anxiety disorders is, however, in its infancy and in some instances is characterized by insufficient power and replication. The studies reviewed here represent important steps to further untangling the pathophysiology of anxiety disorders.

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焦虑症的系统生物学研究综述

“组学”技术和深度表型的发展可能有助于系统生物学方法来理解焦虑症。系统生物学方法将来自多种模式（例如基因组、神经成像）的数据与功能分析（例如动物和组织培养模型）和数学建模（例如机器学习）相结合，以研究各种规模的病理生物物理网络。我们在这里回顾：i) 焦虑症的神经生物学；ii) 系统生物学方法如何推进这项工作；iii) 本研究的临床意义和未来方向。系统生物学方法提高了对候选生物标志物的功能理解，并暗示了未来改进焦虑症的诊断、预后和治疗的潜力。然而，焦虑症的系统生物学方法还处于起步阶段，在某些情况下，其特点是能力和复制能力不足。此处审查的研究代表了进一步解开焦虑症病理生理学的重要步骤。