Wang Qian, Zhang Bin, Yue Zhenyu
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, NY 10029, USA; Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, NY 10029, USA; Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, NY 10029, USA; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, NY 10029-6501, USA; Department of Neurology and Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, NY 10029, USA.
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, NY 10029, USA; Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, NY 10029, USA; Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, NY 10029, USA; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, NY 10029-6501, USA.
Trends Neurosci. 2021 Mar;44(3):182-188. doi: 10.1016/j.tins.2020.11.006.
Parkinson's disease (PD) is a complex neurodegenerative disorder. The identification of genetic variants has shed light on the molecular pathways for inherited PD, while the disease mechanism for idiopathic PD remains elusive, partly due to a lack of robust tools. The complexity of PD arises from the heterogeneity of clinical symptoms, pathologies, environmental insults contributing to the disease, and disease comorbidities. Molecular networks have been increasingly used to identify molecular pathways and drug targets in complex human diseases. Here, we review recent advances in molecular network approaches and their application to PD. We discuss how network modeling can predict functions of PD genetic risk factors through network context and assist in the discovery of network-based therapeutics for neurodegenerative diseases.
帕金森病（PD）是一种复杂的神经退行性疾病。遗传变异的鉴定为遗传性帕金森病的分子途径提供了线索，而特发性帕金森病的发病机制仍然难以捉摸，部分原因是缺乏强大的工具。帕金森病的复杂性源于临床症状、病理学、导致该疾病的环境因素以及疾病共病的异质性。分子网络已越来越多地用于识别复杂人类疾病中的分子途径和药物靶点。在此，我们综述了分子网络方法的最新进展及其在帕金森病中的应用。我们讨论了网络建模如何通过网络背景预测帕金森病遗传风险因素的功能，并协助发现基于网络的神经退行性疾病治疗方法。
