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。遗传变异的鉴定揭示了遗传性 PD 的分子途径，而特发性 PD 的疾病机制仍然难以捉摸，部分原因是缺乏强大的工具。PD 的复杂性源于临床症状、病理、导致疾病的环境损伤和疾病合并症的异质性。分子网络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。在这里，我们回顾了分子网络方法的最新进展及其在 PD 中的应用。我们讨论了网络建模如何通过网络上下文预测 PD 遗传风险因素的功能，并帮助发现基于网络的神经退行性疾病治疗方法。