Disentangling the Molecular Pathways of Parkinson's Disease using Multiscale Network Modeling

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.

Automated summary

Parkinson's disease is a complex neurodegenerative disorder with heterogeneous clinical symptoms, pathologies, environmental factors, and comorbidities. Molecular network approaches have been increasingly used to identify molecular pathways and drug targets for PD, aiding in predicting the functions of genetic risk factors and discovering network-based therapies for neurodegenerative diseases.