Computational systems biology approaches for Parkinson’s disease

Abstract

Parkinson’s disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions. This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches.

Missing heritability in Parkinson’s disease: the emerging role of non-coding genetic variation

“…Especially in case of complex diseases such as PD, with the very broad range of symptoms and implicated tissues, a systems-level approach can contribute to a more generalized view of the pathological changes. The concepts and necessary tools are reviewed elsewhere (Parikshak et al 2015;Glaab 2018). For example, using gene set enrichment analysis and topological pathway models, disease-associated changes on cellular pathways can be identified.…”

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

“…Parkinson's disease (PD) is the second most common age‐related neurodegenerative disorder, affecting over 1% of the population above the age of 60, increasing to 5% in individuals above 85 years of age 390 . PD is a prime example of a multifaceted disease, including a broad range of motor and non‐motor symptoms and possible contribution of genetic and environmental risk factors 391 . Currently, there is no treatment to prevent the progressive depletion of dopaminergic neurons in the substantia nigra that underlies the movement control and cognitive loss, which is manifested with tremors and memory loss 392,393 .…”

Combining Automated Organoid Workflows with Artificial Intelligence‐Based Analyses: Opportunities to Build a New Generation of Interdisciplinary High‐Throughput Screens for Parkinson's Disease and Beyond

“…For neurodegenerative diseases, such AI-based methods can already assist researchers and clinical professionals in the diagnosis of both, Alzheimer's disease (AD) and PD. [161][162][163] Clinical diagnosis of these conditions can be challenging, and current human-based accuracy is about 80%, even among experts. 164,165 Here, data from very different techniques need to be correlated, and novel AI and ML techniques can aid in this process.…”