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.

Keywords: Bioinformatics; Network analysis; Parkinson’s disease; Pathway analysis; Systems biology.

Fig. 1

Overview of common steps in molecular network analyses of disease-related omics data

Fig. 2

Common generic workflow for a machine learning analysis of omics data, including steps to reduce the dimension of the data through feature selection or feature extraction, higher-level machine learning analysis for classifying omics data samples (a supervised analysis) or clustering the samples (an unsupervised analysis), and evaluation of the obtained machine learning models on external test data