Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses

Abstract

Computational modeling has emerged as a critical tool in investigating the complex molecular processes involved in biological systems and diseases. In this study, we apply Boolean modeling to uncover the molecular mechanisms underlying Parkinson's disease (PD), one of the most prevalent neurodegenerative disorders. Our approach is based on the PD-map, a comprehensive molecular interaction diagram that captures the key mechanisms involved in the initiation and progression of PD. Using Boolean modeling, we aim to gain a deeper understanding of the disease dynamics, identify potential drug targets, and simulate the response to treatments. Our analysis demonstrates the effectiveness of this approach in uncovering the intricacies of PD. Our results confirm existing knowledge about the disease and provide valuable insights into the underlying mechanisms, ultimately suggesting potential targets for therapeutic intervention. Moreover, our approach allows us to parametrize the models based on omics data for further disease stratification. Our study highlight the value of computational modeling in advancing our understanding of complex biological systems and diseases, emphasizing the importance of continued research in this field. Furthermore, our findings have potential implications for the development of novel therapies for PD, which is a pressing public health concern. Overall, this study represents a significant step forward in the application of computational modeling to the investigation of neurodegenerative diseases, and underscores the power of interdisciplinary approaches in tackling challenging biomedical problems.

Cohort-specific Boolean models highlight different regulatory modules during Parkinson’s disease progression

“…1. Differentially expressed genes in two datasets describing the PINK1 Q456X and GBA N307S mutations in T2DM were identified and common overlaps with the genes in the substantia nigra dataset on the PD Map were determined [11]. It was found that 81 genes are commonly altered across datasets (see the Suplementary File) 2.…”

“…PBNs enable us to simulate the impact of molecular dysregulation, thus providing more understanding of the disease pathways. This approach is particularly effective in exploring the complexities of PD pathways and their variations across different disease subtypes [11,12].…”