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Title:
A query-driven framework for constructing Boolean networks from disease maps: application to Parkinson’s disease

Authors:
Adrien Rougny, Ahmed Abdelmonem Hemedan, Venkata Satagopam, and Marek Ostaszewski

Abstract:
Complex diseases generally involve several interconnected pathways. The understanding of how their cross-talks lead to specific phenotypes is key to deciphering the molecular basis of diseases and proposing new drug targets. To this end, disease maps encode molecular mechanisms driving disease-related pathways following graphical and computational systems biology standards. For this reason, diagrams of disease maps may support the construction of dynamical models that are necessary to predict effects of perturbations on the molecular mechanisms they describe. Current methods build dynamical models from single diagrams, and are therefore not well suited to capturing the interconnections between pathways. Moreover, they do not support the selection of parts of interest from the input diagrams or the output models, which remains a manual task that is difficult to reproduce. Here, we present a novel query-driven framework for the construction of dynamical models from disease maps that overcome these issues. Specifically, our framework allows users to (i) integrate diagrams of one or more disease maps into a graph database; (ii) query parts of interest from these integrated diagrams; and (iii) transform the output of the query into a Boolean network whose dynamics can then be analyzed using state-of-the-art tools. We apply our framework to the Parkinson’s Disease Map (PD map), a comprehensive resource capturing key PD-specific mechanisms. We focus on key disease phenotypes, neuronal survival and autophagy, to capture critical pathways. We extract mechanisms linked to mitochondrial function, proteostasis, and immune response, which drive neurodegeneration and cellular stress adaptation. We compare our results to diagram-oriented modeling, demonstrating how inter-pathway regulation enhances biological insight. We discuss the advantages and challenges of query-based modeling for transparent, reproducible research and explore its potential for cross-disease applications.