Patient-specific Boolean models of Parkinsons’ disease map propose personalised treatments

Currently, large scale, detailed computational models of health and disease mechanisms are being
established, either in the form of well annotated diagrams [1-4] or network repositories [5-6]. These
resources can be used for interactive visualisation of important processes, and together with omics
datasets they improve interpretation of complex knowledge and data [7-8]. Nevertheless,
visualisation platforms such as MINERVA [7] need closer integration with dedicated, computational
methods, allowing precise and reproducible analysis of explored diagrams and datasets.
In my project, I implemented new approaches for computational analysis of data and models
represented as molecular interaction diagrams. One of the challenges was to propose an approach
for calculating and visualisation of genomic burden from variant data represented on Parkinson’s
disease map. By combining properties of network structure of the diagram (e.g. network
connectivity, directionality), the positioning of the variants (which genes or proteins may be affected
by the variants), and their associated clinical properties (e.g. disease diagnosis, rate of progression,
secondary phenotypes), a prioritisation of key affected areas in the diagram was possible.

We used Parkinsons’ disease map as a scaffold to build Boolean models (BMs) to study the dynamic
properties of disease mechanisms. We infer BMs from the Parkinsons’ disease map in an automated
fashion. Then, we tailored the BMs by integrating cohort-specific miRNA datasets. Such integration
can help to understand the disease behaviour in different cohorts and propose specific therapeutic
strategies. As a result, different profiles of the disease cohorts were constructed automatically. We
investigated the relative correspondence between the disease profiles. Further, we studied the
pathophysiology of Parkinsons’ disease with comorbidities, such as diabetes and dementia, to
understand the disease heterogeneity and response to treatments.
Boolean modelling proved to be a powerful and promising formalism to analyse a range of dynamic
properties of Parkinson’s disease mechanisms and identify the conditions that switch on or off the
disease endpoints.
Keywords: Logical modelling; Boolean networks; Parkinson’s disease map, model stratifications

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