Computational Modelling of Glucocerebrosidase Signalling Pathways in Parkinson’s Disease

Biological systems are complex dynamical systems that include a diverse range of biomolecular species, as well as their interactions and interconnections. Subcellular systems are even more complicated due to the complex biomolecular interactions such as cross-talks, feedback mechanisms, bifurcations and so on that define the overall system’s behaviour. When it comes to complex diseases, understanding these cellular mechanisms is difficult because the causes are not due to a single genetic mutation or altered signalling mechanism. Computational modelling of biological systems aids in the reconstruction of these diseases and to understand clinically or experimentally unanswered questions. In this study, important cellular reactions in the GBA (glucocerebrosidase) signalling, one of the critical genetic mutations involved in Parkinson’s disease, were reconstructed with kinetic laws and mathematical equations. The model contributes to a better understanding of normal cellular physiology and the alterations that occur in Parkinson’s disease. Such models can also be used to make disease predictions, model-based experimental designs and target multiple entities to prevent disease progression.