Stochastic Process Algebra Model of Amyloidogenic Processing in Alzheimer’s Disease

Abstract

AbstractStudies based on the amyloid hypothesis have shown that the proteolytic breakdown of amyloidogenic processing is the main pathology of Alzheimer’s disease (AD). In this study, we focused on the effect of receptor SORLA, a 230 kDa type-1 transmembrane glycoprotein, after binding with the amyloid precursor protein (APP) in the Trans-Golgi Network (TGN). We built a Stochastic Process Algebra (SPA) model to capture receptor SORLA’s behavior and understand its influence in the APP processing. Through the SPA modeling approach, the amyloidogenic processing is modeled as concurrent systems in continuous time Markov chains. We fitted the simulations of our model to the data published by Schmidt and colleagues in 2012. We built a model that initially considers the amyloidogenic processing to occur only in the endosomes. Then, we extended it so that the amyloidogenic processing also occurs in the TGN. In our study, we are able to validate the hypothesis proposed in Willnow and Spoelgen’s study that there might be an indirect interaction between SORLA and—secretase. This indirect interaction takes place when APP and the receptor SORLA reverses, the APP that unbinds with SORLA may be cleaved by the—secretase in the TGN. Our SPA model brought new insights about SORLA’s effect on the amyloidogenic processing, particularly to—secretase in the TGN.KeywordsAlzheimer’s diseaseAPP processingAmyloidogenic processingSPiMStochastic process algebraSORLA