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A Computational Model of the Trans-Synaptic Spread of Pathogenic Tau in Early Alzheimer’s Disease

Institutions: Cumberland Valley High School, Mechanicsburg, PA; Plainview-Old Bethpage JFK High School, Plainview, NY; Boston University RISE Program, Boston, MA; Psychological & Brain Sciences, Boston University, Boston, MA

Published onJan 31, 2021
A Computational Model of the Trans-Synaptic Spread of Pathogenic Tau in Early Alzheimer’s Disease

Alzheimer’s Disease is an extremely prevalent neurodegenerative disorder which causes progressive cognitive decline. This disease is associated with the spread of pathogenic tau protein, which starts in the transentorhinal cortex and progresses to other limbic and cortical areas. Tau is an important structural protein that stabilizes neuronal microtubules. In Alzheimer’s Disease, pathogenic tau proteins detach from microtubules and form neurofibrillary tangles, inhibiting normal neuronal function. Pathogenic tau can spread between neurons via synaptic transmission, so leading therapeutics target its pathological isoforms and inhibit their uptake into adjacent cells. We report the development of a tau propagation computational model based on neuronal firing rates that simulates protein removal as the result of therapeutic intervention and describes its net effects on a neuronal network. Using this model, the tau prevalence and cell death over time were measured to assess a simulated therapeutic’s efficacy. For a quick and efficient alternative method that predicts drug efficacy without running the full simulation, we also trained a machine learning algorithm called a polynomial support vector machine, which classifies a drug’s efficacy given its parameter values. Our data suggest that the most effective therapeutic solutions must affect 95% of infected cells and reduce their tau spreading activity by 85% in order to maintain a 99% neuronal survival rate. Through statistical analysis, we also concluded that targeting a greater tau population is more important than reducing tau by a greater factor to impair tau spread for the most effective therapeutics. Such findings have the potential to guide the development and administration of therapeutics. The simulation itself can also allow future investigators to gain a better understanding of how different factors (i.e. cell type, connectivity) affect tau propagation in AD.

Keywords: Neurodegenerative disease; tauopathy; tau propaga tion; small-world network; therapeutic intervention; tau reduction

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