ArticlePloS one2024
Stochastic model of Alzheimer's disease progression using two-state Markov chains.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 citing papers in PubMed, 3 citations in OpenAlex.
- In silico reconstructions underpin aberrant trafficking dynamics in deficient axons of Dst knockout and Dst/Nefl double-knockout mice.Communications biology · 2025Article
- Complex non-Markovian dynamics and the dual role of astrocytes in Alzheimer's disease development and propagation.Quantitative biology (Beijing, China) · 2025Article
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Abstract
In 2016, Hao and Friedman developed a deterministic model of Alzheimer's disease progression using a system of partial differential equations. This model describes the general behavior of the disease, however, it does not incorporate the molecular and cellular stochasticity intrinsic to the underlying disease processes. Here we extend the Hao and Friedman model by modeling each event in disease progression as a stochastic Markov process. This model identifies stochasticity in disease progression, as well as changes to the mean dynamics of key agents. We find that the pace of neuron death increases whereas the production of the two key measures of progression, Tau and Amyloid beta proteins, decelerates when stochasticity is incorporated into the model. These results suggest that the non-constant reactions and time-steps have a significant effect on the overall progression of the disease.
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