Dataset Information

Stochastic model of Alzheimer's disease progression using two-state Markov chains.

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.

SUBMITTER: Parks ME

PROVIDER: S-EPMC10798450 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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Stochastic model of Alzheimer's disease progression using two-state Markov chains.

Parks Meaghan Elizabeth ME

PloS one 20240119 1

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 ...[more]

PMID: 38241349

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Dataset Information

Stochastic model of Alzheimer's disease progression using two-state Markov chains.

Publications

Stochastic model of Alzheimer's disease progression using two-state Markov chains.

Similar Datasets

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