Stochastic model of Alzheimer's Disease progression using two-state Markov chains.
Stochastic model of Alzheimer's Disease progression using two-state Markov chains.
复制标题
使用两种状态马尔可夫链的阿尔茨海默病进展的随机模型。
DOI：
10.1101/2023.06.29.547071
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Parks,Meaghan
中科院分区：
文献类型：
--
作者：
Parks,Meaghan
2016年，Hao和Friedman使用偏微分方程系统开发了阿尔茨海默病进展的确定性模型。该模型描述了疾病的一般行为，然而，它没有包含潜在疾病过程固有的分子和细胞随机性。在这里，我们扩展了郝和弗里德曼模型，通过建模疾病进展中的每个事件作为一个随机马尔可夫过程。该模型确定了疾病进展的随机性，以及关键药物的平均动力学变化。我们发现，神经元死亡的速度增加，而生产的两个关键措施的进展，Tau和淀粉样β蛋白，减速时，随机性纳入模型。这些结果表明，非恒定反应和时间步长对疾病的总体进展具有显著影响。
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.