Epigenetic rates of change, much as evolutionary mutation rate along a lineage, vary during lifetime. Accurate estimation of the epigenetic state has vast medical and biological implications. To account for these non-linear epigenetic changes with age, we recently developed a formalism inspired by the Pacemaker model of evolution that accounts for varying rates of mutations with time. Here, we present a python implementation of the Epigenetic Pacemaker (EPM), a conditional expectation maximization algorithm that estimates epigenetic landscapes and the state of individuals and may be used to study non-linear epigenetic aging.

中文翻译：

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表观遗传起搏器-在进化框架下模拟表观遗传状态。

表观遗传的变化率，与沿世系的进化突变率一样，在一生中都在变化。对表观遗传状态的准确估计具有广泛的医学和生物学意义。为了说明这些随年龄变化的非线性表观遗传变化，我们最近开发了一种形式主义，该形式主义受到Pacemaker进化模型的启发，该模型说明了随时间变化的突变率。在这里，我们介绍了表观遗传起搏器（EPM）的python实现，这是一种条件期望最大化算法，用于估计表观遗传景观和个体状态，可用于研究非线性表观遗传衰老。