# A Hybrid HMM Approach for the Dynamics of DNA Methylation

## TL;DR

This paper introduces a hybrid hidden Markov model to analyze the rapid and complex dynamics of DNA methylation and demethylation in cells, integrating deterministic and stochastic processes.

## Contribution

The paper presents a novel hybrid HMM that models both deterministic and random events in DNA methylation dynamics, tested on mouse embryonic stem cell data.

## Key findings

- Predicted methylation changes over short time scales.
- Estimated efficiencies of methylation and demethylation steps.
- Validated model on time-resolved data from stem cells.

## Abstract

The understanding of mechanisms that control epigenetic changes is an important research area in modern functional biology. Epigenetic modifications such as DNA methylation are in general very stable over many cell divisions. DNA methylation can however be subject to specific and fast changes over a short time scale even in non-dividing (i.e. not-replicating) cells. Such dynamic DNA methylation changes are caused by a combination of active demethylation and de novo methylation processes which have not been investigated in integrated models. Here we present a hybrid (hidden) Markov model to describe the cycle of methylation and demethylation over (short) time scales. Our hybrid model decribes several molecular events either happening at deterministic points (i.e. describing mechanisms that occur only during cell division) and other events occurring at random time points. We test our model on mouse embryonic stem cells using time-resolved data. We predict methylation changes and estimate the efficiencies of the different modification steps related to DNA methylation and demethylation.