A Stochastic Automata Network Description for Spatial DNA-Methylation Models

TL;DR

This paper introduces a stochastic automata network model to better understand spatial DNA methylation dynamics, accounting for neighborhood effects, and validates it against Monte Carlo simulations.

Contribution

It generalizes single-CpG methylation models to multiple CpGs using SAN, incorporating neighborhood influences, and verifies the approach through simulation comparisons.

Findings

SAN model successfully captures neighborhood effects

Model aligns well with Monte Carlo simulation results

Provides a scalable framework for methylation pattern analysis

Abstract

DNA methylation is an important biological mechanism to regulate gene expression and control cell development. Mechanistic modeling has become a popular approach to enhance our understanding of the dynamics of methylation pattern formation in living cells. Recent findings suggest that the methylation state of a cytosine base can be influenced by its DNA neighborhood. Therefore, it is necessary to generalize existing mathematical models that consider only one cytosine and its partner on the opposite DNA-strand (CpG), in order to include such neighborhood dependencies. One approach is to describe the system as a stochastic automata network (SAN) with functional transitions. We show that single-CpG models can successfully be generalized to multiple CpGs using the SAN description and verify the results by comparing them to results from extensive Monte-Carlo simulations.