A Stochastic Automata Network Description for Spatial DNA-Methylation Models
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.
Cite
@article{arxiv.1910.10968,
title = {A Stochastic Automata Network Description for Spatial DNA-Methylation Models},
author = {Alexander Lück and Verena Wolf},
journal= {arXiv preprint arXiv:1910.10968},
year = {2019}
}
Comments
10 pages, 3 figures, 1 table