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arXiv · 1910.10968

A Stochastic Automata Network Description for Spatial DNA-Methylation Models

Alexander Lück·Verena Wolf
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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.

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Alexander Lück, Verena Wolf. 2019-10-24. A Stochastic Automata Network Description for Spatial DNA-Methylation Models. https://arxiv.org/abs/1910.10968

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