A Stochastic Automata Network Description for Spatial DNA-Methylation Models

  • Alexander LückEmail author
  • Verena Wolf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12040)


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.


DNA methylation Stochastic automata networks Spatial stochastic model 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceSaarland UniversitySaarbrückenGermany

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