  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.
