@misc{
 title = {A Stochastic Automata Network Description for Spatial DNA-Methylation Models},
 type = {misc},
 year = {2019},
 source = {arXiv},
 keywords = {DNA Methylation,Spatial Stochastic Model,Stochastic Automata Networks},
 id = {f8767440-27b0-31ff-adea-869125c96117},
 created = {2020-11-01T23:59:00.000Z},
 file_attached = {false},
 profile_id = {bbb99b2d-2278-3254-820f-2de6d915ce63},
 last_modified = {2020-11-03T18:06:38.924Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Copyright © 2019, arXiv, All rights reserved. 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.},
 bibtype = {misc},
 author = {Lück, A. and Wolf, V.}
}

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