PuSH - Publication Server of Helmholtz Zentrum München: Computational and analytical approaches for DNA methylation pattern modeling.

Information

Clues to quality of journals

Open Access Policy of Helmholtz Association 2016

CC Licencences

Publication Metrics

Navigation

Home

Deutsch

EVA: Electronic Publishing

New EVA Application

Research

Advanced Search

Browse by ...

... HMGU-Authors/Consortia

... Organizational Structure

... Journal

... Publication Type

... Research Data

... Arbeitsgruppen

... Publication Year

Publication overview

Statistics

Statistics (last 5 years)

OA Publications

Publish

Submit Publication

Import publication from...

...EVA

Report missing publication

Highlights

Support & Contact

Contact persons

Help

Data protection

Helmholtz Open Science

JANE Journal Estimator

SHERPA/RoMEO

DOAJ

Export:

Text

Endnote (RIS) BIB

BibTeX

Chen, Y.* ; Hofmann, V.* ; Riess, A. ; Singh, T. ; Majumder, S.* ; John, S.*

Computational and analytical approaches for DNA methylation pattern modeling.

In: (EPiC Series in Computing). 2024. 51-83 (EPiC Series in Computing ; 104)

Publ. Version/Full Text

DOI

Abstract
Metrics
Extra information

DNA methylation is a modification of the biochemical environment of a nucleotide that can occur at so-called CpG sites in the DNA strand. Just as a genetic mutation, it can benefit or harm the organism, depending on where exactly it happens and to what extent. This work focuses on two questions regarding the pattern evolution of methylation in certain DNA sequences, since the impact of methylation has been observed to depend on these patterns: does the size of (de-)methylated CpG clusters depend on reactions with other CpG sites? And can these reactions alter epigenetic variation, i.e. population-wide methylation patterns? To describe the methylome evolution within one individual (on a single cell basis), but also inter-generational developments, we formulate two mathematical models and corresponding master equations: one considering the influence of a single neighboring CpG site and one regarding both nearest neighbors. As the master equations can only be solved for certain parameter values, we use numerical simulations for further analysis. The simulation is compared to the analytical solution for validation, and then it is used for the investigation of the aforementioned questions. We find that for the chosen parameters, the cluster size increases if neighboring interactions are involved, independently of methylation status. Our results suggest that the epigenetic variation is larger in the case of the models which include neighboring interactions.

Altmetric