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Mathematical models of DNA methylation dynamics: Implications for health and ageing
University of Chester, UK.
University of Chester, UK.
University of Chester, UK.ORCID iD: 0000-0002-7871-6426
University of Chester, UK.ORCID iD: 0000-0002-9743-8636
2019 (English)In: Journal of Theoretical Biology, ISSN 0022-5193, E-ISSN 1095-8541, Vol. 462, p. 184-193Article in journal (Refereed) Published
Abstract [en]

DNA methylation is a key epigenetic process which has been intimately associated with gene regulation. In recent years growing evidence has associated DNA methylation status with a variety of diseases including cancer, Alzheimer’s disease and cardiovascular disease. Moreover, changes to DNA methylation have also recently been implicated in the ageing process. The factors which underpin DNA methylation are complex, and remain to be fully elucidated. Over the years mathematical modelling has helped to shed light on the dynamics of this important molecular system. Although the existing models have contributed significantly to our overall understanding of DNA methylation, they fall short of fully capturing the dynamics of this process. In this paper we develop a linear and nonlinear model which captures more fully the dynamics of the key intracellular events which characterise DNA methylation. In particular the outcomes of our linear model result in gene promoter specific methylation levels which are more biologically plausible than those revealed by previous mathematical models. In addition, our nonlinear model predicts DNA methylation promoter bistability which is commonly observed experimentally. The findings from our models have implications for our current understanding of how changes to the dynamics which underpin DNA methylation affect ageing and health. We also propose how our ideas can be tested in the lab.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 462, p. 184-193
Keywords [en]
Gene promoter bistability, Sensitivity analysis, CpG island, CpG dyads, Hypermethylation, Hypomethylation
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kau:diva-89276DOI: 10.1016/j.jtbi.2018.11.006ISI: 000455972600014OAI: oai:DiVA.org:kau-89276DiVA, id: diva2:1649041
Available from: 2022-04-01 Created: 2022-04-01 Last updated: 2022-07-04Bibliographically approved

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Kavallaris, Nikos I.

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