Mathematical models of DNA methylation dynamics: Implications for health and ageing

JOURNAL OF THEORETICAL BIOLOGY

卷:462

Article

Zagkos, Loukas¹  Mc Auley, Mark²  Roberts, Jason¹  Kavallaris, Nikos I.¹ 

[1] Univ Chester, Sch Sci & Engn, Dept Math, Thornton Sci Pk Pool Lane, Chester CH2 4NU, Cheshire, England

[2] Univ Chester, Sch Sci & Engn, Dept Chem Engn, Thornton Sci Pk Pool Lane, Chester CH2 4NU, Cheshire, England

关键词: Gene promoter bistability;    Sensitivity analysis;    CpG island;    CpG dyads;    Hypermethylation;    Hypomethylation;   

DOI  :  10.1016/j.jtbi.2018.11.006

来源: Elsevier

【 摘 要 】

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. (C) 2018 Elsevier Ltd. All rights reserved.

【 授权许可】

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