Abstract
The accurate establishment and maintenance of DNA methylation patterns is vital for mammalian development and disruption to these processes causes human disease. Our understanding of DNA methylation mechanisms has been facilitated by mathematical modelling, particularly stochastic simulations. Megabase-scale variation in DNA methylation patterns is observed in development, cancer and ageing and the mechanisms generating these patterns are little understood. However, the computational cost of stochastic simulations prevents them from modelling such large genomic regions. Here, we test the utility of three different mean-field models to predict summary statistics associated with large-scale DNA methylation patterns. By comparison to stochastic simulations, we show that a cluster mean-field model accurately predicts the statistical properties of steady-state DNA methylation patterns, including the mean and variance of methylation levels calculated across a system of CpG sites, as well as the covariance and correlation of methylation levels between neighbouring sites. We also demonstrate that a cluster mean-field model can be used within an approximate Bayesian computation framework to accurately infer model parameters from data. As mean-field models can be solved numerically in a few seconds, our work demonstrates their utility for understanding the processes underpinning large-scale DNA methylation patterns.
Highlights
DNA methylation is a repressive epigenetic mark [1] which is primarily found on the cytosines of CpG dinucleotides in mammals
By comparison to synthetic data generated from stochastic simulations, we demonstrate that a type of cluster MF model can predict the statistical properties of large-scale methylation patterns
We find that a type of cluster MF model provides excellent predictions and demonstrate that this model can be used within an approximate Bayesian computation (ABC) framework to infer parameters underpinning large-scale methylation systems
Summary
DNA methylation is a repressive epigenetic mark [1] which is primarily found on the cytosines of CpG dinucleotides in mammals. DNA methylation is largely erased from the genome during early mammalian development [2] It is re-established by the de novo DNA methyltransferases DNMT3A and DNMT3B [3] resulting in a landscape where 70–80% of CpGs are methylated in most human cells [4]. The authors modelled collaborative interactions where CpGs within a region of the genome can influence the state of other CpGs, e.g. through enzyme recruitment Models including such collaborativity were subsequently found to explain experimental measurements of methylation maintenance in vitro and in vivo more closely than those that did not include it [15,16]. By comparison to synthetic data generated from stochastic simulations, we demonstrate that a type of cluster MF model can predict the statistical properties of large-scale methylation patterns.
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