Cluster mean-field theory accurately predicts statistical properties of large-scale DNA methylation patterns

Lyndsay Kerr, Duncan Sproul, Ramon Grima*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.

Original languageEnglish
Article number20210707
JournalJournal of the Royal Society. Interface
Issue number186
Early online date26 Jan 2022
Publication statusPublished - Jan 2022

Keywords / Materials (for Non-textual outputs)

  • DNA methylation
  • epigenetics
  • master equations
  • stochastic modelling


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