Speaker
Description
DNA methylation is a ubiquitous epigenetic mark that plays important yet disparate roles in gene regulation. On the one hand, genome-wide methylation patterns help establish and maintain distinct cell types, and these patterns are stably maintained. On the other hand, patterns in some loci are dynamic, facilitating nimble cellular responses to environmental stimuli. This talk will present our efforts combining discrete stochastic mathematical modeling with data-driven statistical inference approaches to shed light on enzymatic mechanisms that enable the mammalian DNA methylation system to accomplish both stability and responsiveness \cite{BONSU2026}. Integration with sequencing data is enabled by a novel mean-field approximation that can efficiently handle collective behavior among multiple genomic sites. We show how local genetic and non-genetic factors control a sensitive DNA methylation switch. As DNA methylation works in concert with other epigenetic mechanisms, our results could contribute to improved models of gene regulatory networks and epigenetic landscapes.
Bibliography
@article{BONSU2026,
title = {ATunable, Ultrasensitive Threshold in Enzymatic Activity Governs the DNA Methylation Landscape},
journal = {Biophysical Journal},
year = {2026},
issn = {0006-3495},
doi = {https://doi.org/10.1016/j.bpj.2026.03.013},
url = {https://www.sciencedirect.com/science/article/pii/S0006349526001852},
author = {Kwadwo A. Bonsu and Nandor Laszik and Annie Trinh and Timothy L. Downing and Elizabeth L. Read}
}
