Elizabeth L. Read

The paper investigates how enzymatic activity thresholds control DNA methylation patterns, finding an ultrasensitive switch mechanism influenced by enzyme interactions.

The paper compares three computational methods for modeling collaborative DNA methylation dynamics and shows how methylation patterns depend on CpG cluster architecture and enzymatic parameters.

This paper develops a computational pipeline to infer kinetic parameters from single-cell RNA sequencing data and assesses parameter identifiability using the stochastic telegraph model.

This paper introduces MPATH, a method to temporally order epigenetic modifications using long-read sequencing, revealing sub-cell-cycle dynamics of methylation states and nucleosome occupancy.

The paper uses statistical and stochastic modeling to analyze Repli-BS data from human embryonic stem cells, revealing kinetic heterogeneity in post-replication DNA methylation.

The paper develops a method to analyze single-cell gene-pair coexpression patterns using stochastic kinetic modeling, revealing gene-pair interactions during embryonic development.