A Mathematical Model for Enhancer Activation Kinetics During Cell Differentiation

Abstract

Cell differentiation and development are for a great part steered by cell type specific enhancers. Transcription factor (TF) binding to an enhancer together with DNA looping result in transcription initiation. In addition to binding motifs for TFs, enhancer regions typically contain specific histone modifications. This information has been used to detect enhancer regions and classify them into different subgroups. However, it is poorly understood how TF binding and histone modifications are causally connected and what kind of molecular dynamics steer the activation process. Contrary to previous studies, we do not treat the activation events as static epigenetic marks but consider the enhancer activation as a dynamic process. We develop a mathematical model to describe the dynamic mechanisms between TF binding and histone modifications known to characterize an active enhancer. We estimate model parameters from time-course data and infer the causal relationships between TF binding and different histone modifications. We benchmark the performance of this framework using simulated data and survey the ability of our method to identify the correct causal mechanisms for a variety of system dynamics, noise levels and the number of measurement time points.

Shadow enhancers mediate trade-offs between transcriptional noise and fidelity

“…Grah et al [22] studied a model related to the Monod-Wyman-Changeux hemoglobin system [23] in the context of enhancer regulation and considered several performance metrics. The work by Nousiainen et al presented a computational framework for identifying model families that can predict enhancer activation dynamics in a mechanistic fashion [24]. In this work, we focused our approach on a set of minimalist reaction network models in which each of the reactions is stochastic, and the parameters were derived from previous transcriptional data of Kruppel enhancers [20].…”