Transcriptional regulation: Effects of promoter proximal pausing on speed, synchrony and reliability

TL;DR

This paper models how promoter proximal pausing influences gene expression speed, synchrony, and reliability, showing that elongation regulation enhances these aspects and affects molecular noise control.

Contribution

It introduces a continuous-time Markov chain model to analyze the effects of elongation regulation on transcription dynamics, providing new insights and predictions.

Findings

Elongation regulation increases gene expression speed and synchrony.

Genes regulated at elongation show reduced molecular noise.

Model provides symbolic expressions for transcription dynamics.

Abstract

Recent whole genome polymerase binding assays have shown that a large proportion of unexpressed genes have pre-assembled RNA pol II transcription initiation complex stably bound to their promoters. Some such promoter proximally paused genes are regulated at transcription elongation rather than at initiation; it has been proposed that this difference allows these genes to both express faster and achieve more synchronous expression across populations of cells, thus overcoming molecular "noise" arising from low copy number factors. It has been established experimentally that genes which are regulated at elongation tend to express faster and more synchronously; however, it has not been shown directly whether or not it is the change in the regulated step {\em per se} that causes this increase in speed and synchrony. We investigate this question by proposing and analyzing a continuous-time Markov chain model of polymerase complex assembly regulated at one of two steps: initial polymerase association with DNA, or release from a paused, transcribing state. Our analysis demonstrates that, over a wide range of physical parameters, increased speed and synchrony are functional consequences of elongation control. Further, we make new predictions about the effect of elongation regulation on the consistent control of total transcript number between cells, and identify which elements in the transcription induction pathway are most sensitive to molecular noise and thus may be most evolutionarily constrained. Our methods produce symbolic expressions for quantities of interest with reasonable computational effort and can be used to explore the interplay between interaction topology and molecular noise in a broader class of biochemical networks. We provide general-purpose code implementing these methods.