Decoding the grammar of transcriptional regulation from RNA polymerase measurements: models and their applications

Abstract

The genomic revolution has indubitably brought about a paradigm shift in the field of molecular biology, wherein we can sequence, write and re-write genomes. In spite of achieving such feats, we still lack a quantitative understanding of how cells integrate environmental and intra-cellular signals at the promoter and accordingly regulate the production of messenger RNAs. This current state of affairs is being redressed by recent experimental breakthroughs which enable the counting of RNA polymerase molecules (or the corresponding nascent RNAs) engaged in the process of transcribing a gene at the single-cell level. Theorists, in conjunction, have sought to unravel the grammar of transcriptional regulation by harnessing the various statistical properties of these measurements. In this review, we focus on the recent progress in developing falsifiable models of transcription that aim to connect the molecular mechanisms of transcription to single-cell polymerase measurements. We discuss studies where the application of such models to the experimental data have led to novel mechanistic insights into the process of transcriptional regulation. Such interplay between theory and experiments will likely contribute towards the exciting journey of unfurling the governing principles of transcriptional regulation ranging from bacteria to higher organisms.

Probing Mechanisms of Transcription Elongation Through Cell-to-Cell Variability of RNA Polymerase

“…Next, we consider transcription elongation. In vivo (6,(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50) and in vitro (51)(52)(53)(54)(55)(56)(57)(58)(59)(60)(61)(62)(63)(64)(65)(66)(67)(68)(69) experimental studies have reported the asynchronous nature of the motion of RNAP molecules during elongation, which includes ubiquitous pausing and stochastic hopping. To incorporate these features of RNAP elongation in the model, we assume that each individual RNAP molecule on the gene transitions between two states: an active state, in which it stochastically processes (through single basepair (bps) hops) down the gene at a rate k EL , called the hopping rate, or a paused state, in which hopping ceases.…”

“…In light of the data introduced above, the goal of this article is to develop the theoretical predictions that can be used, in conjunction with single-cell transcribing RNAP distributions, to reveal the molecular mechanisms that control transcription of a given gene. Although previous studies have used mRNA and protein distributions to infer these mechanisms, these measurements are conflated with the stochasticity of downstream processes such as translation, postprocessing, degradation, and partitioning (31)(32)(33)(34)(35)(36)(37)(38)(39). In this work, we use direct measurement of nascent mRNA distributions, which are not subject to these additional sources of variability.…”

Tuning up Transcription Factors for Therapy

“…At low expression, noise usually becomes prominent. Nonetheless, a variety of promoters are available with distinct stochastic properties [92].…”

Tuning up Transcription Factors for Therapy

“…At low expression, noise becomes prominent. A variety of promoters are available that can drive expression at a desired level and noise [84].…”