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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.
We introduce a minimal model description for the dynamics of transcriptional regulatory networks. It is studied within a mean-field approximation, i.e., by deterministic odes representing the reaction kinetics, and by stochastic simulations employing
Gene expression is controlled primarily by interactions between transcription factor proteins (TFs) and the regulatory DNA sequence, a process that can be captured well by thermodynamic models of regulation. These models, however, neglect regulatory
The set of regulatory interactions between genes, mediated by transcription factors, forms a species transcriptional regulatory network (TRN). By comparing this network with measured gene expression data one can identify functional properties of the
Mutation is a critical mechanism by which evolution explores the functional landscape of proteins. Despite our ability to experimentally inflict mutations at will, it remains difficult to link sequence-level perturbations to systems-level responses.
There is increasing evidence that protein binding to specific sites along DNA can activate the reading out of genetic information without coming into direct physical contact with the gene. There also is evidence that these distant but interacting sit