ArticleBiophysical journal2012
Analytical distribution and tunability of noise in a model of promoter progress.
Article in Biophysical journal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Article
- Antisense transcription can induce expression memory via stable promoter repression.Genome biology · 2025Article
- Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression.Nature communications · 2025Article
- Quantifying cell fate change under different stochastic gene activation frameworks.Quantitative biology (Beijing, China) · 2025Article
- Regulation of biofilm gene expression by DNA replication in Bacillus subtilis.Journal of cellular and molecular medicine · 2024Article
- Transcriptional bursting dynamics in gene expression.Frontiers in genetics · 2024Review
- Stochastic modeling of the mRNA life process: A generalized master equation.Biophysical journal · 2023Article
- Quantifying Information of Dynamical Biochemical Reaction Networks.Entropy (Basel, Switzerland) · 2023Article
- Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model.Royal Society open science · 2023Article
- Regulatory Mechanisms for Transcriptional Bursting Revealed by an Event-Based Model.Research (Washington, D.C.) · 2023Article
- Eukaryotic gene regulation at equilibrium, or non?Current opinion in systems biology · 2022Article
- Queuing Models of Gene Expression: Analytical Distributions and Beyond.Biophysical journal · 2020Article
- Efficient and flexible implementation of Langevin simulation for gene burst production.Scientific reports · 2017Article
- Stochastic models of gene transcription with upstream drives: exact solution and sample path characterization.Journal of the Royal Society, Interface · 2017Article
- A moment-convergence method for stochastic analysis of biochemical reaction networks.The Journal of chemical physics · 2016Article
- Structure of silent transcription intervals and noise characteristics of mammalian genes.Molecular systems biology · 2015Article
- Inferring single-cell gene expression mechanisms using stochastic simulation.Bioinformatics (Oxford, England) · 2015Article
- Reinitiation enhances reliable transcriptional responses in eukaryotes.Journal of the Royal Society, Interface · 2014Article
- Noise and information transmission in promoters with multiple internal States.Biophysical journal · 2014Article
- Promoter-mediated transcriptional dynamics.Biophysical journal · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chromatin template (CT), which accumulates over time until the promoter becomes active, determines upstream dynamics of transcription, but how upstream sequential steps impact downstream dynamics qualitatively and quantitatively is unclear. Here, we analyze a stochastic gene model with a simple yet typical CT that contains one active state and several inactive states of the promoter. We derive the analytical expressions for the noise in mRNA probability distributions governed by master equations. The derived results extend previous work by including the effects of promoter progress on variability and bimodality. Specifically, given a CT for transcription, we analytically demonstrate that inactive phases of the promoter can modulate the noise intensity to the minimum independently of the mean expression of mRNA. If one new inactive state is added to the CT, then the resulting noise will be reduced, implying that the multi-off mechanism plays a role of attenuating the noise. In contrast to the simple on-off mechanism, the multi-off mechanism can also narrow bimodal regions in a certain parameter plane and obscure two peaks, explaining why bimodal distributions are rarely observed in experiments. Our results provide insight into the role of promoter progress in determining the level of cell-to-cell variability in gene expression.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.