ArticlePLoS computational biology2016
Temperature-Dependent Model of Multi-step Transcription Initiation in Escherichia coli Based on Live Single-Cell Measurements.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Bimodality in E. coli gene expression: Sources and robustness to genome-wide stresses.PLoS computational biology · 2025Article
- Delaying production with prokaryotic inducible expression systems.Microbial cell factories · 2024Review
- Transcription Attenuation in Synthetic Promoters in Nonoverlapping Tandem Formation.Biochemistry · 2024Article
- What can we learn when fitting a simple telegraph model to a complex gene expression model?PLoS computational biology · 2024Article
- Allometric scaling of interspecific RNA transcript abundance to extend the use of metatranscriptomics in characterizing complex communities.Molecular ecology resources · 2023Article
- Alteration of DNA supercoiling serves as a trigger of short-term cold shock repressed genes of E. coli.Nucleic acids research · 2022Article
- Genetic circuit design automation with Cello 2.0.Nature protocols · 2022Review
- Statistical inference for a quasi birth-death model of RNA transcription.BMC bioinformatics · 2022Article
- Using average transcription level to understand the regulation of stochastic gene activation.Royal Society open science · 2022Article
- Hardware, Software, and Wetware Codesign Environment for Synthetic Biology.Biodesign research · 2022Review
- Comparative Proteomic Analysis Reveals New Insights Into the Common and Specific Metabolic Regulation of the DiatomFrontiers in plant science · 2020Article
- Transcription closed and open complex formation coordinate expression of genes with a shared promoter region.Journal of the Royal Society, Interface · 2019Article
- Chromosome and plasmid-borne PScientific reports · 2019Article
- A period without PER: understanding 24-hour rhythms without classic transcription and translation feedback loops.F1000Research · 2019Review
- Multiscale effects of heating and cooling on genes and gene networks.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Added value of autoregulation and multi-step kinetics of transcription initiation.Royal Society open science · 2018Article
- Non-transcriptional processes in circadian rhythm generation.Current opinion in physiology · 2018Review
- Precision in a rush: Trade-offs between reproducibility and steepness of the hunchback expression pattern.PLoS computational biology · 2018Article
- Protein plasticity driven by disorder and collapse governs the heterogeneous binding of CytR to DNA.Nucleic acids research · 2018Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcription kinetics is limited by its initiation steps, which differ between promoters and with intra- and extracellular conditions. Regulation of these steps allows tuning both the rate and stochasticity of RNA production. We used time-lapse, single-RNA microscopy measurements in live Escherichia coli to study how the rate-limiting steps in initiation of the Plac/ara-1 promoter change with temperature and induction scheme. For this, we compared detailed stochastic models fit to the empirical data in maximum likelihood sense using statistical methods. Using this analysis, we found that temperature affects the rate limiting steps unequally, as nonlinear changes in the closed complex formation suffice to explain the differences in transcription dynamics between conditions. Meanwhile, a similar analysis of the PtetA promoter revealed that it has a different rate limiting step configuration, with temperature regulating different steps. Finally, we used the derived models to explore a possible cause for why the identified steps are preferred as the main cause for behavior modifications with temperature: we find that transcription dynamics is either insensitive or responds reciprocally to changes in the other steps. Our results suggests that different promoters employ different rate limiting step patterns that control not only their rate and variability, but also their sensitivity to environmental changes.
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