ArticlePLoS computational biology2012
Tuning promoter strength through RNA polymerase binding site design in Escherichia coli.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 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
103 citing papers in PubMed, 199 citations in OpenAlex.
- Hfq-dependent artificial regulatory small RNAs for targeted gene expression control in the nitrogen-fixingSynthetic and systems biotechnology · 2026Article
- Applications of synthetic biology in biomedicine.Molecular biomedicine · 2026Review
- Core elements play distinct roles in promoter birth and transcriptional regulation.Nature communications · 2026Article
- Informational blueprints reveal condition-dependent gene regulatory architectures.bioRxiv : the preprint server for biology · 2026Article
- A unified computational framework for quantitative design and optimization of transcriptional regulation across bacterial species.Nucleic acids research · 2026Article
- Design of bacterial DNT sensors based on computational models.Nucleic acids research · 2026Article
- Deep Generative Model-Driven Design of Microbial Synthetic Promoters.Journal of microbiology and biotechnology · 2025Review
- Computational Pipeline for Targeted Integration and Variable Payload Expression in Bacteriophage Engineering.ACS synthetic biology · 2025Article
- Deep learning guided programmable design of Escherichia coli core promoters from sequence architecture to strength control.Nucleic acids research · 2025Article
- Deciphering plasmid replication dynamics: a computational approach to predict ColE1-like plasmid copy number.Journal of the Royal Society, Interface · 2025Article
- The Environment-Dependent Regulatory Landscape of thebioRxiv : the preprint server for biology · 2025Article
- Article
- Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA binding profiles for Escherichia coli.Nature communications · 2025Article
- A systematic survey of TF function in E. coli suggests RNAP stabilization is a prevalent strategy for both repressors and activators.Nucleic acids research · 2025Article
- A high-resolution view of RNA endonuclease cleavage in Bacillus subtilis.Nucleic acids research · 2025Article
- Systematic evaluation of pigment-based whole-cell lead biosensors: challenges in genetic circuit engineering and critical considerations for background noise control.Frontiers in bioengineering and biotechnology · 2025Article
- Deciphering regulatory architectures of bacterial promoters from synthetic expression patterns.PLoS computational biology · 2024Article
- Transcription Attenuation in Synthetic Promoters in Nonoverlapping Tandem Formation.Biochemistry · 2024Article
- Enhancing metabolic efficiency via novel constitutive promoters to produce protocatechuic acid in Escherichia coli.Applied microbiology and biotechnology · 2024Article
- Deciphering regulatory architectures from synthetic single-cell expression patterns.bioRxiv : the preprint server for biology · 2024Article
43 more citing papers are in PubMed but not listed here.
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 at 1 institution in 1 country.
Funding
Abstract
One of the paramount goals of synthetic biology is to have the ability to tune transcriptional networks to targeted levels of expression at will. As a step in that direction, we have constructed a set of 18 unique binding sites for E. coli RNA Polymerase (RNAP) δ⁷⁰ holoenzyme, designed using a model of sequence-dependent binding energy combined with a thermodynamic model of transcription to produce a targeted level of gene expression. This promoter set allows us to determine the correspondence between the absolute numbers of mRNA molecules or protein products and the predicted promoter binding energies measured in k(B)T energy units. These binding sites adhere on average to the predicted level of gene expression over 3 orders of magnitude in constitutive gene expression, to within a factor of 3 in both protein and mRNA copy number. With these promoters in hand, we then place them under the regulatory control of a bacterial repressor and show that again there is a strict correspondence between the measured and predicted levels of expression, demonstrating the transferability of the promoters to an alternate regulatory context. In particular, our thermodynamic model predicts the expression from our promoters under a range of repressor concentrations between several per cell up to over 100 per cell. After correcting the predicted polymerase binding strength using the data from the unregulated promoter, the thermodynamic model accurately predicts the expression for the simple repression strains to within 30%. Demonstration of modular promoter design, where parts of the circuit (such as RNAP/TF binding strength and transcription factor copy number) can be independently chosen from a stock list and combined to give a predictable result, has important implications as an engineering tool for use in synthetic biology.
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.