ArticleNature communications2020
Predictive design of sigma factor-specific promoters.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
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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.
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Who cites it
31 citing papers in PubMed, 56 citations in OpenAlex.
- UnconditionalSynthetic and systems biotechnology · 2026Article
- Construction of a library of gene expression elements in industrialSynthetic and systems biotechnology · 2026Article
- Core elements play distinct roles in promoter birth and transcriptional regulation.Nature communications · 2026Article
- Synthetic Biology Strategies for Activating Cryptic BGCs inACS omega · 2026Review
- A unified computational framework for quantitative design and optimization of transcriptional regulation across bacterial species.Nucleic acids research · 2026Article
- Unraveling the start element and regulatory divergence of core promoters across the domain bacteria.Nucleic acids research · 2025Article
- Engineering wetware and software for the predictive design of compressed genetic circuits for higher-state decision-making.Nature communications · 2025Article
- Deep learning guided programmable design of Escherichia coli core promoters from sequence architecture to strength control.Nucleic acids research · 2025Article
- De novo design of insulated cis-regulatory elements based on deep learning-predicted fitness landscape.Nucleic acids research · 2025Article
- Engineering artificial cross-species promoters with different transcriptional strengths.Synthetic and systems biotechnology · 2025Article
- Synthetic promoter design iniScience · 2024Article
- Generating information-dense promoter sequences with optimal string packing.PLoS computational biology · 2024Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- GPro: generative AI-empowered toolkit for promoter design.Bioinformatics (Oxford, England) · 2024Article
- Towards a rational approach to promoter engineering: understanding the complexity of transcription initiation in prokaryotes.FEMS microbiology reviews · 2024Review
- Fine-Tuning Gene Expression in Bacteria by Synthetic Promoters.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Deep Learning-Assisted Design of Novel Promoters inAdvanced genetics (Hoboken, N.J.) · 2023Article
- Deep flanking sequence engineering for efficient promoter design using DeepSEED.Nature communications · 2023Article
- From qualitative to quantitative: the state of the art and challenges for plant synthetic biology.Quantitative biology (Beijing, China) · 2023Review
- Enabling technology and core theory of synthetic biology.Science China. Life sciences · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To engineer synthetic gene circuits, molecular building blocks are developed which can modulate gene expression without interference, mutually or with the host's cell machinery. As the complexity of gene circuits increases, automated design tools and tailored building blocks to ensure perfect tuning of all components in the network are required. Despite the efforts to develop prediction tools that allow forward engineering of promoter transcription initiation frequency (TIF), such a tool is still lacking. Here, we use promoter libraries of E. coli sigma factor 70 (σ
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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.