ArticleBiophysical journal2010
Accurate prediction of gene expression by integration of DNA sequence statistics with detailed modeling of transcription regulation.
Article in Biophysical journal, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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
15 citing papers in PubMed, 30 citations in OpenAlex.
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- Recent insights into the genotype-phenotype relationship from massively parallel genetic assays.Evolutionary applications · 2019Review
- From biophysics to 'omics and systems biology.European biophysics journal : EBJ · 2019Review
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- Scoring Targets of Transcription in Bacteria Rather than Focusing on Individual Binding Sites.Frontiers in microbiology · 2017Article
- Predicting the impact of promoter variability on regulatory outputs.Scientific reports · 2015Article
- Real sequence effects on the search dynamics of transcription factors on DNA.Scientific reports · 2015Article
- Comparison of the theoretical and real-world evolutionary potential of a genetic circuit.Physical biology · 2014Article
- Systems biophysics of gene expression.Biophysical journal · 2013Review
- System size reduction in stochastic simulations of the facilitated diffusion mechanism.BMC systems biology · 2012Article
- Control of gene expression by modulated self-assembly.Nucleic acids research · 2011Article
- Deterministic and stochastic population-level simulations of an artificial lac operon genetic network.BMC bioinformatics · 2011Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene regulation involves a hierarchy of events that extend from specific protein-DNA interactions to the combinatorial assembly of nucleoprotein complexes. The effects of DNA sequence on these processes have typically been studied based either on its quantitative connection with single-domain binding free energies or on empirical rules that combine different DNA motifs to predict gene expression trends on a genomic scale. The middle-point approach that quantitatively bridges these two extremes, however, remains largely unexplored. Here, we provide an integrated approach to accurately predict gene expression from statistical sequence information in combination with detailed biophysical modeling of transcription regulation by multidomain binding on multiple DNA sites. For the regulation of the prototypical lac operon, this approach predicts within 0.3-fold accuracy transcriptional activity over a 10,000-fold range from DNA sequence statistics for different intracellular conditions.
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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.