ArticleGenome research2009
Incorporating nucleosomes into thermodynamic models of transcription regulation.
Article in Genome research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed.
- Chromatin enables precise and scalable gene regulation with factors of limited specificity.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Zinc cluster transcription factors frequently activate target genes using a non-canonical half-site binding mode.Nucleic acids research · 2023Article
- Genetic circuit design automation for yeast.Nature microbiology · 2020Article
- Article
- Thermodynamic model of gene regulation for the Or59b olfactory receptor in Drosophila.PLoS computational biology · 2019Article
- Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Modulation of nucleosomal DNA accessibility via charge-altering post-translational modifications in histone core.Epigenetics & chromatin · 2018Article
- Single-molecule DNA unzipping reveals asymmetric modulation of a transcription factor by its binding site sequence and context.Nucleic acids research · 2018Article
- Molecular mechanisms that distinguish TFIID housekeeping from regulatable SAGA promoters.The EMBO journal · 2017Article
- Large-scale mapping of gene regulatory logic reveals context-dependent repression by transcriptional activators.Genome research · 2017Article
- Article
- Mapping nucleosome positions using DNase-seq.Genome research · 2016Article
- Prediction of fine-tuned promoter activity from DNA sequence.F1000Research · 2016Article
- Unraveling determinants of transcription factor binding outside the core binding site.Genome research · 2015Article
- Signal integration by the CYP1A1 promoter--a quantitative study.Nucleic acids research · 2015Article
- Crop epigenetics and the molecular hardware of genotype × environment interactions.Frontiers in plant science · 2015Review
- Estimating binding properties of transcription factors from genome-wide binding profiles.Nucleic acids research · 2015Article
- A framework for modelling gene regulation which accommodates non-equilibrium mechanisms.BMC biology · 2014Article
- Cis-regulatory elements and human evolution.Current opinion in genetics & development · 2014Review
- Hybrid incompatibility arises in a sequence-based bioenergetic model of transcription factor binding.Genetics · 2014Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcriptional control is central to many cellular processes, and, consequently, much effort has been devoted to understanding its underlying mechanisms. The organization of nucleosomes along promoter regions is important for this process, since most transcription factors cannot bind nucleosomal sequences and thus compete with nucleosomes for DNA access. This competition is governed by the relative concentrations of nucleosomes and transcription factors and by their respective sequence binding preferences. However, despite its importance, a mechanistic understanding of the quantitative effects that the competition between nucleosomes and factors has on transcription is still missing. Here we use a thermodynamic framework based on fundamental principles of statistical mechanics to explore theoretically the effect that different nucleosome organizations along promoters have on the activation dynamics of promoters in response to varying concentrations of the regulating factors. We show that even simple landscapes of nucleosome organization reproduce experimental results regarding the effect of nucleosomes as general repressors and as generators of obligate binding cooperativity between factors. Our modeling framework also allows us to characterize the effects that various sequence elements of promoters have on the induction threshold and on the shape of the promoter activation curves. Finally, we show that using only sequence preferences for nucleosomes and transcription factors, our model can also predict expression behavior of real promoter sequences, thereby underscoring the importance of the interplay between nucleosomes and factors in determining expression kinetics.
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