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PLoS Computational Biology
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PLoS Computational Biology
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PLoS Computational Biology
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Research Collection
Article . 2009
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ETH Zürich Research Collection
Article . 2009
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Precise Regulation of Gene Expression Dynamics Favors Complex Promoter Architectures

Authors: Müller, Dirk; Stelling, Jörg;

Precise Regulation of Gene Expression Dynamics Favors Complex Promoter Architectures

Abstract

Promoters process signals through recruitment of transcription factors and RNA polymerase, and dynamic changes in promoter activity constitute a major noise source in gene expression. However, it is barely understood how complex promoter architectures determine key features of promoter dynamics. Here, we employ prototypical promoters of yeast ribosomal protein genes as well as simplified versions thereof to analyze the relations among promoter design, complexity, and function. These promoters combine the action of a general regulatory factor with that of specific transcription factors, a common motif of many eukaryotic promoters. By comprehensively analyzing stationary and dynamic promoter properties, this model-based approach enables us to pinpoint the structural characteristics underlying the observed behavior. Functional tradeoffs impose constraints on the promoter architecture of ribosomal protein genes. We find that a stable scaffold in the natural design results in low transcriptional noise and strong co-regulation of target genes in the presence of gene silencing. This configuration also exhibits superior shut-off properties, and it can serve as a tunable switch in living cells. Model validation with independent experimental data suggests that the models are sufficiently realistic. When combined, our results offer a mechanistic explanation for why specific factors are associated with low protein noise in vivo. Many of these findings hold for a broad range of model parameters and likely apply to other eukaryotic promoters of similar structure.

PLoS Computational Biology, 5 (1)

ISSN:1553-734X

ISSN:1553-7358

Related Organizations
Keywords

Ribosomal Proteins, Saccharomyces cerevisiae Proteins, Fourier Analysis, Models, Genetic, QH301-705.5, Reproducibility of Results, Saccharomyces cerevisiae, Nonlinear Dynamics, Data Interpretation, Statistical, Gene Expression Regulation, Fungal, Linear Models, Nucleic Acid Conformation, Biology (General), DNA, Fungal, Promoter Regions, Genetic, Research Article, Transcription Factors

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Average
Average
Top 10%
Green
gold