ArticleACS synthetic biology2018
A System for Gene Expression Noise Control in Yeast.
Article in ACS synthetic biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Article
- Gene syntaxes modulate gene expression and circuit behavior on plasmids.Journal of biological engineering · 2025Article
- Enhanced metabolic entanglement emerges during the evolution of an interkingdom microbial community.Nature communications · 2024Article
- Synthetic gene circuit evolution: Insights and opportunities at the mid-scale.Cell chemical biology · 2024Review
- Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision.Science advances · 2023Article
- Article
- Heterosis of fitness and phenotypic variance in the evolution of a diploid gene regulatory network.PNAS nexus · 2022Article
- Single-cell measurement quality in bits.PloS one · 2022Article
- Independent control of mean and noise by convolution of gene expression distributions.Nature communications · 2021Article
- Comparison of bias and resolvability in single-cell and single-transcript methods.Communications biology · 2021Article
- Article
- Orthogonal control of mean and variability of endogenous genes in a human cell line.Nature communications · 2021Article
- Functional roles of microbial cell-to-cell heterogeneity and emerging technologies for analysis and control.Current opinion in microbiology · 2020Review
- Orthogonal tuning of gene expression noise using CRISPR-Cas.Nucleic acids research · 2020Article
- Design of a MAPK signalling cascade balances energetic cost versus accuracy of information transmission.Nature communications · 2020Article
- Application of combinatorial optimization strategies in synthetic biology.Nature communications · 2020Review
- From Microbial Communities to Distributed Computing Systems.Frontiers in bioengineering and biotechnology · 2020Review
- Article
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4 authors.
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Abstract
Gene expression noise arises from stochastic variation in the synthesis and degradation of mRNA and protein molecules and creates differences in protein numbers across populations of genetically identical cells. Such variability can lead to imprecision and reduced performance of both native and synthetic networks. In principle, gene expression noise can be controlled through the rates of transcription, translation and degradation, such that different combinations of those rates lead to the same protein concentrations but at different noise levels. Here, we present a "noise tuner" which allows orthogonal control over the transcription and the mRNA degradation rates by two different inducer molecules. Combining experiments with theoretical analysis, we show that in this system the noise is largely determined by the transcription rate, whereas the mean expression is determined by both the transcription rate and mRNA stability and can thus be decoupled from the noise. This noise tuner enables 2-fold changes in gene expression noise over a 5-fold range of mean protein levels. We demonstrated the efficacy of the noise tuner in a complex regulatory network by varying gene expression noise in the mating pathway of Saccharomyces cerevisiae, which allowed us to control the output noise and the mutual information transduced through the pathway. The noise tuner thus represents an effective tool of gene expression noise control, both to interrogate noise sensitivity of natural networks and enhance performance of synthetic circuits.
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