ArticleJournal of mathematical biology2018
Limits of noise for autoregulated gene expression.
Article in Journal of mathematical biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Stochastic Gene Expression Model with State-Dependent Protein Activation Delay.bioRxiv : the preprint server for biology · 2026Article
- Sequestration of gene products by decoys enhances precision in the timing of intracellular events.Scientific reports · 2024Article
- Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells.Genome biology · 2024Article
- Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells.bioRxiv : the preprint server for biology · 2023Article
- Inference on autoregulation in gene expression with variance-to-mean ratio.Journal of mathematical biology · 2023Article
- The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian.bioRxiv : the preprint server for biology · 2023Article
- Stochastic Simulations as a Tool for Assessing Signal Fidelity in Gene Expression in Synthetic Promoter Design.Biology · 2021Article
- Western Diet Decreases the Liver Mitochondrial Oxidative Flux of Succinate: Insight from a Murine NAFLD Model.International journal of molecular sciences · 2021Article
- Enhancement of gene expression noise from transcription factor binding to genomic decoy sites.Scientific reports · 2020Article
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Authors and funding
2 authors.
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Abstract
Gene expression is influenced by extrinsic noise (involving a fluctuating environment of cellular processes) and intrinsic noise (referring to fluctuations within a cell under constant environment). We study the standard model of gene expression including an (in-)active gene, mRNA and protein. Gene expression is regulated in the sense that the protein feeds back and either represses (negative feedback) or enhances (positive feedback) its production at the stage of transcription. While it is well-known that negative (positive) feedback reduces (increases) intrinsic noise, we give a precise result on the resulting fluctuations in protein numbers. The technique we use is an extension of the Langevin approximation and is an application of a central limit theorem under stochastic averaging for Markov jump processes (Kang et al. in Ann Appl Probab 24:721-759, 2014). We find that (under our scaling and in equilibrium), negative feedback leads to a reduction in the Fano factor of at most 2, while the noise under positive feedback is potentially unbounded. The fit with simulations is very good and improves on known approximations.
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