ArticleBiophysical journal2014
Transient changes in intercellular protein variability identify sources of noise in gene expression.
Article in Biophysical journal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Stochastic Gene Expression Model with State-Dependent Protein Activation Delay.bioRxiv : the preprint server for biology · 2026Article
- Exploring single-cell biosynthetic noise and dynamics for enhanced betaxanthin production in Escherichia coli.Nature communications · 2025Article
- Regulation of Transcriptional Bursting and Spatial Patterning in EarlybioRxiv : the preprint server for biology · 2025Article
- Sequestration of gene products by decoys enhances precision in the timing of intracellular events.Scientific reports · 2024Article
- Out-of-equilibrium gene expression fluctuations in the presence of extrinsic noise.Physical biology · 2023Article
- A Noise-Tolerating Gene Association Network Uncovering an Oncogenic Regulatory Motif in Lymphoma Transcriptomics.Life (Basel, Switzerland) · 2023Article
- Stochastic fluctuations promote ordered pattern formation of cells in the Notch-Delta signaling pathway.PLoS computational biology · 2022Article
- Using average transcription level to understand the regulation of stochastic gene activation.Royal Society open science · 2022Article
- Article
- A DNA repair pathway can regulate transcriptional noise to promote cell fate transitions.Science (New York, N.Y.) · 2021Article
- Noise suppression in stochastic genetic circuits using PID controllers.PLoS computational biology · 2021Article
- Enhancement of gene expression noise from transcription factor binding to genomic decoy sites.Scientific reports · 2020Article
- Stochastic fluctuations in apoptotic threshold of tumour cells can enhance apoptosis and combat fractional killing.Royal Society open science · 2020Article
- Limits of noise for autoregulated gene expression.Journal of mathematical biology · 2018Article
- Gene expression noise is affected differentially by feedback in burst frequency and burst size.Journal of mathematical biology · 2017Article
- First-passage time approach to controlling noise in the timing of intracellular events.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Effects of cell-cycle-dependent expression on random fluctuations in protein levels.Royal Society open science · 2016Article
- Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes.PLoS computational biology · 2016Article
- Inferring single-cell gene expression mechanisms using stochastic simulation.Bioinformatics (Oxford, England) · 2015Article
- Iron acquisition and oxidative stress response in aspergillus fumigatus.BMC systems biology · 2015Article
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Abstract
Protein levels differ considerably between otherwise identical cells, and these differences significantly affect biological function and phenotype. Previous work implicated various noise mechanisms that drive variability in protein copy numbers across an isogenic cell population. For example, transcriptional bursting of mRNAs has been shown to be a major source of noise in the expression of many genes. Additional expression variability, referred to as extrinsic noise, arises from intercellular variations in mRNA transcription and protein translation rates attributed to cell-to-cell differences in cell size, abundance of ribosomes, etc. We propose a method to determine the magnitude of different noise sources in a given gene of interest. The method relies on blocking transcription and measuring changes in protein copy number variability over time. Our results show that this signal has sufficient information to quantify both the extent of extrinsic noise and transcription bursting in gene expression. Moreover, if the mean mRNA count is known, then the relative contributions of transcription versus translation rate fluctuations to extrinsic noise can also be determined. In summary, our study provides an easy-to-implement method for characterizing noisy protein expression that complements existing techniques for studying stochastic dynamics of genetic circuits.
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