Using Gene Expression Noise to Understand Gene Regulation

By Brian Munsky, G. Neuert, Alexander van Oudenaarden. Science, 2012

747 citations recorded by the source corpus.

Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical cells in identical environments display variable phenotypes. Stochastic gene expression, or gene expression “noise,” has been suggested as a major source of this variability, and its physiological consequences have been topics of intense research for the last decade. Several recent studies have measured variability in protein and messenger RNA levels, and they have discovered strong connections between noise and gene regulation mechanisms. When integrated with discrete stochastic models, measurements of cell-to-cell variability provide a sensitive “fingerprint” with which to explore fundamental questions of gene regulation. In this review, we highlight several studies that used gene expression variability to develop a quantitative understanding of the mechanisms and dynamics of gene regulation.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b59880ee42c474a6d2d89ea195bf11ac15ff7e87

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Citations in the corpus

Spatially resolved, highly multiplexed RNA profiling in single cells (2015, positive): “Such covariation analysis can constrain regulatory networks, suggest new regulatory pathways, and predict function for unannotated genes based on associationswith covarying genes (11, 28).”
Quantifying the dynamics of IRES and cap translation with single-molecule resolution in live cells (2020, background): “Moreover, these transitions appear to be modulated to control translational output similar to how promoter activation and deactivation con-trol transcriptional bursts 43 .”
Single-Cell and Single-Molecule Analysis of Gene Expression Regulation (2016, background): “Noise can be of extrinsic or intrinsic origin (106).”
Computational and analytical challenges in single-cell transcriptomics (2015, background): “However, as these parameters are measured in units of time and we observe only the stationary distribution, the rate of decay is typically set to one, which makes the kinetic parameters independent of time 19,84 .”
Design and Analysis of Single-Cell Sequencing Experiments. (2015, background): “Moreover, even cells of the same type can show substantial gene expression variability leading to phenotypic variation (Eldar and Elowitz, 2010; Munsky et al., 2012; Snijder and Pelkmans, 2011).”