Linking Stochastic Dynamics to Population Distribution: An Analytical Framework of Gene Expression

Friedman, Nir; Cai, Long; Xie, X. Sunney

doi:10.1103/physrevlett.97.168302

Cited by 692 publications

(1,011 citation statements)

References 24 publications

(32 reference statements)

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“…One benefit may be longer autocorrelation times in the protein levels that bimodality can provide. Unimodal distributions in stress resistance levels can be the result of random fluctuations in gene expression (32). The halflife of these fluctuations is typically 1-3 generations (36,37).…”

Section: Discussionmentioning

confidence: 99%

“…A g-distribution arises from a two-state model of gene expression, where the promoter can be ON or OFF and the protein is expressed in bursts (32). In this context, a corresponds to the number of bursts per cell cycle, while b is the average number of molecules produced per burst.…”

Section: Gamma Distributionsmentioning

confidence: 99%

“…We later relaxed this restriction and obtained similar results, as discussed below. A gamma distribution arises from a twostate model of gene expression, where the promoter can be ON or OFF and the protein is expressed in bursts (32). We used the differential evolution algorithm to select the shape and rate parameters of the distributions (Materials and Methods).…”

Section: Protein Expression Model and Environmental Variablesmentioning

confidence: 99%

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Phenotypic Diversity Using Bimodal and Unimodal Expression of Stress Response Proteins

Garcia‐Bernardo

Dunlop

2016

Biophysical Journal

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Populations of cells need to express proteins to survive the sudden appearance of stressors. However, these mechanisms may be taxing. Populations can introduce diversity, allowing individual cells to stochastically switch between fast-growing and stress-tolerant states. One way to achieve this is to use genetic networks coupled with noise to generate bimodal distributions with two distinct subpopulations, each adapted to a stress condition. Another survival strategy is to rely on random fluctuations in gene expression to produce continuous, unimodal distributions of the stress response protein. To quantify the environmental conditions where bimodal versus unimodal expression is beneficial, we used a differential evolution algorithm to evolve optimal distributions of stress response proteins given environments with sudden fluctuations between low and high stress. We found that bimodality evolved for a large range of environmental conditions. However, we asked whether these findings were an artifact of considering two well-defined stress environments (low and high stress). As noise in the environment increases, or when there is an intermediate environment (medium stress), the benefits of bimodality decrease. Our results indicate that under realistic conditions, a continuum of resistance phenotypes generated through a unimodal distribution is sufficient to ensure survival without a high cost to the population.

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Section: Discussionmentioning

confidence: 99%

Section: Gamma Distributionsmentioning

confidence: 99%

Section: Protein Expression Model and Environmental Variablesmentioning

confidence: 99%

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Phenotypic Diversity Using Bimodal and Unimodal Expression of Stress Response Proteins

Garcia‐Bernardo

Dunlop

2016

Biophysical Journal

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“…Taken together, our results suggest that we can achieve similar quantitative effects on the mean expression of a cell population by either adding poly(dA:dT) tracts or strengthening the affinity of a transcription factor binding site, but the promoter with the poly(dA:dT) will have a higher burst frequency, lower burst size, and, as predicted by analytical models (Paulsson and Ehrenberg 2000;Kepler and Elston 2001;Raser and O'Shea 2004;Friedman et al 2006), lower noise, as compared with the promoter with the stronger site. Since the effects of these manipulations are predictable, these two distinct strategies may allow for partially decoupling mean expression level and noise.…”

Section: Achieving Similar Mean Expression Levels With Predictably DImentioning

confidence: 68%

“…Thus, combined with the above studies, an intriguing hypothesis is that the two distinct types of sequence changes in either TF sites or in nucleosome-disfavoring sequences provide a genetic mechanism that may allow partial decoupling of mean expression level and transcriptional noise. Specifically, since mean expression is the product of burst frequency and burst size, and noise (under some assumptions) is the inverse of burst frequency (Paulsson and Ehrenberg 2000;Friedman et al 2006), then increasing burst frequency is expected to result in lower noise compared with a similar increase in mean expression that is due to an increase in burst size.…”

mentioning

confidence: 99%

Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise

et al. 2013

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Individual cells from a genetically identical population exhibit substantial variation in gene expression. A significant part of this variation is due to noise in the process of transcription that is intrinsic to each gene, and is determined by factors such as the rate with which the promoter transitions between transcriptionally active and inactive states, and the number of transcripts produced during the active state. However, we have a limited understanding of how the DNA sequence affects such promoter dynamics. Here, we used single-cell time-lapse microscopy to compare the effect on transcriptional dynamics of two distinct types of sequence changes in the promoter that can each increase the mean expression of a cell population by similar amounts but through different mechanisms. We show that increasing expression by strengthening a transcription factor binding site results in slower promoter dynamics and higher noise as compared with increasing expression by adding nucleosome-disfavoring sequences. Our results suggest that when achieving the same mean expression, the strategy of using stronger binding sites results in a larger number of transcripts produced from the active state, whereas the strategy of adding nucleosome-disfavoring sequences results in a higher frequency of promoter transitions between active and inactive states. In the latter strategy, this increased sampling of the active state likely reduces the expression variability of the cell population. Our study thus demonstrates the effect of cis-regulatory elements on expression variability and points to concrete types of sequence changes that may allow partial decoupling of expression level and noise.

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Entangled signal pathways can both control expression stability and induce stochastic focusing

Wang

Liu

2018

FEBS Letters

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Gene transcription is often controlled by multiple interacting signal pathways, but how these pathways impact gene expression is not fully understood. Here, we refine a mechanic model based on experiments in murine embryonic stem cells and analyze the influence of pathway-pathway cross-talk strength (CTS) on mRNA expression stability. We find that the CTS can tune this stability, depending on the manner of regulation. Furthermore, there is an optimal CTS such that the expression pattern is most stable but free energy consumption is at its highest; the CTS can induce stochastic focusing of the mRNA level but this is at the cost of energy. In both cases, there is a cross-talk-mediated trade-off, implying that entangled signal pathways can control both expression stability and energy dissipation.

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Linking Stochastic Dynamics to Population Distribution: An Analytical Framework of Gene Expression

Cited by 692 publications

References 24 publications

Phenotypic Diversity Using Bimodal and Unimodal Expression of Stress Response Proteins

Phenotypic Diversity Using Bimodal and Unimodal Expression of Stress Response Proteins

Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise

Entangled signal pathways can both control expression stability and induce stochastic focusing

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