Contributions of low molecule number and chromosomal positioning to stochastic gene expression

Becskei, Attila; Kaufmann, B.; Oudenaarden, Alexander van

doi:10.1038/ng1616

Cited by 296 publications

(303 citation statements)

References 49 publications

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“…It has been shown (8,12,13,15,17,18,20,21) that noise in protein levels comes mainly from discreteness of mRNA and protein numbers (intrinsic noise) as well as from global changes in intracellular environment that affect decay and production rates (global noise). To model intrinsic noise, we must estimate two parameters for each gene: the average number of proteins produced from a single mRNA (burst size) and the conversion factor between absolute protein numbers and fluorescence counts.…”

Section: Noise and Stochastic Measurementsmentioning

confidence: 99%

“…Although this approach is correct in principle, it is often too complicated to have general applicability because the parameters required are usually unmeasured or difficult to acquire. Since the advent of these microscopic approaches, much has been learned about sources and propagation of noise in gene networks (8,(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21), leading to comprehensive models of stochastic behavior. However, these previous models have lacked a sufficiently detailed set of dynamic data on which to test predictions of dynamic population distributions (22)(23)(24).…”

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Predicting stochastic gene expression dynamics in single cells

Mettetal

Muzzey

Pedraza

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

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152

123

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Fluctuations in protein numbers (noise) due to inherent stochastic effects in single cells can have large effects on the dynamic behavior of gene regulatory networks. Although deterministic models can predict the average network behavior, they fail to incorporate the stochasticity characteristic of gene expression, thereby limiting their relevance when single cell behaviors deviate from the population average. Recently, stochastic models have been used to predict distributions of steady-state protein levels within a population but not to predict the dynamic, presteadystate distributions. In the present work, we experimentally examine a system whose dynamics are heavily influenced by stochastic effects. We measure population distributions of protein numbers as a function of time in the Escherichia coli lactose uptake network (lac operon). We then introduce a dynamic stochastic model and show that prediction of dynamic distributions requires only a few noise parameters in addition to the rates that characterize a deterministic model. Whereas the deterministic model cannot fully capture the observed behavior, our stochastic model correctly predicts the experimental dynamics without any fit parameters. Our results provide a proof of principle for the possibility of faithfully predicting dynamic population distributions from deterministic models supplemented by a stochastic component that captures the major noise sources.gene networks ͉ systems biology O ne of the central goals of systems biology is to predict the dynamic behavior of a cell's genetic and metabolic networks. These predictions traditionally stem from models in which discrete molecular events, such as transcription and translation, are represented by continuous and deterministic differential equations. Such equations are valid when behavior of individual cells is very similar to the average behavior of the population. However, in many cases, the inherently stochastic nature of biological systems leads to significant cell-to-cell variability (1-3), and previous studies indicate that individual cells often behave very differently from the population average in response to external stimuli. For example, studies of bacterial persistence indicate that the population survival rate can be fundamentally different from the average cell's survival rate in response to environmental stress (4, 5). The impact of noise-induced population heterogeneity is also relevant when studying cellular memory, where fluctuations in protein numbers can cause the stability of epigenetic memory to degrade by effectively causing cells to forget their original states (6, 7). In these cases, stochastic modeling techniques must be used to describe the large cell-tocell variability. Although deterministic models can describe dynamic network behavior and analytical stochastic models can faithfully predict steady-state population distributions (8), little work has been done to connect dynamic cellular behavior with noise models. It is important that stochastic models of biological systems...

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Section: Noise and Stochastic Measurementsmentioning

confidence: 99%

mentioning

confidence: 99%

Predicting stochastic gene expression dynamics in single cells

Mettetal

Muzzey

Pedraza

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

152

123

View full text Add to dashboard Cite

show abstract

“…Biochemical systems are usually affected by noise (Spudich & Koshland 1976;Arkin et al 1998;Elowitz et al 2002;Becskei et al 2005;Rosenfeld et al 2005;Dunlop et al 2008). For a comprehensive review of noise in genetic systems see Kaern et al (2005), Kaufmann & van Oudenaarden (2007) and Raj & van Oudenaarden (2008).…”

Section: Introductionmentioning

confidence: 99%

Computational limits to binary genes

Zabet

Chu

2009

J. R. Soc. Interface.

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We analyse the trade-off between the speed with which a gene can propagate information, the noise of its output and its metabolic cost. Our main finding is that for any given level of metabolic cost there is an optimal trade-off between noise and processing speed. Any system with a non-vanishing leak expression rate is suboptimal, i.e. it will exhibit higher noise and/or slower speed than leak-free systems with the same metabolic cost. We also show that there is an optimal Hill coefficient h which minimizes noise and metabolic cost at fixed speeds, and an optimal threshold K which minimizes noise.

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“…Since these original studies in bacteria, the double-reporter system has been successfully adapted to other organisms. [33][34][35] Besides the doublereporter system, other methods proposed for estimating the intrinsic and extrinsic components of gene expression noise have relied on total noise measurements for various copy numbers of a reporter gene 36,37 or mutations altering biological parameters relevant for intrinsic noise only (such as the promoter sequence, and the rates of transcription and translation). 32,38,39 Below we summarize the essence of the theory 17 underlying the use of the double-reporter methodology 18 to separate the intrinsic and extrinsic components of gene expression noise.…”

Section: Intrinsic and Extrinsic Noise: An Overviewmentioning

confidence: 99%

A common repressor pool results in indeterminacy of extrinsic noise

Stamatakis

Adams

Balázsi

2011

Chaos: An Interdisciplinary Journal of Nonlinear Science

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For just over a decade, stochastic gene expression has been the focus of many experimental and theoretical studies. It is now widely accepted that noise in gene expression can be decomposed into extrinsic and intrinsic components, which have orthogonal contributions to the total noise. Intrinsic noise stems from the random occurrence of biochemical reactions and is inherent to gene expression. Extrinsic noise originates from fluctuations in the concentrations of regulatory components or random transitions in the cell’s state and is imposed to the gene of interest by the intra- and extra-cellular environment. The basic assumption has been that extrinsic noise acts as a pure input on the gene of interest, which exerts no feedback on the extrinsic noise source. Thus, multiple copies of a gene would be uniformly influenced by an extrinsic noise source. Here, we report that this assumption falls short when multiple genes share a common pool of a regulatory molecule. Due to the competitive utilization of the molecules existing in this pool, genes are no longer uniformly influenced by the extrinsic noise source. Rather, they exert negative regulation on each other and thus extrinsic noise cannot be determined by the currently established method.

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Contributions of low molecule number and chromosomal positioning to stochastic gene expression

Cited by 296 publications

References 49 publications

Predicting stochastic gene expression dynamics in single cells

Predicting stochastic gene expression dynamics in single cells

Computational limits to binary genes

A common repressor pool results in indeterminacy of extrinsic noise

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