Living with Noisy Genes: How Cells Function Reliably with Inherent Variability in Gene Expression

Maheshri, Narendra; O’Shea, Erin K.

doi:10.1146/annurev.biophys.36.040306.132705

Cited by 299 publications

(283 citation statements)

References 70 publications

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“…Noise within genetic circuits is typically known to destabilize stable states (48,49). Numerous studies have demonstrated that noise can provide the perturbation for a system to escape the attraction of stable states and trigger switching between distinct phenotypes (48,50,51).…”

Section: Discussionmentioning

confidence: 99%

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A genetic timer through noise-induced stabilization of an unstable state

Turcotte

García-Ojalvo

Süel

2008

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

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Stochastic fluctuations affect the dynamics of biological systems. Typically, such noise causes perturbations that can permit genetic circuits to escape stable states, triggering, for example, phenotypic switching. In contrast, studies have shown that noise can surprisingly also generate new states, which exist solely in the presence of fluctuations. In those instances noise is supplied externally to the dynamical system. Here, we present a mechanism in which noise intrinsic to a simple genetic circuit effectively stabilizes a deterministically unstable state. Furthermore, this noise-induced stabilization represents a unique mechanism for a genetic timer. Specifically, we analyzed the effect of noise intrinsic to a prototypical two-component gene-circuit architecture composed of interacting positive and negative feedback loops. Genetic circuits with this topology are common in biology and typically regulate cell cycles and circadian clocks. These systems can undergo a variety of bifurcations in response to parameter changes. Simulations show that near one such bifurcation, noise induces oscillations around an unstable spiral point and thus effectively stabilizes this unstable fixed point. Because of the periodicity of these oscillations, the lifetime of the noise-dependent stabilization exhibits a polymodal distribution with multiple, well defined, and regularly spaced peaks. Therefore, the noise-induced stabilization presented here constitutes a minimal mechanism for a genetic circuit to function as a timer that could be used in the engineering of synthetic circuits.bifurcation ͉ dynamics ͉ circuit ͉ stochastic ͉ quantized cycle S tochastic fluctuations in gene expression and protein concentrations are a natural by-product of biochemical reactions in cells. Properties of this biochemical noise within genetic circuits, such as their amplitude, distribution, and propagation, have been extensively characterized (1-9). Additionally, theoretical and experimental studies have established that such noise can induce stochastic switching between distinct and stable phenotypic states (5, 10-23). Noise within genetic circuits is therefore thought to contribute to phenotypic heterogeneity in genetically identical cellular populations. It has also recently been shown experimentally that noise can trigger cellular differentiation in fruit flies and bacteria (14,17,18,24). Together, these studies establish that noise can play an active functional role in cellular processes by effectively destabilizing and thus inducing escape from stable phenotypic states.Besides its common role in destabilizing stable states, noise can also have the more counterintuitive effect of generating new stable states that do not exist in the absence of fluctuations (25). In particular, noise-induced bistability has been reported theoretically (26, 27) and experimentally (2). In those situations, one of the two stable solution branches is usually present irrespective of fluctuations, whereas the second one is purely induced by noise (28). The appeara...

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Section: Discussionmentioning

confidence: 99%

“…Numerous studies have demonstrated that noise can provide the perturbation for a system to escape the attraction of stable states and trigger switching between distinct phenotypes (48,50,51). For example, the phage-lysis-lysogeny cellular decision-making switch was one of the first systems for which noise was proposed to be the trigger (52).…”

Section: Discussionmentioning

confidence: 99%

A genetic timer through noise-induced stabilization of an unstable state

Turcotte

García-Ojalvo

Süel

2008

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

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“…Fluctuations in the process of transcription can generate considerable cell-to-cell variability in the expression level of genes across isogenic cell populations (Blake et al 2003;Kaern et al 2005;Maheshri and O'Shea 2007;Raj and van Oudenaarden 2009). This variability can result in phenotypic diversity and thus lead to potentially important effects on many cellular and developmental processes.…”

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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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“…Specifically, we show when entrainment of the population to a reference sinusoidal signal is possible in open and closed loop control and whether the spatially resolved population can track a time-varying reference signal. Since we use an ABM approach we also study the effect of cell-to-cell heterogeneity across the population, as in a physical setting cell dynamics will tend to vary slightly even between members of the same colony [2, 30,31]. Hence, the model serves as an in silico test-bed where cellular populations are simulated in a spatially explicit 3D-environment and the ability to entrain each population using open and closed loop methods is compared.…”

Section: Introductionmentioning

confidence: 99%

Entrainment and Control of Bacterial Populations: An in Silico Study over a Spatially Extended Agent Based Model

Mina¹,

Tsaneva-Atanasova

Bernardo

2016

ACS Synth. Biol.

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms AbstractAs control in cellular populations is becoming more common we extend a spatially explicit agent based model (ABM), developed previously to investigate population emergent behaviour of synchronized oscillating cells in a microfluidic chamber, to include control. Thus, unlike most of the work in models that deal with control of biological systems, we model individual cells with spatial dependencies that may contribute to certain behavioural responses. We use the model to test whether linear control methods can be used to tame the collective behaviour of a bacterial population as recently suggested in the literature. We compare and contrast open and closed loop control in a spatially explicit model of control (specifically proportional control (P-control), proportional-integral control (PI-control) and proportional-integral-derivative control (PID-control) as can be applied in a microfluidic chamber setting) and show when entrainment to a non-natural oscillating period is possible, using an increasing bacterial population size. Results indicate that during open loop control entrainment is only possible in a subset of forcing periods, unlike closed loop control, and a wide variety of dynamical behaviours is obtained outside the regions of entrainment which in a physical setting may be undesirable. However, even with closed loop control, fixed gains cannot harness a population that keeps growing beyond a certain size.

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Living with Noisy Genes: How Cells Function Reliably with Inherent Variability in Gene Expression

Cited by 299 publications

References 70 publications

A genetic timer through noise-induced stabilization of an unstable state

A genetic timer through noise-induced stabilization of an unstable state

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

Entrainment and Control of Bacterial Populations: An in Silico Study over a Spatially Extended Agent Based Model

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