High Cooperativity in Negative Feedback can Amplify Noisy Gene Expression

Bokes, Pavol; Lin, Yen Ting; Singh, Abhyudai

doi:10.1007/s11538-018-0438-y

Cited by 25 publications

(19 citation statements)

References 79 publications

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“…In general, negative feedback at the transcriptional level reduces cell-to-cell variation (Becskei and Serrano 2000); however, it has been suggested that negative feedback at the post-transcriptional level reduces cell-to-cell variation more efficiently than a transcriptional regulation feedback (Singh 2011). Additional interactions can change the nature of the feedback system (Ananthasubramaniam and Herzel 2014); for instance, positive feedback interactions may arise within a negative feedback motif (Bokes et al 2018; Nikolic et al 2018). Positive feedback amplifies phenotypic heterogeneity, and can generate subpopulations of cells with different phenotypic states.…”

Section: Toxin Activation Influences Phenotypic Heterogeneitymentioning

confidence: 99%

Autoregulation of bacterial gene expression: lessons from the MazEF toxin–antitoxin system

Nikolić

2018

Curr Genet

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Autoregulation is the direct modulation of gene expression by the product of the corresponding gene. Autoregulation of bacterial gene expression has been mostly studied at the transcriptional level, when a protein acts as the cognate transcriptional repressor. A recent study investigating dynamics of the bacterial toxin-antitoxin MazEF system has shown how autoregulation at both the transcriptional and post-transcriptional levels affects the heterogeneity of Escherichia coli populations. Toxin-antitoxin systems hold a crucial but still elusive part in bacterial response to stress. This perspective highlights how these modules can also serve as a great model system for investigating basic concepts in gene regulation. However, as the genomic background and environmental conditions substantially influence toxin activation, it is important to study (auto)regulation of toxin-antitoxin systems in well-defined setups as well as in conditions that resemble the environmental niche.

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Section: Toxin Activation Influences Phenotypic Heterogeneitymentioning

confidence: 99%

Autoregulation of bacterial gene expression: lessons from the MazEF toxin–antitoxin system

Nikolić

2018

Curr Genet

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“…Production delay is an inevitable part of gene expression (Monk, 2003;Zavala and Marquez-Lago, 2014;Bokes et al, 2018). It can be caused by a number of mechanisms, e.g.…”

Section: Introductionmentioning

confidence: 99%

Mixture distributions in a stochastic gene expression model with delayed feedback

Bokes

Borri

Palumbo

et al. 2019

Preprint

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Noise in gene expression can be substantively affected by the presence of production delay. Here we consider a mathematical model with bursty production of protein, a one-step production delay (the passage of which activates the protein), and feedback in the frequency of bursts. We specifically focus on examining the steady-state behaviour of the model in the slow-activation (i.e. large-delay) regime. Using a quasi-steady-state (QSS) approximation, we derive an autonomous ordinary differential equation for the inactive protein that applies in the slow-activation regime. If the differential equation is monostable, the steady-state distribution of the inactive (active) protein is approximated by a single Gaussian (Poisson) mode located at the globally stable steady state of the differential equation. If the differential equation is bistable (due to cooperative positive feedback), the steady-state distribution of the inactive (active) protein is approximated by a mixture of Gaussian (Poisson) modes located at the stable steady states; the weights of the modes are determined from a WKB approximation to the stationary distribution. The asymptotic results are compared to numerical solutions of the chemical master equation.

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“…The eventual increase in the noise can be attributed e.g. to low copy number effects [24], loss of time averaging [17], or the failure to control large bursts [5]. In this paper we examine how the choice of feedback pathway (burst frequency or decay rate) affects the shape of the noise response to strengthening feedback.…”

Section: Introductionmentioning

confidence: 99%

Controlling noisy expression through auto regulation of burst frequency and protein stability

Bokes

Singh

2019

Preprint

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Protein levels can be controlled by regulating protein synthesis or half life. The aim of this paper is to investigate how introducing feedback in burst frequency or protein decay rate affects the stochastic distribution of protein level. Using a tractable hybrid mathematical framework, we show that the two feedback pathways lead to the same mean and noise predictions in the small-noise regime. Away from the small-noise regime, feedback in decay rate outperforms feedback in burst frequency in terms of noise control. The difference is particularly conspicuous in the strong-feedback regime. We also formulate a fine-grained discrete model which reduces to the hybrid model in the large system-size limit. We show how to approximate the discrete protein copy-number distribution and its Fano factor using hybrid theory. We also demonstrate that the hybrid model reduces to an ordinary differential equation in the limit of small noise. Our study thus contains a comparative evaluation of feedback in burst frequency and decay rate, and provides additional results on model reduction and approximation.

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High Cooperativity in Negative Feedback can Amplify Noisy Gene Expression

Cited by 25 publications

References 79 publications

Autoregulation of bacterial gene expression: lessons from the MazEF toxin–antitoxin system

Autoregulation of bacterial gene expression: lessons from the MazEF toxin–antitoxin system

Mixture distributions in a stochastic gene expression model with delayed feedback

Controlling noisy expression through auto regulation of burst frequency and protein stability

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