Buffering Gene Expression Noise by MicroRNA Based Feedforward Regulation

Bokes, Pavol; Hojcka, Michal; Singh, Abhyudai

doi:10.1007/978-3-319-99429-1_8

Cited by 11 publications

(11 citation statements)

References 43 publications

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“…Here we extend the hybrid stochastic model (6) with feedback in burst frequency and decay rate (Fig 1, right). In the feedback model, the probability of a burst to occur in a time interval of length dt is equal to ah(x)dt + o(dt), where x gives the current protein concentration and h(x) is a response function as specified below.…”

Section: Feedback Modelmentioning

confidence: 99%

“…The definition (21) of the relative noise superficially resembles the Fano factor [6]. However, the two should not be confused.…”

Section: Relative Noisementioning

confidence: 99%

“…The Fano factor, which is defined as the variance to mean ratio, is a widely used measure of variability in discrete probability distributions and discrete stochastic models for gene expression [6]. The protein Fano factor satisfies…”

Section: Discrete Approachmentioning

confidence: 99%

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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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Section: Feedback Modelmentioning

confidence: 99%

“…The definition (21) of the relative noise superficially resembles the Fano factor [6]. However, the two should not be confused.…”

Section: Relative Noisementioning

confidence: 99%

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

Bokes

Singh

2019

Preprint

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“…This example shows that coupling could indeed reduce noise. In fact, similar circuits have been analyzed in [14], [10] that report specific RNA circuit designs with coupling could suppress gene expression noise.…”

Section: A Coupling Could Reduce Noisementioning

confidence: 99%

“…Coupling as a noise suppression mechanism is also rather intuitive from an informationtheoretic point of view. Indeed, earlier studies [10], [14], [15], [16] have shown a few specific examples of coupled reactions could have less noise than the decoupled versions. However, it is not known what are the general conditions for coupling to suppress noise, the fundamental limitations on noise suppression once we allow coupling (in the spirit of [2]), or how to use coupling when designing biochemical network architectures.…”

Section: Introductionmentioning

confidence: 99%

Coupled Reaction Networks for Noise Suppression

Xiao

Fang

Doyle

2018

Preprint

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Noise is intrinsic to many important regulatory processes in living cells, and often forms obstacles to be overcome for reliable biological functions. However, due to stochastic birth and death events of all components in biomolecular systems, suppression of noise of one component by another is fundamentally hard and costly. Quantitatively, a widelycited severe lower bound on noise suppression in biomolecular systems was established by Lestas et. al. in 2010, assuming that the plant and the controller have separate birth and death reactions. This makes the precision observed in several biological phenomena, e.g., cell fate decision making and cell cycle time ordering, seem impossible. We demonstrate that coupling, a mechanism widely observed in biology, could suppress noise lower than the bound of Lestas et. al. with moderate energy cost. Furthermore, we systematically investigate the coupling mechanism in all two-node reaction networks, showing that negative feedback suppresses noise better than incoherent feedforward achitectures, coupled systems have less noise than their decoupled version for a large class of networks, and coupling has its own fundamental limitations in noise suppression. Results in this work have implications for noise suppression in biological control and provide insight for a new efficient mechanism of noise suppression in biology.

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Controlling Noisy Expression Through Auto Regulation of Burst Frequency and Protein Stability

Bokes

Singh

2019

Hybrid Systems Biology

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Buffering Gene Expression Noise by MicroRNA Based Feedforward Regulation

Cited by 11 publications

References 43 publications

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

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

Coupled Reaction Networks for Noise Suppression

Controlling Noisy Expression Through Auto Regulation of Burst Frequency and Protein Stability

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