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

Wang, Haohua; Liu, Yijie; Li, Qingqing

doi:10.1002/1873-3468.13012

Cited by 3 publications

(2 citation statements)

References 66 publications

(116 reference statements)

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“…The posttranscriptional regulation process can be studied at a global level by analyzing the gene regulatory network to identify key regulators that are important in different regulatory pathways 20,21,22 . At 23 a more abstract level, we can also study quantitatively how the regulator can modify the gene expression level with given reaction scheme and parameters 24,25,26,23 , which is the focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the noise in bursty gene expression under regulation by small RNAs

Qiu

Jia

2019

Int. J. Mod. Phys. C

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Gene expression is a fundamental process in a living system. The small RNAs (sRNAs) is widely observed as a global regulator in gene expression. The inherent nonlinearity in this regulatory process together with the bursty production of messenger RNA (mRNA), sRNA and protein make the exact solution for this stochastic process intractable. This is particularly the case when quantifying the protein noise level, which has great impact on multiple cellular processes. Here, we propose an approximate yet reasonably accurate solution for the gene expression noise with infrequent burst and strong regulation by sRNAs. This analytical solution allows us to better analyze the noise and stochastic deviation of protein level. We find that the regulation amplifies the noise, reduces the protein level. The stochasticity in the regulation generates more proteins than what if the stochasticity is removed from the system. The sRNA level is most important to the relationship between the noise and stochastic deviation. The results provide analytical tools for more general studies of gene expression and strengthen our quantitative understandings of post-transcriptional regulation in controlling gene expression processes.

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Section: Introductionmentioning

confidence: 99%

Quantifying the noise in bursty gene expression under regulation by small RNAs

Qiu

Jia

2019

Int. J. Mod. Phys. C

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“…Feedback can be negative and positive; here, we shall specifically focus on positive feedback on burst size. The impact of negative feedback on burst size and burst frequency has been investigated in [8,9] Models of stochastic gene expression are typically based on the random telegraph framework [10][11][12][13][14][15][16][17]. In a random telegraph model, a gene can be either in an On state or in an Off state, transitioning randomly in time between the two.…”

Section: Introductionmentioning

confidence: 99%

Maintaining Gene Expression Levels by Positive Feedback in Burst Size in the Presence of Infinitesimal Delay

Bokes

2018

Preprint

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Synthesis of individual molecules in the expression of genes often occurs in bursts of multiple copies. Gene regulatory feedback can affect the frequency with which these bursts occur or their size. Whereas frequency regulation has traditionally received more attention, we focus specifically on the regulation of burst size. It turns out that there are (at least) two alternative formulations of feedback in burst size. In the first, newly produced molecules immediately partake in feedback, even within the same burst. In the second, there is no within-burst regulation due to what we call infinitesimal delay. We describe both alternatives using a minimalistic Markovian drift-jump framework combining discrete and continuous dynamics. We derive detailed analytic results and efficient simulation algorithms for positive noncooperative autoregulation (whether infinitesimally delayed or not). We show that at steady state both alternatives lead to a gamma distribution of protein level. The steady-state distribution becomes available only after a transcritical bifurcation point is passed. Interestingly, the onset of the bifurcation is postponed by the inclusion of infinitesimal delay.

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