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Effects of promoter leakage on dynamics of gene expression.

Huang L, Yuan Z, Liu P, Zhou T - BMC Syst Biol (2015)

Bottom Line: Quantitative analysis of simple molecular networks is an important step forward understanding fundamental intracellular processes.We first derive the analytical distribution of gene product, and then analyze effects of promoter leakage on expression dynamics including bursting kinetics.Specifically, promoter leakage is a universal mechanism of reducing expression noise, controlling phenotypes in different environments and making the gene produce generate fewer bursts.

View Article: PubMed Central - PubMed

Affiliation: Guangdong Province Key Laboratory of Computational Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, 510275, PR China. hlfang208@163.com.

ABSTRACT

Background: Quantitative analysis of simple molecular networks is an important step forward understanding fundamental intracellular processes. As network motifs occurring recurrently in complex biological networks, gene auto-regulatory circuits have been extensively studied but gene expression dynamics remain to be fully understood, e.g., how promoter leakage affects expression noise is unclear.

Results: In this work, we analyze a gene model with auto regulation, where the promoter is assumed to have one active state with highly efficient transcription and one inactive state with very lowly efficient transcription (termed as promoter leakage). We first derive the analytical distribution of gene product, and then analyze effects of promoter leakage on expression dynamics including bursting kinetics. Interestingly, we find that promoter leakage always reduces expression noise and that increasing the leakage rate tends to simplify phenotypes. In addition, higher leakage results in fewer bursts.

Conclusions: Our results reveal the essential role of promoter leakage in controlling expression dynamics and further phenotype. Specifically, promoter leakage is a universal mechanism of reducing expression noise, controlling phenotypes in different environments and making the gene produce generate fewer bursts.

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Related in: MedlinePlus

Effects of promoter leakage on distribution. (A) no feedback (f = 0): the gene product distribution changes from bimodality to unimodality when the leakage rate increases. The parameter values are set as λ1 = 40, γ0 = 0.1, γ1 = 0.2; (B) negative feedback: only one peak closed to the origin gradually becomes another peak away from the origin with the increase of the leakage rate. Other parameter values are set as λ1 = 40, γ0 = 0.1, γ1 = 0.2, f = 0.1; (C) positive feedback: two peaks gradually become one peak away from the origin with the increase of the leakage rate, where the parameter values are set as λ0 = 40, γ0 = 0.1, γ1 = 0.5, f = 0.1.
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Fig3: Effects of promoter leakage on distribution. (A) no feedback (f = 0): the gene product distribution changes from bimodality to unimodality when the leakage rate increases. The parameter values are set as λ1 = 40, γ0 = 0.1, γ1 = 0.2; (B) negative feedback: only one peak closed to the origin gradually becomes another peak away from the origin with the increase of the leakage rate. Other parameter values are set as λ1 = 40, γ0 = 0.1, γ1 = 0.2, f = 0.1; (C) positive feedback: two peaks gradually become one peak away from the origin with the increase of the leakage rate, where the parameter values are set as λ0 = 40, γ0 = 0.1, γ1 = 0.5, f = 0.1.

Mentions: It has been shown that a two-state gene model can exhibit bimodal distributions if neither promoter leakage nor auto regulation is considered [28,59]. Bimodality can also occur even in the presence of regulation [60,61]. If promoter leakage is considered, however, we find that the situation is different. The numerical results are demonstrated in Figure 3, where we consider three cases: no feedback, which corresponds to f = 0; negative feedback, which implies that D1 represents the active state but λ1 corresponds to the normal transcription rate whereas λ0 to the leakage rate; and positive feedback, which implies that D0 represents the active state but λ0 corresponds to the normal transcription rate whereas λ1 to the leakage rate.Figure 3


Effects of promoter leakage on dynamics of gene expression.

Huang L, Yuan Z, Liu P, Zhou T - BMC Syst Biol (2015)

Effects of promoter leakage on distribution. (A) no feedback (f = 0): the gene product distribution changes from bimodality to unimodality when the leakage rate increases. The parameter values are set as λ1 = 40, γ0 = 0.1, γ1 = 0.2; (B) negative feedback: only one peak closed to the origin gradually becomes another peak away from the origin with the increase of the leakage rate. Other parameter values are set as λ1 = 40, γ0 = 0.1, γ1 = 0.2, f = 0.1; (C) positive feedback: two peaks gradually become one peak away from the origin with the increase of the leakage rate, where the parameter values are set as λ0 = 40, γ0 = 0.1, γ1 = 0.5, f = 0.1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4384279&req=5

Fig3: Effects of promoter leakage on distribution. (A) no feedback (f = 0): the gene product distribution changes from bimodality to unimodality when the leakage rate increases. The parameter values are set as λ1 = 40, γ0 = 0.1, γ1 = 0.2; (B) negative feedback: only one peak closed to the origin gradually becomes another peak away from the origin with the increase of the leakage rate. Other parameter values are set as λ1 = 40, γ0 = 0.1, γ1 = 0.2, f = 0.1; (C) positive feedback: two peaks gradually become one peak away from the origin with the increase of the leakage rate, where the parameter values are set as λ0 = 40, γ0 = 0.1, γ1 = 0.5, f = 0.1.
Mentions: It has been shown that a two-state gene model can exhibit bimodal distributions if neither promoter leakage nor auto regulation is considered [28,59]. Bimodality can also occur even in the presence of regulation [60,61]. If promoter leakage is considered, however, we find that the situation is different. The numerical results are demonstrated in Figure 3, where we consider three cases: no feedback, which corresponds to f = 0; negative feedback, which implies that D1 represents the active state but λ1 corresponds to the normal transcription rate whereas λ0 to the leakage rate; and positive feedback, which implies that D0 represents the active state but λ0 corresponds to the normal transcription rate whereas λ1 to the leakage rate.Figure 3

Bottom Line: Quantitative analysis of simple molecular networks is an important step forward understanding fundamental intracellular processes.We first derive the analytical distribution of gene product, and then analyze effects of promoter leakage on expression dynamics including bursting kinetics.Specifically, promoter leakage is a universal mechanism of reducing expression noise, controlling phenotypes in different environments and making the gene produce generate fewer bursts.

View Article: PubMed Central - PubMed

Affiliation: Guangdong Province Key Laboratory of Computational Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, 510275, PR China. hlfang208@163.com.

ABSTRACT

Background: Quantitative analysis of simple molecular networks is an important step forward understanding fundamental intracellular processes. As network motifs occurring recurrently in complex biological networks, gene auto-regulatory circuits have been extensively studied but gene expression dynamics remain to be fully understood, e.g., how promoter leakage affects expression noise is unclear.

Results: In this work, we analyze a gene model with auto regulation, where the promoter is assumed to have one active state with highly efficient transcription and one inactive state with very lowly efficient transcription (termed as promoter leakage). We first derive the analytical distribution of gene product, and then analyze effects of promoter leakage on expression dynamics including bursting kinetics. Interestingly, we find that promoter leakage always reduces expression noise and that increasing the leakage rate tends to simplify phenotypes. In addition, higher leakage results in fewer bursts.

Conclusions: Our results reveal the essential role of promoter leakage in controlling expression dynamics and further phenotype. Specifically, promoter leakage is a universal mechanism of reducing expression noise, controlling phenotypes in different environments and making the gene produce generate fewer bursts.

Show MeSH
Related in: MedlinePlus