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Bursting noise in gene expression dynamics: linking microscopic and mesoscopic models.

Lin YT, Galla T - J R Soc Interface (2016)

Bottom Line: The dynamics of short-lived mRNA results in bursts of protein production in gene regulatory networks.We investigate the propagation of bursting noise between different levels of mathematical modelling and demonstrate that conventional approaches based on diffusion approximations can fail to capture bursting noise.An alternative coarse-grained model, the so-called piecewise deterministic Markov process (PDMP), is seen to outperform the diffusion approximation in biologically relevant parameter regimes.

View Article: PubMed Central - PubMed

Affiliation: Theoretical Physics, School of Physics and Astronomy, The University of Manchester, Manchester M13 9PL, UK yenting.lin@manchester.ac.uk.

No MeSH data available.


Related in: MedlinePlus

Stationary distribution of protein numbers, shown in the range  on a linear scale on both axes. (a) FM: full model describing the mRNA and protein populations; (b) GB: protein-only model with geometrically distributed bursts; (c) CB: protein-only model with constant bursts; and (d) NB: protein-only model without bursts. (Online version in colour.)
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RSIF20150772F2: Stationary distribution of protein numbers, shown in the range on a linear scale on both axes. (a) FM: full model describing the mRNA and protein populations; (b) GB: protein-only model with geometrically distributed bursts; (c) CB: protein-only model with constant bursts; and (d) NB: protein-only model without bursts. (Online version in colour.)

Mentions: Numerical simulations of each of the models are carried out using standard methods [27,28]. In the following, we present statistical properties of the models, leaving typical sample paths to the electronic supplementary material. FigureĀ 2 displays the numerically computed stationary distributions for the FM, GB, CB and NB models. In this section, we discuss the outcomes of the different models qualitatively. A more quantitative comparison of the stationary distributions can be found in the electronic supplementary material.Figure 2.


Bursting noise in gene expression dynamics: linking microscopic and mesoscopic models.

Lin YT, Galla T - J R Soc Interface (2016)

Stationary distribution of protein numbers, shown in the range  on a linear scale on both axes. (a) FM: full model describing the mRNA and protein populations; (b) GB: protein-only model with geometrically distributed bursts; (c) CB: protein-only model with constant bursts; and (d) NB: protein-only model without bursts. (Online version in colour.)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4759790&req=5

RSIF20150772F2: Stationary distribution of protein numbers, shown in the range on a linear scale on both axes. (a) FM: full model describing the mRNA and protein populations; (b) GB: protein-only model with geometrically distributed bursts; (c) CB: protein-only model with constant bursts; and (d) NB: protein-only model without bursts. (Online version in colour.)
Mentions: Numerical simulations of each of the models are carried out using standard methods [27,28]. In the following, we present statistical properties of the models, leaving typical sample paths to the electronic supplementary material. FigureĀ 2 displays the numerically computed stationary distributions for the FM, GB, CB and NB models. In this section, we discuss the outcomes of the different models qualitatively. A more quantitative comparison of the stationary distributions can be found in the electronic supplementary material.Figure 2.

Bottom Line: The dynamics of short-lived mRNA results in bursts of protein production in gene regulatory networks.We investigate the propagation of bursting noise between different levels of mathematical modelling and demonstrate that conventional approaches based on diffusion approximations can fail to capture bursting noise.An alternative coarse-grained model, the so-called piecewise deterministic Markov process (PDMP), is seen to outperform the diffusion approximation in biologically relevant parameter regimes.

View Article: PubMed Central - PubMed

Affiliation: Theoretical Physics, School of Physics and Astronomy, The University of Manchester, Manchester M13 9PL, UK yenting.lin@manchester.ac.uk.

No MeSH data available.


Related in: MedlinePlus