Linear noise approximation for stochastic oscillations of intracellular calcium

Cantini, Laura; Cianci, Claudia; Fanelli, Duccio; Massi, Emma; Barletti, Luigi

doi:10.1016/j.jtbi.2014.01.035

Cited by 8 publications

(8 citation statements)

References 27 publications

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“…Earlier research suggested that noise has great effects on the dynamic activity of the system [57,58]. In our study, the minor effects of noises on the system mainly refer to the characteristics of the system such as the bistable phenomenon of the rate equation exhibited.…”

Section: Discussionmentioning

confidence: 72%

Negative feedback contributes to the stochastic expression of the interferon-β gene in virus-triggered type I interferon signaling pathways

Zhang

Tian

Zou

2015

Mathematical Biosciences

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Section: Discussionmentioning

confidence: 72%

Negative feedback contributes to the stochastic expression of the interferon-β gene in virus-triggered type I interferon signaling pathways

Zhang

Tian

Zou

2015

Mathematical Biosciences

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“…In important biological processes such as cell cycle regulation [ 6 , 9 , 10 ], maintainance of stemness [ 36 , 37 ], differentiation regulation [ 36 , 37 ] etc., intrinsic noise has been shown to play crucial role to determine the ultimate cell fate. Not only that, intrinsic noise can even drive a system to a completely different dynamical regime which is not permitted if we analyze the corresponding deterministic model under same conditions [ 38 , 39 ]. One of the standard practice to tackle intrinsic noise theoretically is to construct a proper mechanistic model in terms of mass action kinetic terms for the corresponding gene regulatory network under consideration and employ the Gillespie stochastic simulation algorithm (SSA) [ 16 ] to accurately quantify the molecular noise present in the system.…”

Section: Discussionmentioning

confidence: 99%

Are Quasi-Steady-State Approximated Models Suitable for Quantifying Intrinsic Noise Accurately?

Sengupta

Kar

2015

PLoS ONE

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Large gene regulatory networks (GRN) are often modeled with quasi-steady-state approximation (QSSA) to reduce the huge computational time required for intrinsic noise quantification using Gillespie stochastic simulation algorithm (SSA). However, the question still remains whether the stochastic QSSA model measures the intrinsic noise as accurately as the SSA performed for a detailed mechanistic model or not? To address this issue, we have constructed mechanistic and QSSA models for few frequently observed GRNs exhibiting switching behavior and performed stochastic simulations with them. Our results strongly suggest that the performance of a stochastic QSSA model in comparison to SSA performed for a mechanistic model critically relies on the absolute values of the mRNA and protein half-lives involved in the corresponding GRN. The extent of accuracy level achieved by the stochastic QSSA model calculations will depend on the level of bursting frequency generated due to the absolute value of the half-life of either mRNA or protein or for both the species. For the GRNs considered, the stochastic QSSA quantifies the intrinsic noise at the protein level with greater accuracy and for larger combinations of half-life values of mRNA and protein, whereas in case of mRNA the satisfactory accuracy level can only be reached for limited combinations of absolute values of half-lives. Further, we have clearly demonstrated that the abundance levels of mRNA and protein hardly matter for such comparison between QSSA and mechanistic models. Based on our findings, we conclude that QSSA model can be a good choice for evaluating intrinsic noise for other GRNs as well, provided we make a rational choice based on experimental half-life values available in literature.

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“…To this end we consider stochastic simulations of the elementary reactions [ 24 ], and the strategy based on the linear-noise approximation (LNA). The LNA is broadly used to study how noise affects reaction networks at the cellular level [ 25 – 27 ]. This approach provides exact predictions for reaction networks composed of zero- and first-order reactions, or for networks involving second-order reactions and large volumes at constant concentrations.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, it has been proved that the precision of LNA predictions can be extended to a strict class of chemical systems with second-order reactions for all volumes [ 28 ]. This approach has been previously used to estimate power spectrum derived from biochemical circuits [ 20 , 27 ], and from genetic oscillators [ 26 , 29 ]. In this approximation the fluctuations around the mean concentrations Eq (1) are given by [ 25 ]: where and are the Jacobian and diffusion matrices, respectively, and is a Wiener process.…”

Section: Methodsmentioning

confidence: 99%

Promoters Architecture-Based Mechanism for Noise-Induced Oscillations in a Single-Gene Circuit

2016

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It is well known that single-gene circuits with negative feedback loop can lead to oscillatory gene expression when they operate with time delay. In order to generate these oscillations many processes can contribute to properly timing such delay. Here we show that the time delay coming from the transitions between internal states of the cis-regulatory system (CRS) can drive sustained oscillations in an auto-repressive single-gene circuit operating in a small volume like a cell. We found that the cooperative binding of repressor molecules is not mandatory for a oscillatory behavior if there are enough binding sites in the CRS. These oscillations depend on an adequate balance between the CRS kinetic, and the synthesis/degradation rates of repressor molecules. This finding suggest that the multi-site CRS architecture can play a key role for oscillatory behavior of gene expression. Finally, our results can also help to synthetic biologists on the design of the promoters architecture for new genetic oscillatory circuits.

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Linear noise approximation for stochastic oscillations of intracellular calcium

Cited by 8 publications

References 27 publications

Negative feedback contributes to the stochastic expression of the interferon-β gene in virus-triggered type I interferon signaling pathways

Negative feedback contributes to the stochastic expression of the interferon-β gene in virus-triggered type I interferon signaling pathways

Are Quasi-Steady-State Approximated Models Suitable for Quantifying Intrinsic Noise Accurately?

Promoters Architecture-Based Mechanism for Noise-Induced Oscillations in a Single-Gene Circuit

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