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Noise regulation by quorum sensing in low mRNA copy number systems.

Weber M, Buceta J - BMC Syst Biol (2011)

Bottom Line: The interplay between noisy sources and the communication process produces a repertoire of dynamics that depends on the diffusion rate.Importantly, the total noise shows a non-monotonic behavior as a function of the diffusion rate.QS systems seems to avoid values of the diffusion that maximize the total noise.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computer Simulation and Modelling (Co.S.Mo.) Lab, Parc Científic de Barcelona, C/Baldiri Reixac 10-12, Barcelona 08028, Spain.

ABSTRACT

Background: Cells must face the ubiquitous presence of noise at the level of signaling molecules. The latter constitutes a major challenge for the regulation of cellular functions including communication processes. In the context of prokaryotic communication, the so-called quorum sensing (QS) mechanism relies on small diffusive molecules that are produced and detected by cells. This poses the intriguing question of how bacteria cope with the fluctuations for setting up a reliable information exchange.

Results: We present a stochastic model of gene expression that accounts for the main biochemical processes that describe the QS mechanism close to its activation threshold. Within that framework we study, both numerically and analytically, the role that diffusion plays in the regulation of the dynamics and the fluctuations of signaling molecules. In addition, we unveil the contribution of different sources of noise, intrinsic and transcriptional, in the QS mechanism.

Conclusions: The interplay between noisy sources and the communication process produces a repertoire of dynamics that depends on the diffusion rate. Importantly, the total noise shows a non-monotonic behavior as a function of the diffusion rate. QS systems seems to avoid values of the diffusion that maximize the total noise. These results point towards the direction that bacteria have adapted their communication mechanisms in order to improve the signal-to-noise ratio.

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Distributions and dynamics of the signaling molecule in a diffusionless system. Panel A: Distributions of cA at steady-state for different sets of parameters (, ) as indicated in Figure 2B. In all cases  = 0. The histogram obtained in simulations (blue bars) compares well with the distribution from the analytical calculations (blue line). Yet, deviations are observed due to intrinsic noise (see text). Panel B: The dynamics of the autoinducer show different behaviors depending on the region of the parameters phase space (see Figure 2). Two typical trajectories are shown with a grey-shaded background indicating the presence of a mRNA molecule in the cell.
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Figure 3: Distributions and dynamics of the signaling molecule in a diffusionless system. Panel A: Distributions of cA at steady-state for different sets of parameters (, ) as indicated in Figure 2B. In all cases = 0. The histogram obtained in simulations (blue bars) compares well with the distribution from the analytical calculations (blue line). Yet, deviations are observed due to intrinsic noise (see text). Panel B: The dynamics of the autoinducer show different behaviors depending on the region of the parameters phase space (see Figure 2). Two typical trajectories are shown with a grey-shaded background indicating the presence of a mRNA molecule in the cell.

Mentions: The distribution of cA at the steady-state is computed for the different parameter sets according to the ranges and constraints described above (section Methods). In order to explore the role of the diffusion in the dynamics of the signaling molecule we first study the case . According to the analytical calculations, see Eq. (17), in this case two possible distributions for the concentration of cA can be observed depending on the value of . Since > 1 we can expect a maximum only if > 1 (note that if ), otherwise extrema are not expected. The results of the numerical simulations (Gillespie), Figure 3A, reveal that scenario. Note that in all cases the histogram obtained from the simulations fits fairly well to the expression (12) except for deviations due to the intrinsic noise that are not taken into account by the analytical approach. The differences among dynamics are evidenced by the trajectories, Figure 3B. Thus, for < 1 the dynamics of the autoinducer shows a burst-like behavior. If > 1 the frequency of bursts is high enough to maintain the concentration of signaling molecules near the average and a single-peak distribution develops.


Noise regulation by quorum sensing in low mRNA copy number systems.

Weber M, Buceta J - BMC Syst Biol (2011)

Distributions and dynamics of the signaling molecule in a diffusionless system. Panel A: Distributions of cA at steady-state for different sets of parameters (, ) as indicated in Figure 2B. In all cases  = 0. The histogram obtained in simulations (blue bars) compares well with the distribution from the analytical calculations (blue line). Yet, deviations are observed due to intrinsic noise (see text). Panel B: The dynamics of the autoinducer show different behaviors depending on the region of the parameters phase space (see Figure 2). Two typical trajectories are shown with a grey-shaded background indicating the presence of a mRNA molecule in the cell.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3037314&req=5

Figure 3: Distributions and dynamics of the signaling molecule in a diffusionless system. Panel A: Distributions of cA at steady-state for different sets of parameters (, ) as indicated in Figure 2B. In all cases = 0. The histogram obtained in simulations (blue bars) compares well with the distribution from the analytical calculations (blue line). Yet, deviations are observed due to intrinsic noise (see text). Panel B: The dynamics of the autoinducer show different behaviors depending on the region of the parameters phase space (see Figure 2). Two typical trajectories are shown with a grey-shaded background indicating the presence of a mRNA molecule in the cell.
Mentions: The distribution of cA at the steady-state is computed for the different parameter sets according to the ranges and constraints described above (section Methods). In order to explore the role of the diffusion in the dynamics of the signaling molecule we first study the case . According to the analytical calculations, see Eq. (17), in this case two possible distributions for the concentration of cA can be observed depending on the value of . Since > 1 we can expect a maximum only if > 1 (note that if ), otherwise extrema are not expected. The results of the numerical simulations (Gillespie), Figure 3A, reveal that scenario. Note that in all cases the histogram obtained from the simulations fits fairly well to the expression (12) except for deviations due to the intrinsic noise that are not taken into account by the analytical approach. The differences among dynamics are evidenced by the trajectories, Figure 3B. Thus, for < 1 the dynamics of the autoinducer shows a burst-like behavior. If > 1 the frequency of bursts is high enough to maintain the concentration of signaling molecules near the average and a single-peak distribution develops.

Bottom Line: The interplay between noisy sources and the communication process produces a repertoire of dynamics that depends on the diffusion rate.Importantly, the total noise shows a non-monotonic behavior as a function of the diffusion rate.QS systems seems to avoid values of the diffusion that maximize the total noise.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computer Simulation and Modelling (Co.S.Mo.) Lab, Parc Científic de Barcelona, C/Baldiri Reixac 10-12, Barcelona 08028, Spain.

ABSTRACT

Background: Cells must face the ubiquitous presence of noise at the level of signaling molecules. The latter constitutes a major challenge for the regulation of cellular functions including communication processes. In the context of prokaryotic communication, the so-called quorum sensing (QS) mechanism relies on small diffusive molecules that are produced and detected by cells. This poses the intriguing question of how bacteria cope with the fluctuations for setting up a reliable information exchange.

Results: We present a stochastic model of gene expression that accounts for the main biochemical processes that describe the QS mechanism close to its activation threshold. Within that framework we study, both numerically and analytically, the role that diffusion plays in the regulation of the dynamics and the fluctuations of signaling molecules. In addition, we unveil the contribution of different sources of noise, intrinsic and transcriptional, in the QS mechanism.

Conclusions: The interplay between noisy sources and the communication process produces a repertoire of dynamics that depends on the diffusion rate. Importantly, the total noise shows a non-monotonic behavior as a function of the diffusion rate. QS systems seems to avoid values of the diffusion that maximize the total noise. These results point towards the direction that bacteria have adapted their communication mechanisms in order to improve the signal-to-noise ratio.

Show MeSH
Related in: MedlinePlus