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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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Related in: MedlinePlus

Probability densities of the signaling molecule and parameter space. Panel A: Schematic representation of the different probability densities of the autoinducer concentration depending on the value of  and  with respect to that of . Given a set of values (, ) the dynamics of the autoinducer shows different behaviors depending on the value of the diffusion parameter since the transitions lines are located at ,  = 1 + . The constraints of our modelling in terms of the parameters values make the region on the top-left corner non-accessible (see text). Panel B: Parameter space diagram (, ) indicating the sets of parameters used in the simulations (solid squares): γ1 = (8, 2), γ2 = (15, 5), γ3 = (8, 0.5), γ4 = (15, 0.5). The experimental values reported for the degradation rate of the mRNA leads to a biological meaningful range for  (blue region). The low constitutive expression assumption is prescribed by the constraint  (red colored region).
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Figure 2: Probability densities of the signaling molecule and parameter space. Panel A: Schematic representation of the different probability densities of the autoinducer concentration depending on the value of and with respect to that of . Given a set of values (, ) the dynamics of the autoinducer shows different behaviors depending on the value of the diffusion parameter since the transitions lines are located at , = 1 + . The constraints of our modelling in terms of the parameters values make the region on the top-left corner non-accessible (see text). Panel B: Parameter space diagram (, ) indicating the sets of parameters used in the simulations (solid squares): γ1 = (8, 2), γ2 = (15, 5), γ3 = (8, 0.5), γ4 = (15, 0.5). The experimental values reported for the degradation rate of the mRNA leads to a biological meaningful range for (blue region). The low constitutive expression assumption is prescribed by the constraint (red colored region).

Mentions: Where the extremum is a maximum if and a minimum if . In the other cases the probability density does not display any extrema. Therefore, as a function of and , the probability density may show four different behaviours depending on the value of the diffusion coefficient as schematically represented in Figure 2A. However, a constraint in our modeling restricts the regions, i.e. behaviors, accessible to the autoinducer dynamics. We have assumed a low constitutive expression such that only a single mRNA molecule can be transcribed at a time. The latter implies that


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

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

Probability densities of the signaling molecule and parameter space. Panel A: Schematic representation of the different probability densities of the autoinducer concentration depending on the value of  and  with respect to that of . Given a set of values (, ) the dynamics of the autoinducer shows different behaviors depending on the value of the diffusion parameter since the transitions lines are located at ,  = 1 + . The constraints of our modelling in terms of the parameters values make the region on the top-left corner non-accessible (see text). Panel B: Parameter space diagram (, ) indicating the sets of parameters used in the simulations (solid squares): γ1 = (8, 2), γ2 = (15, 5), γ3 = (8, 0.5), γ4 = (15, 0.5). The experimental values reported for the degradation rate of the mRNA leads to a biological meaningful range for  (blue region). The low constitutive expression assumption is prescribed by the constraint  (red colored region).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Probability densities of the signaling molecule and parameter space. Panel A: Schematic representation of the different probability densities of the autoinducer concentration depending on the value of and with respect to that of . Given a set of values (, ) the dynamics of the autoinducer shows different behaviors depending on the value of the diffusion parameter since the transitions lines are located at , = 1 + . The constraints of our modelling in terms of the parameters values make the region on the top-left corner non-accessible (see text). Panel B: Parameter space diagram (, ) indicating the sets of parameters used in the simulations (solid squares): γ1 = (8, 2), γ2 = (15, 5), γ3 = (8, 0.5), γ4 = (15, 0.5). The experimental values reported for the degradation rate of the mRNA leads to a biological meaningful range for (blue region). The low constitutive expression assumption is prescribed by the constraint (red colored region).
Mentions: Where the extremum is a maximum if and a minimum if . In the other cases the probability density does not display any extrema. Therefore, as a function of and , the probability density may show four different behaviours depending on the value of the diffusion coefficient as schematically represented in Figure 2A. However, a constraint in our modeling restricts the regions, i.e. behaviors, accessible to the autoinducer dynamics. We have assumed a low constitutive expression such that only a single mRNA molecule can be transcribed at a time. The latter implies that

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