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Stochastic cellular fate decision making by multiple infecting lambda phage.

Robb ML, Shahrezaei V - PLoS ONE (2014)

Bottom Line: Here, we attempt to provide a mechanistic explanation of these results using a simple stochastic model of the lambda phage genetic network.Several potential factors including intrinsic gene expression noise, spatial dynamics and cell-cycle effects are investigated.However, simulations suggest spatial segregation of phage particles does not play a significant role.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Imperial College, London, United Kingdom.

ABSTRACT
Bacteriophage lambda is a classic system for the study of cellular decision making. Both experiments and mathematical models have demonstrated the importance of viral concentration in the lysis-lysogeny decision outcome in lambda phage. However, a recent experimental study using single cell and single phage resolution reported that cells with the same viral concentrations but different numbers of infecting phage (multiplicity of infection) can have markedly different rates of lysogeny. Thus the decision depends on not only viral concentration, but also directly on the number of infecting phage. Here, we attempt to provide a mechanistic explanation of these results using a simple stochastic model of the lambda phage genetic network. Several potential factors including intrinsic gene expression noise, spatial dynamics and cell-cycle effects are investigated. We find that interplay between the level of intrinsic noise and viral protein decision threshold is a major factor that produces dependence on multiplicity of infection. However, simulations suggest spatial segregation of phage particles does not play a significant role. Cellular image processing is used to re-analyse the original time-lapse movies from the recent study and it is found that higher numbers of infecting phage reduce the cell elongation rate. This could also contribute to the observed phenomena as cellular growth rate can affect transcription rates. Our model further predicts that rate of lysogeny is dependent on bacterial growth rate, which can be experimentally tested. Our study provides new insight on the mechanisms of individual phage decision making. More generally, our results are relevant for the understanding of gene-dosage compensation in cellular systems.

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The Role of Spatial Effects.Average CII concentrations for different rates of diffusion. (A) MOI = 1, V = 1, phage at centre (,); (B) MOI = 2, V = 2, phage at centre (,); (C) MOI = 2, V = 2, phages at a quarter and three quarter of the cell length (,); (D) MOI = 2, V = 2, phage at cell poles (,). Average [CII] with for non-spatial model (blue line) shown alongside spatial model (yellow lines). Results based on  simulations. (E) Rate of lysogeny across different threshold values for spatial model with original diffusion rates (solid lines). Results are shown alongside the non-spatial results (dashed lines).
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pone-0103636-g006: The Role of Spatial Effects.Average CII concentrations for different rates of diffusion. (A) MOI = 1, V = 1, phage at centre (,); (B) MOI = 2, V = 2, phage at centre (,); (C) MOI = 2, V = 2, phages at a quarter and three quarter of the cell length (,); (D) MOI = 2, V = 2, phage at cell poles (,). Average [CII] with for non-spatial model (blue line) shown alongside spatial model (yellow lines). Results based on simulations. (E) Rate of lysogeny across different threshold values for spatial model with original diffusion rates (solid lines). Results are shown alongside the non-spatial results (dashed lines).

Mentions: Phage can infect bacteria at any position along the cell surface, although recent research suggests that phage prefers the poles [24], [29]. Thus, it is likely that multiple phage are spatially separated in the bacteria. Delay caused by the diffusion of biomolecules from one phage to another could affect the rate of lysogeny, particularly when diffusion is slow or the infecting phages are far apart. To investigate possible spatial effects on rate of lysogeny, we use a particle-based approach that tracks individual molecules as they diffuse and react inside the cell [30]. We compare the case where there is one infecting phage positioned at the centre of a small cell with the case where two phage arranged in 3 different ways infect a cell with double the volume, therefore keeping viral concentration the same. In these cases two phages will be positioned either at the centre, at a quarter and three quarter of the cell length or at the cell poles (Figure 6A–D). The results were also compared with the non-spatial stochastic simulations outlined in the previous section. Due to the computational time required to do these simulations we perform a lower number in comparison to the non-spatial case (), still the standard error is relatively low. It can be seen in Figure 6A–D that the effect of phage positioning and diffusion rate on the mean [CII] is small. These results are also close to the non-spatial and deterministic models.


Stochastic cellular fate decision making by multiple infecting lambda phage.

Robb ML, Shahrezaei V - PLoS ONE (2014)

The Role of Spatial Effects.Average CII concentrations for different rates of diffusion. (A) MOI = 1, V = 1, phage at centre (,); (B) MOI = 2, V = 2, phage at centre (,); (C) MOI = 2, V = 2, phages at a quarter and three quarter of the cell length (,); (D) MOI = 2, V = 2, phage at cell poles (,). Average [CII] with for non-spatial model (blue line) shown alongside spatial model (yellow lines). Results based on  simulations. (E) Rate of lysogeny across different threshold values for spatial model with original diffusion rates (solid lines). Results are shown alongside the non-spatial results (dashed lines).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4126663&req=5

pone-0103636-g006: The Role of Spatial Effects.Average CII concentrations for different rates of diffusion. (A) MOI = 1, V = 1, phage at centre (,); (B) MOI = 2, V = 2, phage at centre (,); (C) MOI = 2, V = 2, phages at a quarter and three quarter of the cell length (,); (D) MOI = 2, V = 2, phage at cell poles (,). Average [CII] with for non-spatial model (blue line) shown alongside spatial model (yellow lines). Results based on simulations. (E) Rate of lysogeny across different threshold values for spatial model with original diffusion rates (solid lines). Results are shown alongside the non-spatial results (dashed lines).
Mentions: Phage can infect bacteria at any position along the cell surface, although recent research suggests that phage prefers the poles [24], [29]. Thus, it is likely that multiple phage are spatially separated in the bacteria. Delay caused by the diffusion of biomolecules from one phage to another could affect the rate of lysogeny, particularly when diffusion is slow or the infecting phages are far apart. To investigate possible spatial effects on rate of lysogeny, we use a particle-based approach that tracks individual molecules as they diffuse and react inside the cell [30]. We compare the case where there is one infecting phage positioned at the centre of a small cell with the case where two phage arranged in 3 different ways infect a cell with double the volume, therefore keeping viral concentration the same. In these cases two phages will be positioned either at the centre, at a quarter and three quarter of the cell length or at the cell poles (Figure 6A–D). The results were also compared with the non-spatial stochastic simulations outlined in the previous section. Due to the computational time required to do these simulations we perform a lower number in comparison to the non-spatial case (), still the standard error is relatively low. It can be seen in Figure 6A–D that the effect of phage positioning and diffusion rate on the mean [CII] is small. These results are also close to the non-spatial and deterministic models.

Bottom Line: Here, we attempt to provide a mechanistic explanation of these results using a simple stochastic model of the lambda phage genetic network.Several potential factors including intrinsic gene expression noise, spatial dynamics and cell-cycle effects are investigated.However, simulations suggest spatial segregation of phage particles does not play a significant role.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Imperial College, London, United Kingdom.

ABSTRACT
Bacteriophage lambda is a classic system for the study of cellular decision making. Both experiments and mathematical models have demonstrated the importance of viral concentration in the lysis-lysogeny decision outcome in lambda phage. However, a recent experimental study using single cell and single phage resolution reported that cells with the same viral concentrations but different numbers of infecting phage (multiplicity of infection) can have markedly different rates of lysogeny. Thus the decision depends on not only viral concentration, but also directly on the number of infecting phage. Here, we attempt to provide a mechanistic explanation of these results using a simple stochastic model of the lambda phage genetic network. Several potential factors including intrinsic gene expression noise, spatial dynamics and cell-cycle effects are investigated. We find that interplay between the level of intrinsic noise and viral protein decision threshold is a major factor that produces dependence on multiplicity of infection. However, simulations suggest spatial segregation of phage particles does not play a significant role. Cellular image processing is used to re-analyse the original time-lapse movies from the recent study and it is found that higher numbers of infecting phage reduce the cell elongation rate. This could also contribute to the observed phenomena as cellular growth rate can affect transcription rates. Our model further predicts that rate of lysogeny is dependent on bacterial growth rate, which can be experimentally tested. Our study provides new insight on the mechanisms of individual phage decision making. More generally, our results are relevant for the understanding of gene-dosage compensation in cellular systems.

Show MeSH
Related in: MedlinePlus