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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 Effect of Growth Media on Rate of Lysogeny.(A) Division time for cells infected with lambda phage. For MOI this is the division time of the cell, for MOI this is the division time for infected cells that choose lysogeny. (B) Effect of growth rate on rate of lysogeny in different growth media. Here, simulations compared the rate of lysogeny in cells of the same size with different MOI. Growth rate modulation of transcription rate are similar to the study by Klumpp et al.[33].
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pone-0103636-g008: The Effect of Growth Media on Rate of Lysogeny.(A) Division time for cells infected with lambda phage. For MOI this is the division time of the cell, for MOI this is the division time for infected cells that choose lysogeny. (B) Effect of growth rate on rate of lysogeny in different growth media. Here, simulations compared the rate of lysogeny in cells of the same size with different MOI. Growth rate modulation of transcription rate are similar to the study by Klumpp et al.[33].

Mentions: Growth media can modulate bacterial growth rate (elongation rate) and cell cycle time [33]. Since, we observed MOI modulation of growth rate affects rates of lysogeny, we argued growth modulation by growth media should have similar effects. To investigate this phenomenon using our model, we assume modulation of cellular growth rate does not reduce cell cycle time below 60 minutes over which we estimate the rate of lysogeny. This is a reasonable assumption since in the analysis of experimental movies from [24], we observe that phage infection delays division time (in the generation of cells that are infected) for cells that undergo lysogeny (Figure 8A). We use growth rate modulation of transcription rate and dilution to estimate rate of lysogeny for the MOI and MOI in a cell of the same size (Figure 8B). Note that the rate of lysogeny is higher for MOI due to the higher viral concentration. The model predicts that a decrease in growth rate will lead to sharp increase in rate of lysogeny. We also observe that apart from the extreme points, where the rate of lysogeny is close to 0 or 1, that the observed difference between MOI and MOI is largely preserved (Figure 8B).


Stochastic cellular fate decision making by multiple infecting lambda phage.

Robb ML, Shahrezaei V - PLoS ONE (2014)

The Effect of Growth Media on Rate of Lysogeny.(A) Division time for cells infected with lambda phage. For MOI this is the division time of the cell, for MOI this is the division time for infected cells that choose lysogeny. (B) Effect of growth rate on rate of lysogeny in different growth media. Here, simulations compared the rate of lysogeny in cells of the same size with different MOI. Growth rate modulation of transcription rate are similar to the study by Klumpp et al.[33].
© Copyright Policy
Related In: Results  -  Collection

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

pone-0103636-g008: The Effect of Growth Media on Rate of Lysogeny.(A) Division time for cells infected with lambda phage. For MOI this is the division time of the cell, for MOI this is the division time for infected cells that choose lysogeny. (B) Effect of growth rate on rate of lysogeny in different growth media. Here, simulations compared the rate of lysogeny in cells of the same size with different MOI. Growth rate modulation of transcription rate are similar to the study by Klumpp et al.[33].
Mentions: Growth media can modulate bacterial growth rate (elongation rate) and cell cycle time [33]. Since, we observed MOI modulation of growth rate affects rates of lysogeny, we argued growth modulation by growth media should have similar effects. To investigate this phenomenon using our model, we assume modulation of cellular growth rate does not reduce cell cycle time below 60 minutes over which we estimate the rate of lysogeny. This is a reasonable assumption since in the analysis of experimental movies from [24], we observe that phage infection delays division time (in the generation of cells that are infected) for cells that undergo lysogeny (Figure 8A). We use growth rate modulation of transcription rate and dilution to estimate rate of lysogeny for the MOI and MOI in a cell of the same size (Figure 8B). Note that the rate of lysogeny is higher for MOI due to the higher viral concentration. The model predicts that a decrease in growth rate will lead to sharp increase in rate of lysogeny. We also observe that apart from the extreme points, where the rate of lysogeny is close to 0 or 1, that the observed difference between MOI and MOI is largely preserved (Figure 8B).

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