Limits...
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.

Show MeSH

Related in: MedlinePlus

Robustness of our results with respect to variations in model assumptions and parameters.(A) Rate of lysogeny across different threshold values for model with CII tetramers instead of dimers, phage replication (deterministically doubling every 3 minutes for the first 15 minutes) and CI self repression. Blue line: MOI, V; green line: MOI, V. (B) Global sensitivity analysis using 200 different parameter sets chosen by randomly varying all parameters within a factor of 2 or 10 of their nominal values. Rate of lysogeny for MOI, V against MOI, V are plotted with blue dots for changes by a factor of 2 and green dots for changes by a factor of 10. The orange dashed line represents the point where rate for MOI, V is equal to MOI, V. The rate of lysogeny for each parameter set is estimated using 500 stochastic simulations with a decision threshold set at  above the mean  value for that parameter set.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4126663&req=5

pone-0103636-g005: Robustness of our results with respect to variations in model assumptions and parameters.(A) Rate of lysogeny across different threshold values for model with CII tetramers instead of dimers, phage replication (deterministically doubling every 3 minutes for the first 15 minutes) and CI self repression. Blue line: MOI, V; green line: MOI, V. (B) Global sensitivity analysis using 200 different parameter sets chosen by randomly varying all parameters within a factor of 2 or 10 of their nominal values. Rate of lysogeny for MOI, V against MOI, V are plotted with blue dots for changes by a factor of 2 and green dots for changes by a factor of 10. The orange dashed line represents the point where rate for MOI, V is equal to MOI, V. The rate of lysogeny for each parameter set is estimated using 500 stochastic simulations with a decision threshold set at above the mean value for that parameter set.

Mentions: The proposed role of intrinsic noise in decision making is general and does not depend on the specific choice of model assumptions and parameters. To explicitly demonstrate the general validity of our results, we have performed additional analysis including parameter sensitivity analysis and modifying some of our modelling assumptions. Firstly, our model assumes CII is a dimer for simplicity, while it is known that CII is in fact tetrameric [27]. We therefore look at the effect of allowing CII dimers to bind and form tetramers, and tetramers to bind to the promoter and controlling gene expression. Secondly, there is evidence that upon infection phages replicate in the cells doubling their number every 2–3 minutes for the first 15 minutes [28]. Including phage replication in our model has the effect of increasing the mean [CII]. While CI can undergo self repression at higher concentrations of the dimer [11]. We attempted to include all of these assumptions in the model, and the results are shown in Figure 5A. It can be seen that adding these features, despite a change in the mean [CII], we observe similar qualitative results. We also tried including each of modifications individually, observing again that our conclusions still hold (results not shown). Finally, to test the effect of model parameters, we performed a global parameter sensitivity analysis on our system by varying all model parameters randomly within a factor of two or ten below and above their nominal values. For almost all parameter sets tested, using a high decision threshold (greater than mean for that parameter set), we observed a lower rate of lysogeny for the case MOI = , compared to the case MOI = , (Figure 5B), which is consistent with what is observed for the original parameter set (Figure 4C).


Stochastic cellular fate decision making by multiple infecting lambda phage.

Robb ML, Shahrezaei V - PLoS ONE (2014)

Robustness of our results with respect to variations in model assumptions and parameters.(A) Rate of lysogeny across different threshold values for model with CII tetramers instead of dimers, phage replication (deterministically doubling every 3 minutes for the first 15 minutes) and CI self repression. Blue line: MOI, V; green line: MOI, V. (B) Global sensitivity analysis using 200 different parameter sets chosen by randomly varying all parameters within a factor of 2 or 10 of their nominal values. Rate of lysogeny for MOI, V against MOI, V are plotted with blue dots for changes by a factor of 2 and green dots for changes by a factor of 10. The orange dashed line represents the point where rate for MOI, V is equal to MOI, V. The rate of lysogeny for each parameter set is estimated using 500 stochastic simulations with a decision threshold set at  above the mean  value for that parameter set.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0103636-g005: Robustness of our results with respect to variations in model assumptions and parameters.(A) Rate of lysogeny across different threshold values for model with CII tetramers instead of dimers, phage replication (deterministically doubling every 3 minutes for the first 15 minutes) and CI self repression. Blue line: MOI, V; green line: MOI, V. (B) Global sensitivity analysis using 200 different parameter sets chosen by randomly varying all parameters within a factor of 2 or 10 of their nominal values. Rate of lysogeny for MOI, V against MOI, V are plotted with blue dots for changes by a factor of 2 and green dots for changes by a factor of 10. The orange dashed line represents the point where rate for MOI, V is equal to MOI, V. The rate of lysogeny for each parameter set is estimated using 500 stochastic simulations with a decision threshold set at above the mean value for that parameter set.
Mentions: The proposed role of intrinsic noise in decision making is general and does not depend on the specific choice of model assumptions and parameters. To explicitly demonstrate the general validity of our results, we have performed additional analysis including parameter sensitivity analysis and modifying some of our modelling assumptions. Firstly, our model assumes CII is a dimer for simplicity, while it is known that CII is in fact tetrameric [27]. We therefore look at the effect of allowing CII dimers to bind and form tetramers, and tetramers to bind to the promoter and controlling gene expression. Secondly, there is evidence that upon infection phages replicate in the cells doubling their number every 2–3 minutes for the first 15 minutes [28]. Including phage replication in our model has the effect of increasing the mean [CII]. While CI can undergo self repression at higher concentrations of the dimer [11]. We attempted to include all of these assumptions in the model, and the results are shown in Figure 5A. It can be seen that adding these features, despite a change in the mean [CII], we observe similar qualitative results. We also tried including each of modifications individually, observing again that our conclusions still hold (results not shown). Finally, to test the effect of model parameters, we performed a global parameter sensitivity analysis on our system by varying all model parameters randomly within a factor of two or ten below and above their nominal values. For almost all parameter sets tested, using a high decision threshold (greater than mean for that parameter set), we observed a lower rate of lysogeny for the case MOI = , compared to the case MOI = , (Figure 5B), which is consistent with what is observed for the original parameter set (Figure 4C).

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