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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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Decision Criteria.(A) Illustration of a stochastic trajectory of CII proteins from time of infection to decision. We consider the decision criteria as to whether the area under the curve of CII (light blue area) is above , or equivalently average  is above  over a particular length of time . This criteria is outlined in flow diagram B.
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pone-0103636-g002: Decision Criteria.(A) Illustration of a stochastic trajectory of CII proteins from time of infection to decision. We consider the decision criteria as to whether the area under the curve of CII (light blue area) is above , or equivalently average is above over a particular length of time . This criteria is outlined in flow diagram B.

Mentions: To determine the outcome of infection in silico, we use stochastic simulations of our model to produce stochastic realisations of the early viral gene expression using the Gillespie algorithm [25]. To quantify the cell fate decision, we need to choose a criterion which we can use to classify whether an individual simulation undergoes lysis or lysogeny. The lambda phage decision is complex and could depend on transient or steady-state dynamics of multiple factors [11]. To unravel the basic mechanisms at work, we simply assume that the decision can be determined from the transient dynamics of a single gene, CII (Fig. 2A). As illustrated in Figure 2B, we assume if the time-averaged levels of CII up to a time point () is below (above) a certain threshold the cell fate is lysis (lysogeny). Observing CII levels at a single time point instead of time averaging to determine cell fate, produces very similar results (results not shown).


Stochastic cellular fate decision making by multiple infecting lambda phage.

Robb ML, Shahrezaei V - PLoS ONE (2014)

Decision Criteria.(A) Illustration of a stochastic trajectory of CII proteins from time of infection to decision. We consider the decision criteria as to whether the area under the curve of CII (light blue area) is above , or equivalently average  is above  over a particular length of time . This criteria is outlined in flow diagram B.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0103636-g002: Decision Criteria.(A) Illustration of a stochastic trajectory of CII proteins from time of infection to decision. We consider the decision criteria as to whether the area under the curve of CII (light blue area) is above , or equivalently average is above over a particular length of time . This criteria is outlined in flow diagram B.
Mentions: To determine the outcome of infection in silico, we use stochastic simulations of our model to produce stochastic realisations of the early viral gene expression using the Gillespie algorithm [25]. To quantify the cell fate decision, we need to choose a criterion which we can use to classify whether an individual simulation undergoes lysis or lysogeny. The lambda phage decision is complex and could depend on transient or steady-state dynamics of multiple factors [11]. To unravel the basic mechanisms at work, we simply assume that the decision can be determined from the transient dynamics of a single gene, CII (Fig. 2A). As illustrated in Figure 2B, we assume if the time-averaged levels of CII up to a time point () is below (above) a certain threshold the cell fate is lysis (lysogeny). Observing CII levels at a single time point instead of time averaging to determine cell fate, produces very similar results (results not shown).

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