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Modeling early events in Francisella tularensis pathogenesis.

Gillard JJ, Laws TR, Lythe G, Molina-París C - Front Cell Infect Microbiol (2014)

Bottom Line: The model is mechanistic and governed by a small number of experimentally verifiable parameters.The mean and variance of these distributions are determined by model parameters with a precise biological interpretation, providing new mechanistic insights into the determinants of immune and bacterial kinetics.Insights into the dynamics of macrophage suppression and activation gained by the model can be used to explore the potential benefits of interventions that stimulate macrophage activation.

View Article: PubMed Central - PubMed

Affiliation: Defence Science and Technology Laboratory Porton Down, Salisbury, UK.

ABSTRACT
Computational models can provide valuable insights into the mechanisms of infection and be used as investigative tools to support development of medical treatments. We develop a stochastic, within-host, computational model of the infection process in the BALB/c mouse, following inhalational exposure to Francisella tularensis SCHU S4. The model is mechanistic and governed by a small number of experimentally verifiable parameters. Given an initial dose, the model generates bacterial load profiles corresponding to those produced experimentally, with a doubling time of approximately 5 h during the first 48 h of infection. Analytical approximations for the mean number of bacteria in phagosomes and cytosols for the first 24 h post-infection are derived and used to verify the stochastic model. In our description of the dynamics of macrophage infection, the number of bacteria released per rupturing macrophage is a geometrically-distributed random variable. When combined with doubling time, this provides a distribution for the time taken for infected macrophages to rupture and release their intracellular bacteria. The mean and variance of these distributions are determined by model parameters with a precise biological interpretation, providing new mechanistic insights into the determinants of immune and bacterial kinetics. Insights into the dynamics of macrophage suppression and activation gained by the model can be used to explore the potential benefits of interventions that stimulate macrophage activation.

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Related in: MedlinePlus

Implementing the Gillespie algorithm. At each step, one type of event is chosen, with probabilities weighted by the corresponding rates. There are twenty-four possibilities, corresponding to one of the colors and one of the three spatial locations.
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Figure 2: Implementing the Gillespie algorithm. At each step, one type of event is chosen, with probabilities weighted by the corresponding rates. There are twenty-four possibilities, corresponding to one of the colors and one of the three spatial locations.

Mentions: In our model, with large numbers of computational objects representing bacteria and macrophages, we determine which event occurs at each step as follows. The unit interval is divided into sub-intervals represented in Figure 2; each sub-interval corresponds to one type of event. Here, there are eight types of event, as described above, in three spatial compartments, twenty four in total. At each step in the simulation, the probability that a given event is the next to occur is the width of the corresponding sub-interval.


Modeling early events in Francisella tularensis pathogenesis.

Gillard JJ, Laws TR, Lythe G, Molina-París C - Front Cell Infect Microbiol (2014)

Implementing the Gillespie algorithm. At each step, one type of event is chosen, with probabilities weighted by the corresponding rates. There are twenty-four possibilities, corresponding to one of the colors and one of the three spatial locations.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4263195&req=5

Figure 2: Implementing the Gillespie algorithm. At each step, one type of event is chosen, with probabilities weighted by the corresponding rates. There are twenty-four possibilities, corresponding to one of the colors and one of the three spatial locations.
Mentions: In our model, with large numbers of computational objects representing bacteria and macrophages, we determine which event occurs at each step as follows. The unit interval is divided into sub-intervals represented in Figure 2; each sub-interval corresponds to one type of event. Here, there are eight types of event, as described above, in three spatial compartments, twenty four in total. At each step in the simulation, the probability that a given event is the next to occur is the width of the corresponding sub-interval.

Bottom Line: The model is mechanistic and governed by a small number of experimentally verifiable parameters.The mean and variance of these distributions are determined by model parameters with a precise biological interpretation, providing new mechanistic insights into the determinants of immune and bacterial kinetics.Insights into the dynamics of macrophage suppression and activation gained by the model can be used to explore the potential benefits of interventions that stimulate macrophage activation.

View Article: PubMed Central - PubMed

Affiliation: Defence Science and Technology Laboratory Porton Down, Salisbury, UK.

ABSTRACT
Computational models can provide valuable insights into the mechanisms of infection and be used as investigative tools to support development of medical treatments. We develop a stochastic, within-host, computational model of the infection process in the BALB/c mouse, following inhalational exposure to Francisella tularensis SCHU S4. The model is mechanistic and governed by a small number of experimentally verifiable parameters. Given an initial dose, the model generates bacterial load profiles corresponding to those produced experimentally, with a doubling time of approximately 5 h during the first 48 h of infection. Analytical approximations for the mean number of bacteria in phagosomes and cytosols for the first 24 h post-infection are derived and used to verify the stochastic model. In our description of the dynamics of macrophage infection, the number of bacteria released per rupturing macrophage is a geometrically-distributed random variable. When combined with doubling time, this provides a distribution for the time taken for infected macrophages to rupture and release their intracellular bacteria. The mean and variance of these distributions are determined by model parameters with a precise biological interpretation, providing new mechanistic insights into the determinants of immune and bacterial kinetics. Insights into the dynamics of macrophage suppression and activation gained by the model can be used to explore the potential benefits of interventions that stimulate macrophage activation.

Show MeSH
Related in: MedlinePlus