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A stochastic simulator of birth-death master equations with application to phylodynamics.

Vaughan TG, Drummond AJ - Mol. Biol. Evol. (2013)

Bottom Line: Importantly, phylogenetic trees or networks can be generated alongside the histories they correspond to, enabling investigations into the interplay between genealogies and population dynamics.Summary statistics such as means and variances can be recorded in place of the full ensemble, allowing for a reduction in the amount of memory used--an important consideration for models including large numbers of individual subpopulations or demes.Simulated phylogenetic trees can be recorded using the standard Newick or NEXUS formats, with extensions to these formats used for non-tree-like inheritance relationships.

View Article: PubMed Central - PubMed

Affiliation: Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand. t.g.vaughan@massey.ac.nz

ABSTRACT
In this article, we present a versatile new software tool for the simulation and analysis of stochastic models of population phylodynamics and chemical kinetics. Models are specified via an expressive and human-readable XML format and can be used as the basis for generating either single population histories or large ensembles of such histories. Importantly, phylogenetic trees or networks can be generated alongside the histories they correspond to, enabling investigations into the interplay between genealogies and population dynamics. Summary statistics such as means and variances can be recorded in place of the full ensemble, allowing for a reduction in the amount of memory used--an important consideration for models including large numbers of individual subpopulations or demes. In the case of population size histories, the resulting simulation output is written to disk in the flexible JSON format, which is easily read into numerical analysis environments such as R for visualization or further processing. Simulated phylogenetic trees can be recorded using the standard Newick or NEXUS formats, with extensions to these formats used for non-tree-like inheritance relationships.

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Using MASTER to perform within-host infection dynamics simulations. (a) Expected viral load conditional on chronic infection. (b) Relative covariance between infected cell and virion within-host populations.
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mst057-F4: Using MASTER to perform within-host infection dynamics simulations. (a) Expected viral load conditional on chronic infection. (b) Relative covariance between infected cell and virion within-host populations.

Mentions: Figure 4 illustrates the moment estimates obtained by MASTER using 103 trajectories, with reaction rates chosen from a previous publication (Vaughan et al. 2011) and an initial virus count of 1. Figure 4a shows that the absolute number of virions present in the trajectories at the time of peak viremia is on the order of , strongly justifying our use of -leaping. The effect of the conditioning on the survival of the infection is evident in the tight confidence intervals about the population size at this peak; although there remains a wide uncertainty in the viral load before the peak. Figure 4b shows the dynamics of the estimated relative covariance between the infected cell and virion populations, which we define as(13)and can calculate based on MASTER’s estimates of together with those of the individual means. The strong pre-peak-viremia correlation between the demographic fluctuations in each of these populations noted elsewhere (Vaughan et al. 2011) is clearly visible.Fig. 4.


A stochastic simulator of birth-death master equations with application to phylodynamics.

Vaughan TG, Drummond AJ - Mol. Biol. Evol. (2013)

Using MASTER to perform within-host infection dynamics simulations. (a) Expected viral load conditional on chronic infection. (b) Relative covariance between infected cell and virion within-host populations.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3649681&req=5

mst057-F4: Using MASTER to perform within-host infection dynamics simulations. (a) Expected viral load conditional on chronic infection. (b) Relative covariance between infected cell and virion within-host populations.
Mentions: Figure 4 illustrates the moment estimates obtained by MASTER using 103 trajectories, with reaction rates chosen from a previous publication (Vaughan et al. 2011) and an initial virus count of 1. Figure 4a shows that the absolute number of virions present in the trajectories at the time of peak viremia is on the order of , strongly justifying our use of -leaping. The effect of the conditioning on the survival of the infection is evident in the tight confidence intervals about the population size at this peak; although there remains a wide uncertainty in the viral load before the peak. Figure 4b shows the dynamics of the estimated relative covariance between the infected cell and virion populations, which we define as(13)and can calculate based on MASTER’s estimates of together with those of the individual means. The strong pre-peak-viremia correlation between the demographic fluctuations in each of these populations noted elsewhere (Vaughan et al. 2011) is clearly visible.Fig. 4.

Bottom Line: Importantly, phylogenetic trees or networks can be generated alongside the histories they correspond to, enabling investigations into the interplay between genealogies and population dynamics.Summary statistics such as means and variances can be recorded in place of the full ensemble, allowing for a reduction in the amount of memory used--an important consideration for models including large numbers of individual subpopulations or demes.Simulated phylogenetic trees can be recorded using the standard Newick or NEXUS formats, with extensions to these formats used for non-tree-like inheritance relationships.

View Article: PubMed Central - PubMed

Affiliation: Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand. t.g.vaughan@massey.ac.nz

ABSTRACT
In this article, we present a versatile new software tool for the simulation and analysis of stochastic models of population phylodynamics and chemical kinetics. Models are specified via an expressive and human-readable XML format and can be used as the basis for generating either single population histories or large ensembles of such histories. Importantly, phylogenetic trees or networks can be generated alongside the histories they correspond to, enabling investigations into the interplay between genealogies and population dynamics. Summary statistics such as means and variances can be recorded in place of the full ensemble, allowing for a reduction in the amount of memory used--an important consideration for models including large numbers of individual subpopulations or demes. In the case of population size histories, the resulting simulation output is written to disk in the flexible JSON format, which is easily read into numerical analysis environments such as R for visualization or further processing. Simulated phylogenetic trees can be recorded using the standard Newick or NEXUS formats, with extensions to these formats used for non-tree-like inheritance relationships.

Show MeSH
Related in: MedlinePlus