The logistic growth model is given by dN/dt = rN(1-N/K) where N is the number (density) of indviduals at time t, K is the carrying capacity of the population, r is the intrinsic growth rate of the population. We assume r=b-d where b is the per capita p.c. birth rate and d is the p.c. death rate.
This model consists of two reaction channels,
N ---b---> N + N
N ---d'---> 0
where d'=d+(b-d)N/K. The propensity functions are a_1=bN and a_2=d'N.
Load package
library(GillespieSSA)Define parameters
parms <- c(b = 2, d = 1, K = 1000) # Parameters
tf <- 10 # Final time
simName <- "Logistic growth" Define initial state vector
x0 <- c(N = 500)Define state-change matrix
nu <- matrix(c(+1, -1),ncol = 2)Define propensity functions
a <- c("b*N", "(d+(b-d)*N/K)*N")Run simulations with the Direct method
set.seed(1)
out <- ssa(
x0 = x0,
a = a,
nu = nu,
parms = parms,
tf = tf,
method = ssa.d(),
simName = simName,
verbose = FALSE,
consoleInterval = 1
)
ssa.plot(out, show.title = TRUE, show.legend = FALSE)Run simulations with the Explict tau-leap method
set.seed(1)
out <- ssa(
x0 = x0,
a = a,
nu = nu,
parms = parms,
tf = tf,
method = ssa.etl(tau = .03),
simName = simName,
verbose = FALSE,
consoleInterval = 1
)
ssa.plot(out, show.title = TRUE, show.legend = FALSE)Run simulations with the Binomial tau-leap method
set.seed(1)
out <- ssa(
x0 = x0,
a = a,
nu = nu,
parms = parms,
tf = tf,
method = ssa.btl(f = 5),
simName = simName,
verbose = FALSE,
consoleInterval = 1
)
ssa.plot(out, show.title = TRUE, show.legend = FALSE)Run simulations with the Optimized tau-leap method
set.seed(1)
out <- ssa(
x0 = x0,
a = a,
nu = nu,
parms = parms,
tf = tf,
method = ssa.otl(),
simName = simName,
verbose = FALSE,
consoleInterval = 1
)
ssa.plot(out, show.title = TRUE, show.legend = FALSE)