Nothing
set.seed(1) knitr::opts_chunk$set(fig.width = 8, fig.height = 6)
The Linear Chain System consists of M chain reactions with M+1 species as follows:
S_1 --c1--> S_2 S_2 --c2--> S_3 ... S_M --cM--> S_(M+1)
Load package
library(GillespieSSA)
Define parameters
parms <- c(c = 1) # Rate parameter M <- 50 # Number of chain reactions simName <- "Linear Chain System" # Simulation name tf <- 5 # Final time
Define initial state vector
x0 <- c(1000, rep(0, M)) names(x0) <- paste0("x", seq_len(M+1))
Define state-change matrix
nu <- matrix(rep(0, M * (M+1)), ncol = M) nu[cbind(seq_len(M), seq_len(M))] <- -1 nu[cbind(seq_len(M)+1, seq_len(M))] <- 1
Define propensity functions
a <- paste0("c*x", seq_len(M))
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 = .1), 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 = 50), 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)
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