User:James Chappell /R RK4 Modeling: Difference between revisions
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The RK4 tool is a tool that can be used to model ordinary differential equations (ODE). Like other ODE solvers you can output the results of a simulation into graph or tables. | The RK4 tool is a tool that can be used to model ordinary differential equations (ODE). Like other ODE solvers you can output the results of a simulation into graph or tables. | ||
'''Example 1 : Simple Constitutive Expression''' | |||
constitutiveexpression <- function(t, x, parms) { | |||
Define the name of the model | |||
mRNA <- x[1] | |||
protein <- x[2] | |||
Name the response variables – these are the outputs of our model | |||
with(as.list(parms),{ | |||
dmRNA <- k1 - d1*mRNA | |||
dprotein <- k2*mRNA - d2*protein | |||
res<-c(dmRNA, dprotein) | |||
list(res) | |||
})} | |||
Combine these vectors into a list called res | |||
times <-seq(0, 1000, length=1001) | |||
parms <-c(k1=0.69 , k2=f ,d1=0.23 ,d2=0.012) | |||
Generate a time series over which to solve the equations (1000 in steps of 1) | |||
Set the parameter values in parms | |||
y <- xstart <-c (mRNA=1, protein=1) | |||
Set the starting value for response variables | |||
output <- as.data.frame(rk4(xstart, times, constitutiveexpression, parms)) | |||
plot(output$time, output$protein, ylim=c(0,2000), ylab="protein (molecules)",xlab="time (min)",type="n") | |||
} | |||
Revision as of 08:11, 8 July 2009
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James Chappell
Imperial College London Synthetic Biology Lab
RK4
The RK4 tool is a tool that can be used to model ordinary differential equations (ODE). Like other ODE solvers you can output the results of a simulation into graph or tables.
Example 1 : Simple Constitutive Expression
constitutiveexpression <- function(t, x, parms) {
Define the name of the model
mRNA <- x[1]
protein <- x[2]
Name the response variables – these are the outputs of our model
with(as.list(parms),{
dmRNA <- k1 - d1*mRNA
dprotein <- k2*mRNA - d2*protein
res<-c(dmRNA, dprotein)
list(res)
})}
Combine these vectors into a list called res
times <-seq(0, 1000, length=1001)
parms <-c(k1=0.69 , k2=f ,d1=0.23 ,d2=0.012)
Generate a time series over which to solve the equations (1000 in steps of 1)
Set the parameter values in parms
y <- xstart <-c (mRNA=1, protein=1)
Set the starting value for response variables
output <- as.data.frame(rk4(xstart, times, constitutiveexpression, parms))
plot(output$time, output$protein, ylim=c(0,2000), ylab="protein (molecules)",xlab="time (min)",type="n") }
