SIfEK (version 0.1.0)

SSA.MM: Estimation of single Michaelis-Menten constant using the stochastic simulation approximation

Description

The function estimates single Michaelis-Menten constant using the likelihood function with the stochastic simulation approximation method.

Usage

SSA.MM(method = T, time, species, enz, subs, MM, catal, tun = 2.4, std,
  nrepeat, jump = 1, burning = 0, MM_m = 1, MM_v = 1e+06)

Arguments

method

method selection: T=TQ model, F=SQ model(default = T)

time

observed time interval

species

observed trajectory of product

enz

enzyme concentration

subs

substrate concentration

MM

initial value of MM constant

catal

true value of catalytic constant

tun

tunning constant of MH algorithm (default=2.4)

std

standard deviation of proposal distribution (if =0, caclulated by Opt. function)

nrepeat

total number of iteration (default=10000)

jump

length of distance (default =1)

burning

lenth of burning period (default =0)

MM_m

prior mean of gamma prior (default =1)

MM_v

prior variance of gamma prior (default =10000)

Value

A vector of posterior samples of Michaelis-Menten constant

Details

The function SSA.MM generates a set of MCMC simulation samples from the conditional posterior distribution of Michaelis-Menten constant of enzyme kinetics model. As the MM constant is only parameter to be estimated in the function the user should assign catalytic constant as well as initial enzyme concentration and substrate concentration. The prior information for the parameter can be given. The turning constant (scale_tun) and standard deviation of proposal normal distribution (sig) can be set to controlled proper mixing and acceptance ratio of the parameter from the conditional posterior distribution. The posterior samples are only stored with fixed interval according to set "jump" to reduce serial correlation. The initial iterations are removed for convergence. The <U+201C>burning<U+201D> is set the length of initial iterations. The diffusion approximation method is used for construction of the likelihood.

Examples

# NOT RUN {
data("Chymo_low")
time1=Chymo_low[,1]
species1=Chymo_low[,2]
 Chymotrypsin.mm<-SSA.MM(method=TRUE,time=time1,species=species1,enz=4.4e+7,subs=4.4e+7
 ,MM=10000,catal=0.051,tun=2.4,std=8e+7 ,nrepeat=10000,jump=1
 ,burning=0,MM_m=1,MM_v=1e+10)
# }