SIfEK (version 0.1.0)

GP.cat: Estimation of single catalytic constant using Gaussian processes

Description

The function estimates single catalytic constant using single data set with an initial enzyme concentrations and substrate concentration. The Gaussian processes is utilized for the likelihood function.

Usage

GP.cat(method = T, RAM = F, time, dat, enz, subs, MM, catal,
  nrepeat = 10000, jump = 1, burning = 0, catal_m_v = c(1, 10000),
  sig, va)

Arguments

method

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

RAM

Robust Adaptive MCMC options (default = F)

time

total time of data

dat

observed dataset (trajectory column)

enz

enzyme concentrate

subs

substrate concentrate

MM

true value of MM constant

catal

initial value of catalytic constant

nrepeat

total number of iteration (default=10000)

jump

length of distance (default =1)

burning

length of burning period ( default =0)

catal_m_v

Catalytic prior gamma mean, variance(default=c(1,10000))

sig

standard deviation of univariate Normal proposal distribution

va

variance of dataset

Value

A vector of posterior samples of catalytic constant

Details

The function GP.cat generates a set of MCMC simulation samples from the conditional posterior distribution of catalytic constant of enzyme kinetics model. As the catalytic constant is only parameter to be estimated in the function the user should assign MM constant as well as initial enzyme concentration and substrate concentration. The prior information for the parameter can be given. The GP.cat function can select Robust Adaptive Metropolis (RAM) algorithm as well as Metropolis-Hastings algorithm with random walk chain for MCMC procedure. When <U+201C>RAM<U+201D> is assigned T then the function use RAM method and the <U+201C>sig<U+201D> is used as initial standard deviation of normal proposal distribution. When <U+201C>RAM<U+201D> is F, the function use Metropolis-Hastings algorithm with random walk chain and the <U+201C>sig<U+201D> can be set to controlled proper mixing and acceptance ratio of the parameter from the conditional posterior distribution. The <U+201C>va<U+201D> is the variance of the Gaussian process. 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 Gaussian process method is used for construction of the likelihood.

Examples

# NOT RUN {
data('Chymo_low')
time1=max(Chymo_low[,1])*1.01
sk_GPMH=GP.cat(method=TRUE,time=time1,dat=Chymo_low[,2],enz=4.4e+7,subs=4.4e+7
                      ,MM=4.4e+8,catal=0.05,nrepeat=10000,jump=1,burning=0
                      ,catal_m_v=c(1,10000),sig=0.016,va=var(Chymo_low[,2]))
# use RAM algorithm #
sk_GPRAM=GP.cat(method=TRUE,RAM=TRUE,time=time1,dat=Chymo_low[,2],enz=4.4e+7,subs=4.4e+7
                      ,MM=4.4e+8,catal=0.05,nrepeat=10000,jump=1,burning=0
                      ,catal_m_v=c(1,10000),sig=0.1,va=var(Chymo_low[,2]))
# }
# NOT RUN {
# }