SIfEK (version 0.1.0)

DA.combi: Simultaneous estimation of Michaelis-Menten constant and catalytic constant using combined data and the likelihood function with the diffusion approximation method.

Description

The function estimates both catalytic constant and Michaelis-Menten constant simultaneously using combined data sets with different enzyme concentrations or substrate concentrations. The diffusion approximation is utilized for the likelihood function.

Usage

DA.combi(method = T, dat1, dat2, enz, subs, MM, catal, sig,
  nrepeat = 11000, jump = 1, burning = 1000, catal_m_v = c(1,
  10000), MM_m_v = c(1, 10000), scale_tun = 80)

Arguments

method

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

dat1

observed dataset1 ( time & trajectory columns)

dat2

observed dataset2 ( time & trajectory columns)

enz

enzyme concentrate

subs

substrate concentrate

MM

initial value of MM constant

catal

initial value of catalytic constant

sig

variance of bivariate Normal proposal distribution

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))

MM_m_v

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

scale_tun

scale tunning constant for stochastic simulation

Value

A n*2 matrix of postrior samples of catalytic constant and MM constant

Details

The function DA.combi generates a set of MCMC simulation samples from the posterior distribution of catalytic constant and MM constant of enzyme kinetics model. As the function uses combined data set with different initial concentration of enzyme or substrate concentration the user should input two values of enzyme and substrate initial concentration. The prior information for both two parameters can be given. The turning constant (scale_tun) and variances for two constants (sig) can be set to controlled proper mixing and acceptance ratio for updating two parameters simultaneously. 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')
data('Chymo_high')
comb_DA=DA.combi(method=TRUE,dat1=Chymo_low,dat2=Chymo_high,enz=c(4.4e+7,4.4e+9)
               ,subs=c(4.4e+7,4.4e+7),MM=4.4e+8,catal=0.05,sig=2.0*(c(0.005,8e+7))^2
               ,nrepeat=10000,jump=1,burning=0,catal_m_v = c(1, 1e+6)
               ,MM_m_v = c(1, 1e+10),scale_tun=100)
# }
# NOT RUN {
# }