SIfEK (version 0.1.0)

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

Description

The function estimates both catalytic constant and Michaelis-Menten constant simultaneously using single data set with an initial enzyme concentrations and substrate concentration. The diffusion approximation is utilized for the likelihood function.

Usage

DA.multi(method = T, dat, enz, subs, MM, catal, nrepeat = 10000,
  jump = 1, burning, catal_m_v = c(1, 10000), MM_m_v = c(1, 10000),
  sig, scale_tun = 80)

Arguments

method

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

dat

observed dataset (time & trajectory columns)

enz

enzyme concentrate

subs

substrate concentrate

MM

initial 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))

MM_m_v

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

sig

variance of bivariate Normal proposal distribution

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.multi generates a set of MCMC simulation samples from the posterior distribution of catalytic constant and MM constant of enzyme kinetics model. As the function estimates both two constants the user should input the 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')
dou_DA=DA.multi(method=TRUE,dat=Chymo_low,enz=4.4e+7,subs=4.4e+7,MM=4.4e+8,catal=0.05
                ,nrepeat=10000,jump=1,burning=1,catal_m_v=c(1,1e+10),MM_m_v=c(1e+9,1e+18)
                ,sig=2.4*0.001*c(0.05,4.4e+8),scale_tun=80)
# }
# NOT RUN {
# }