DA.combi: Simultaneous estimation of Michaelis-Menten constant and...
In SIfEK: Statistical Inference for Enzyme Kinetics

Description Usage Arguments Details Value Examples

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.

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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)

`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

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 “burning” is set the length of initial iterations. The diffusion approximation method is used for construction of the likelihood.

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

## 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)

## End(Not run)