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

In SIfEK: Statistical Inference for Enzyme Kinetics

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)

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

Value

A vector of posterior samples of Michaelis-Menten constant

Examples

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)

Example output

MCMC simulation summary 
Posterior mean:        4.47e+08 
Posterior sd:          5.304e+07 
Credible interval(l):  3.556e+08 
Credible interval(U):  5.644e+08 
Relative CV:           1.187e-06 
Acceptance ratio:      4.3e-01 

SIfEK documentation built on May 1, 2019, 9:11 p.m.