EKMCMC (version 0.1.0)

MM_catal_est: Simulataneous estimation of Michaelis-Menten constant and catalytic constant

Description

The function estimates both catalytic and MM constant simultaneously with given enzyme concentration and substrate concentration.

Usage

MM_catal_est(method = T, time, species, enz, subs, MM, catal, tun = 2.4,
  std, nrepeat, jump = 1, burning = 0, catal_m = 1, catal_v = 10000,
  MM_m = 1, MM_v = 10000)

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

true value of MM constant

catal

initial 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)

catal_m

prior mean of gamma prior (default =1)

catal_v

prior variance of gamma prior (default =10000)

MM_m

prior mean of gamma prior (default =1)

MM_v

prior variance of gamma prior (default =10000)

Value

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

Details

The function MM_catal_est generates a set of Markov Chain Monte Carlo simulation samples from the posterior distribution of MM and catalytic constant of enzyme kinetics model. Because the function considers both MM constant and catalytic constant as parameters to be estimated, the user should input constants of enzyme concentration, substrate concentration. prior information for both two parameter can be given. The turning constant and standard deviation can be set to controlled proper mixing and acceptance ratio of MM constant from it's conditional posterior distribution. 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.

Examples

# NOT RUN {
data("Chymo_low")
time1=Chymo_low[,1]
species1=Chymo_low[,2]
Chymotrypsin.low<-MM_catal_est(method=TRUE, time=time1,species=species1,enz=4.4e+7
,subs=4.4e+7,MM=1e+9,catal=0.01,tun=2.4,std=8e+7,nrepeat=1000,jump=10,
burning=0,catal_m=1,catal_v=1e+10, MM_m=1e+9,MM_v=1e+18)
# }