EKMCMC (version 0.1.0)

combined_est: Simulataneous estimation of Michaelis-Menten constant and catalytic constant using combined data

Description

The function estimates both catalytic and MM constant simultaneously using combined data sets for different enzyme concentrations and substrate concentrations for two input data sets according to the different values of enzyme concentrations or substrate concentration.

Usage

combined_est(method = T, time1, time2, species1, species2, enz1, enz2, subs1,
  subs2, MM, catal, tun = 2.4, std, nrepeat, jump = 1, burning = 0,
  catal_m = 1, catal_v = 1e+05, MM_m = 1, MM_v = 1e+05)

Arguments

method

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

time1

observed time interval for data1

time2

observed time interval for data2

species1

observed trajectory of product for data1

species2

observed trajectory of product for data2

enz1

enzyme concentration for data1

enz2

enzyme concentration for data2

subs1

substrate concentration for data1

subs2

substrate concentration for data2

MM

initial 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

nrepeat

total number of iteration

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 combined_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 concentrations, substrate concentrations. Because this function utilizes two data sets according to the different values of enzyme concentration or substrate concentration the user inputs two sets of information of input data set, enzyme concentration, and 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]
data("Chymo_high")
time2=Chymo_high[,1]
species2=Chymo_high[,2]
enz.Chymotrypsin<-combined_est(method=TRUE, time1=time1 ,time2=time2 ,species1=species1
                               ,species2=species2,enz1=4.4e+7,enz2=4.4e+9
                               ,subs1=4.4e+7,subs2=4.4e+7,MM=1e+9,catal=0.01,
                               tun=2.0,std=8e+7,nrepeat=1000,jump=10,burning=0
                               ,catal_m=1,catal_v=1e+6, MM_m=1,MM_v=1e+10)
# }