MM_est {EKMCMC} | R Documentation |
Function for estimating the Michaelis-Menten constant
Description
The function estimates the Michaelis-Menten constant using progress-curve data, enzyme concentrations, substrate concentrations, and the catalytic constant.
Usage
MM_est(
method,
timespan,
products,
enz,
subs,
catal,
K_M_init,
std,
tun,
nrepeat,
jump,
burn,
K_M_m,
K_M_v,
volume,
t_unit,
c_unit
)
Arguments
method |
This determines which model, the sQSSA or tQSSA model, is used for the estimation. Specifically, the input for method is TRUE (FALSE); then the tQSSA (sQSSA) model is used. |
timespan |
time points when the concentrations of products were measured. |
products |
measured concentrations of products |
enz |
initial enzyme concentrations |
subs |
initial substrate concentrations |
catal |
true value of the catalytic constant. |
K_M_init |
initial value of K_M constant for the Metropolis-Hastings algorithm. If the input is FALSE then it is determined by max(subs). |
std |
standard deviation of proposal distribution. If the input is FALSE then it is determined by using the hessian of log posterior distribution. |
tun |
tuning constant for the Metropolis-Hastings algorithm when std is FALSE (i.e., hessian of the log posterior distribution is used). |
nrepeat |
number of effective iteration, i.e., posterior samples. |
jump |
length of distance between sampling, i.e., thinning rate. |
burn |
length of burn-in period. |
K_M_m |
prior mean of gamma prior for the Michaelis-Menten constant K_M. If the input is FALSE then it is determined by max(subs). |
K_M_v |
prior variance of gamma prior for the Michaelis-Menten constant K_M. If the input is FALSE then it is determined by max(subs)^2*1000. |
volume |
the volume of a system. It is used to scale the product concentration. FALSE input provides automatic scaling. |
t_unit |
the unit of time points. It can be an arbitrary string. |
c_unit |
the unit of concentrations. It can be an arbitrary string. |
Details
The function MM_est generates a set of Markov Chain Monte Carlo simulation samples from posterior distribution of the Michaelis-Menten constant of enzyme kinetics model. Because the function estimates only the Michaelis-Menten constant the true value of the catalytic constant should be given. Authors' recommendation: "Do not use this function directly. Do use the function main_est() to estimate the parameter so that the main function calls this function"
Value
A vector containing posterior samples of the estimated parameter: the Michaelis-Menten constant.
Examples
## Not run:
data("timeseries_data_example")
timespan1=timeseries_data_example[,c(1,3,5,7)]
products1=timeseries_data_example[,c(2,4,6,8)]
MM_result <- MM_est(method=TRUE,timespan=timespan1,products=products1,
enz = c(4.4, 4.4, 440, 440), subs=c(4.4, 4.4, 4.4, 4.4), catal = 0.051,
K_M_init = 1, K_M_m = 1, K_M_v = 100000, std = 10, tun =3.5,
nrepeat = 1000, jump = 10, burn = 1000, volume = FALSE,
t_unit = "sec", c_unit = "mM")
## End(Not run)