EKMCMC (version 1.1.2)

MM_est: 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.

Value

A vector containing posterior samples of the estimated parameter: the Michaelis-Menten constant.

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"

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")
# }