EKMCMC (version 1.1.2)

MM_catal_est: Function for estimating both of the Michaelis-Menten constant and catalytic constant simultaneously

Description

The function estimates both of the catalytic and the Michaelis-Meten constants simultaneously using progress-curve data, enzyme concentrations, and substrate concentrations.

Usage

MM_catal_est(
  method,
  timespan,
  products,
  enz,
  subs,
  K_M_init,
  std,
  tun,
  nrepeat,
  jump,
  burn,
  catal_m,
  catal_v,
  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

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

tunning 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.

catal_m

prior mean of gamma prior for the catalytic constant k_cat.

catal_v

prior variance of gamma prior for the catalytic constant k_cat.

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 matrix containing posterior samples of the estimated parameters: the catalytic constant and the Michaelis-Menten constant.

Details

The function MM_catal_est generates a set of Markov Chain Monte Carlo simulation samples from the posterior distribution of K_M and catalytic constant of enzyme kinetics model. Authors' recommendation: "Do not use this function directly. Do use the function main_est() to estimate the parameters 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_catal_result <- MM_catal_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), 
K_M_init = 1, catal_m=1, catal_v = 1000, 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")
# }