catalytic_est {EKMCMC}

Function for estimating the catalytic constant

Description

The function estimates catalytic constant using progress-curve data, enzyme concentrations, substrate concentrations, and the Michaelis-Meten constant.

Usage

catalytic_est(
  method,
  timespan,
  products,
  enz,
  subs,
  K_M,
  catal_m,
  catal_v,
  nrepeat,
  jump,
  burn,
  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

true value of the Michaelis-Menten constant.

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.

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.

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 catalytic_est generates a set of Monte Carlo simulation samples from posterior distribution of the catalytic constant of enzyme kinetics model. Because the function estimates only the catalytic constant, the true value of the Michaelis-Menten 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 catalytic 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)]
catalytic_result <- catalytic_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=44, catal_m = 1, catal_v = 1000, jump = 10, burn = 1000, nrepeat = 1000, 
volume = FALSE, t_unit = "sec", c_unit = "mM")

## End(Not run)