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

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"

Value

A matrix containing posterior samples of the estimated parameters: the catalytic constant and 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_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")

## End(Not run)

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

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)

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

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)

Main function for estimating catalytic constant k_cat and Michaelis-Menten (MM) constant K_M

Description

The function estimates either the catalytic constant, the Michaelis-Menten constant, or both simultaneously using progress-curve data, initial enzyme concentrations, and initial substrate concentrations.

Usage

main_est(
  method = TRUE,
  timeseries,
  enz,
  subs,
  K_M = FALSE,
  catal = FALSE,
  K_M_init = FALSE,
  std = FALSE,
  tun = 2.4,
  nrepeat = 1000,
  jump = 10,
  burn = 1000,
  catal_m = 1,
  catal_v = 1e+06,
  K_M_m = FALSE,
  K_M_v = FALSE,
  volume = FALSE,
  t_unit,
  c_unit
)

Arguments

Details

The function main_est generates a set of Markov Chain Monte Carlo (MCMC) simulation samples from the posterior distribution of the catalytic constant or (and) the Michaelis-Menten constant of enzyme kinetics model. Users should input initial enzyme concentrations, substrate concentrations, and progress-curve data. Prior information for both parameters can be given. The Gibbs sampling and Metropolis Hastings algorithms are used to sample the parameters. Parameters for the MCMC such as tuning parameter for proposal distribution, prior parameters, and the iteration number can be specified by users. This function use one of catalytic_est(), MM_est(), MM_catal_est() to generate the samples depending on parameter(s) to be estimated.

Value

A vector (or matrix) containing posterior samples of the estimated parameter(s).

Examples

## Not run: 
data("timeseries_data_example")
result <- main_est(method=TRUE, timeseries = timeseries_data_example, 
enz = c(4.4, 4.4, 440, 440), subs=c(4.4, 4.4, 4.4, 4.4), K_M_init = 1e+1, 
std=1e+1, tun = 3.5, jump=10, burn=1000, nrepeat=1000,
catal_m=1, catal_v=100, K_M_m=1, K_M_v=1e+4, volume = FALSE, 
t_unit = "sec", c_unit = "mM")

## End(Not run)

Product concentration of 101 observed time with different initial conditions

Description

An artificial data set containing the product concentration observed with the high and low enzyme concentrations. The 1st, 3rd, 5th, and 7th columns are observed times, and the 2nd, 4th, 6th, and 8th columns are product concentrations. The 2nd and 4th columns are observed with the initial enzyme concentrations of 4.4, and the 6th and 8th columns are observed with the initial enzyme concentrations of 440. The initial substrate concentrations are 4.4 for all data.

Usage

timeseries_data_example

Format

A data frame with 101 rows and 8 variables:

V1 V3 V5 V7

observed times, no unit

V2 V4 V6 V8

product concentrations, no unit