corr2D (version 1.0.3)

sim2ddata: Simulate kinetic data from two-step sequential first-order reactions

Description

sim2ddata simulates kinetic data for the sequential reaction A -> B -> C with the time constants k1 and k2.

Usage

sim2ddata(
  L = 400,
  t = 0:10,
  k1 = 0.2,
  k2 = 0.8,
  X = c(1000, 1400),
  A = c(1080, 1320),
  Aamp = c(3, 8),
  B = c(1120, 1280),
  Bamp = c(5, 15),
  C = c(1160, 1240),
  Camp = c(4, 9)
)

Value

sim2ddata returns a matrix containing the kinetic data. The matrix contains the sampled reaction times by rows and the spectral variables by columns. The reaction times are the row names while the spectral variables are saved as the column names. The matrix has the ideal format to be analyzed by corr2d.

Arguments

L

Positive, non-zero integer specifying how many spectral variables should be used to describe the kinetic dataset.

t

Numeric vector containing non-negative real numbers describing at which reaction times the kinetic data should be sampled.

k1, k2

Positive, non-zero real numbers describing the time constants used to simulate the reactions A -> B (k1) and B -> C (k2).

X

Numeric vector with two values specifying the range of the simulated spectral variables.

A, B, C

Numeric vector with two real values specifying the two signal positions of species A, B and C, respectively. It's the mean used in dnorm to simulate the signal. C and Camp may be NULL in which case only the reaction A -> B is simulated and sampled.

Aamp, Bamp, Camp

Numeric vector with two values specifying the signal width of species A, B and C, respectively. It's the standard deviation (sd) used in dnorm to simulate the signal. C and Camp may be NULL in which case only the reaction A -> B is simulated and sampled.

Details

The simulation assumes 2 spectral signals for each of the 3 species A, B and C. The sequential reaction is defined by 2 time constants k1 and k2. The spectral information can be sampled at every point during the reaction to get an arbitrary profile of the kinetic data. The signals of the three species are modeled by a normal distribution. In addition the spectral variable is assumed to be equidistant and the number of spectral variables can also be chosen arbitrary.

References

The default values are inspired by: I. Noda (2014) <DOI:10.1016/j.molstruc.2014.01.024>

Examples

    testdata <- sim2ddata()
    
    twodtest <- corr2d(testdata, corenumber = 1)
    
    plot_corr2d(twodtest)