Table 3 Overview of the regressor- and response matrices used in the regression analyses and the test sets used for the three application examples

Gene regulatory networks

125 initial conditions for the three state variables X1, X2 and X3 (at time zero) in a 5³ FFD (dimensions: 125 × 3)

[D sin(D) cos(D)]

The concatenated time series for the state variables X1, X2 and X3 (Y_i = 3 × 300 time points,

i = 1 to nr. of observations)

33% of the observations in D (randomly chosen), and the corresponding Y-values

Mammalian circadian clock model

Nine model parameters in an OMBR design using eight levels for each parameter

(dimensions: 8192 × 9)

[D cross-terms of D D²]

16 state variable time series modelled separately (for each state variable Y_i = 200 time points,

i = 1 to nr. of observations)

8192 new parameter combinations generated by random Monte Carlo sampling (here the entire matrix D was used for calibration) and corresponding Y-values

Mouse ventricular myocyte model

Ten model parameters in a 3¹⁰ FFD

(dimensions: 59049 × 10)

[D cross-terms of D D²]*

35 state variable time series modelled separately (for each state variable Y_i = 200 time points,

i = 1 to nr. of observations)*

33% of the observations in D that did not fail (randomly chosen), and the corresponding Y-values