Robust sampling time design for biochemical systems

Abstract Optimal sampling time design by considering parameter uncertainties has rarely been considered in published research. In this work, the robust experimental design (RED) for sampling time selection is investigated. The aim is to exploit the sampling strategy using which the experiment can provide the most informative data for improving parameter estimation quality. With an enzyme reaction case study system, two global sensitivity analysis (GSA) approaches, the Morris screening method and the Sobol’s method, are firstly applied to find out the key parameters that have large influences to model outputs of interest. Then three different RED methods, the worst-case strategy, the Bayesian design, and the GSA-based approach, are developed to design the optimal sampling time schedule. Simulation results suggest that, among the three RED methods, the equally spaced sampling from the Bayesian design has the best robustness towards parameter uncertainties.

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