Specifying informative experiment stimulation conditions for resolving dynamical uncertainty in biological systems

A computationally efficient model-based design of experiments (MBDOE) strategy is developed to plan an optimal experiment by specifying the experimental stimulation magnitudes and measurement points. The strategy is extended from previous work which optimized the experimental design over a space of measurable species and time points. We include system inputs (stimulation conditions) in the experiment design search to investigate if the addition of perturbations enhances the ability of the MBDOE method to resolve uncertainties in system dynamics. The MBDOE problem is made computationally tractable by using a sparse-grid approximation of the model output dynamics, pre-specifying the time points at which the input or experimental perturbations can be applied, and creating scenario trees to explore the endogenous uncertainty. Consecutive scenario trees are used to determine the best input magnitudes and select the optimal associated measurement species and time points. We demonstrate the effectiveness of this strategy on a T-Cell Receptor (TCR) signaling pathway model.