Designing Optimal Experiments to Discriminate Interaction Graph Models

Modern methods for the inference of cellular networks from experimental data often express nondeterminism by proposing an ensemble of candidate models with similar properties. To further discriminate among these model candidates, new experiments need to be carried out. Theoretically, the number of possible experiments is exponential in the number of possible perturbations. In praxis, experiments are expensive and usually there exist several constraints limiting which experiments can be performed. Limiting factors may exist on the combinations of perturbations that are technically possible, which components can be measured, and limitations on the number of affordable experiments. Further, not all experiments are equally well suited to discriminate model candidates. Therefore, the goal of optimal experiment design is to determine those experiments that discriminate most of the candidates while minimizing the costs. We present an approach for experiment planning with interaction graph models and sign consistency methods. This new approach can be used in combination with methods for network inference and consistency checking. The proposed method determines experiments which are most suitable to deliver results that reduce the number of candidate models. We applied our method to study the Erythropoietin signal transduction in human kidney cells HEK293. We first used simulated experiment data from an ODE model to demonstrate in silico that our experimental design results in the inference of the gold standard model. Finally, we used the approach to plan in vivo experiments that enabled us to discriminate model candidates for the Erythropoietin signal transduction in this cell line.