Experimentalists face the dilemma of choosing between the accuracy and costs of an experiment.Optimization methods form the basic computational tools to address fundamental questions of optimal experimental design.Driven by its application,optimal experimental design leads to challenging Bayes risk minimization problems.We address challenges such as ill-posedness of the parameter estimation problem and large scales of ODE systems.We present a design framework for dynamical systems and illustrate its performance on biological models.