Towards experimental design using a Bayesian framework for parameter identification in dynamic intracellular network models

Biological measurements of intracellular regulation processes are typically noisy, and time resolution is low. In practice often only steady state measurements of perturbation experiments are available. Since data acquisition is expensive, a framework for experimental design that allows the inclusion of prior knowledge and takes uncertainty into account is highly desirable. We introduce a framework for the experimental design problem to infer parameters from steady state observations of intracellular networks. Our network model consists of (nonlinear) ordinary differential equations based on chemical reaction kinetics. We consider sets of structural perturbation experiments, that is, steady state measurements of the system subject to gene knockout or mutations. The model is stochastically embedded by introducing Gaussian measurement errors. This allows the application of a statistical Bayesian framework and usage of information-theoretic measures for experimental design. We propose to choose the optimal experiments with respect to identifiability of model parameters by maximizing the information content of the expected outcome, measured as the entropy of the posterior distributions. In this setting the posterior has no closed form and an analysis requires efficient sampling methods. We introduce a simulation-based experimental design framework for the identification of network parameters with an efficient entropy estimation approach. First results are shown on a network model for secretory pathway control. Secretion of proteins from cells involves the budding of vesicles at the Golgi. For this process PKD activity is central