Using a Bayesian Posterior Density in the Design of Perturbation Experiments for Network Reconstruction

Abstract

Gene perturbation experiments are commonly used in the reconstruction of gene regulatory networks. Because such experiments are often difficult to perform, it is important to predict on a rational basis those experiments likely to result in the greatest resolution of model uncertainty. When a method for constructing Bayesian posterior densities on the space of network models is available, this provides a means with which to estimate the expected reduction in entropy that would result from a given perturbation experiment. We define an algorithm for selecting perturbation experiments based on this idea, and demonstrate it using a simulation study using a Bayesian network model.