  Wet-lab experiments, in which the dynamics within living cells are observed,
are usually costly and time consuming. This is particularly true if single-cell
measurements are obtained using experimental techniques such as flow-cytometry
or fluorescence microscopy. It is therefore important to optimize experiments
with respect to the information they provide about the system. In this paper we
make a priori predictions of the amount of information that can be obtained
from measurements. We focus on the case where the measurements are made to
estimate parameters of a stochastic model of the underlying biochemical
reactions. We propose a numerical scheme to approximate the Fisher information
of future experiments at different observation time points and determine
optimal observation time points. To illustrate the usefulness of our approach,
we apply our method to two interesting case studies.
