  Scientists are attempting to use models of ever increasing complexity,
especially in medicine, where gene-based diseases such as cancer require better
modeling of cell regulation. Complex models suffer from uncertainty and
experiments are needed to reduce this uncertainty. Because experiments can be
costly and time-consuming it is desirable to determine experiments providing
the most useful information. If a sequence of experiments is to be performed,
experimental design is needed to determine the order. A classical approach is
to maximally reduce the overall uncertainty in the model, meaning maximal
entropy reduction. A recently proposed method takes into account both model
uncertainty and the translational objective, for instance, optimal structural
intervention in gene regulatory networks, where the aim is to alter the
regulatory logic to maximally reduce the long-run likelihood of being in a
cancerous state. The mean objective cost of uncertainty (MOCU) quantifies
uncertainty based on the degree to which model uncertainty affects the
objective. Experimental design involves choosing the experiment that yields the
greatest reduction in MOCU. This paper introduces finite-horizon dynamic
programming for MOCU-based sequential experimental design and compares it to
the greedy approach, which selects one experiment at a time without
consideration of the full horizon of experiments. A salient aspect of the paper
is that it demonstrates the advantage of MOCU-based design over the widely used
entropy-based design for both greedy and dynamic-programming strategies and
investigates the effect of model conditions on the comparative performances.
