  For biological experiments aiming at calibrating models with unknown
parameters, a good experimental design is crucial, especially for those subject
to various constraints, such as financial limitations, time consumption and
physical practicability. In this paper, we discuss a sequential experimental
design based on information theory for parameter estimation and apply it to two
biological systems. Two specific issues are addressed in the proposed
applications, namely the determination of the optimal sampling time and the
optimal choice of observable. The optimal design, either sampling time or
observable, is achieved by an information-theoretic sensitivity analysis. It is
shown that this is equivalent with maximizing the mutual information and
contrasted with non-adaptive designs, this information theoretic strategy
provides the fastest reduction of uncertainty.

Applications;Quantitative Methods