ABSTRACT
It is quite obvious by now that the quality of the parameter estimates that have been obtained with any of the previously described techniques ultimately depends on "how good" the data at hand is. It is thus very important, when we do have the option, to design our experiments in such a way so that the information content of the data is the highest possible. Generally speaking, there are two approaches to experimental design: (1) factorial design and (2) sequential design. In sequential experimental design we attempt to satisfy one of the following two objectives: (i) estimate the parameters as accurately as possible or (ii) discriminate among several rival models and select the best one. In this chapter we briefly discuss the design of preliminary experiments (factorial designs), and then we focus our attention on the sequential experimental design where we examine algebraic and ODE models separately.
