Several inten'clated methodological issues of this symposium are addressed, with the focus of formulating and quantifying kinetic models whose purpose is to answer particular quantitative questions about living systems. The unifying concept is kinetic experiment design, that is, decisions about what to do in the experimental laboratory to achieve this goal, and also how to do it “best.” The overriding factor in this endeavor is the paucity of “good-enough” data available from many types of biosystem studies. More often than not, the data are too few, too noisy, or both, and the number of output “ports” available to experimental probes are also too few. Optimal sampling schedule (OSS) design, based in information theory and asymptotic theory of mathematical statistics, represents one approach to optimizing the precision of model parameter estimates from small data sets. Some practical results of kinetic experiments perfomied with optimal sampling schedule designs are discussed, based on experiences in real endocrine and metabolic system studies in several animal species. Some potential pitfalls of this ap-proach are also discussed. A related problem is how to quantify unidentifiable models, i.e., models with “too many parameters”, more parameters than the number or variety of data available to quantify all or even some of them. Interval identifiahility and parameter interval analysis methods and results are reviewed, for several classes of multicompartmental models, because this is one approach to circumventing the unidentifiability problem, without the need for distorting or further abstracting relatively realistic model structures by reducing the dimensionality of their state variables or parameters. Finally, for models and systems with more than just a few pools, i.e., complex models, a case is made for using easier steady state experiment designs and models, rather than more complicated transient response studies, to reduce the dimensionality of the parameter estimation problem, rendering it more identifiable, or completely identifiable. Cut-set analysis, a graph theoretic approach, is used to help solve this problem. This is all done in the context of matching the model and the experiment design to the goals of a study, and an example is presented illustrating how a multidimensional system can be explored so that a 1-dimensional model fits the data best, and helps to answer some common questions in kinetic analysis.