These, and other similar experiments, point to the flaws in using pure experimental techniques to address complex issues of immune regulation and development. There is now an increasing need to understand how the immune system functions as a whole in order to be able to better understand the development of diseases such as autoimmunity, allergy and cancer, and to design effective strategies to treat them. An important concept is that the immune response evolves rapidly in time and its interactions are all highly regulated. The only real way to study the whole integrated system is through the use of mathematical modeling. There has been relatively little interest, on the part of experimental immunologists, in this area, probably due to the complexity of the mathematics necessary for this work and the dearth of mathematicians sufficiently cognizant of the immune system to make such models useful.
Mathematical models can serve several distinct purposes.
They can be used to analyze experimental results and provide predictions and suggestions for follow-up experiments, or they can attempt to synthesize existing knowledge and provide a theoretical framework for the interpretation of existing paradigms. Both types of model are useful to the experimental immunologist, and the ultimate merit of particular models depends on the specific questions they are designed to answer. The more assumptions that have to be put into the model, the harder it is to be confident about the conclusions. On the other hand, a well designed model can test different assumptions and provide important new insights into questions that cannot be readily answered experimentally. As such, this review will focus on aspects of modeling that appear, to this writer, to have contributed significantly to the understanding of the immune system.