Making sense of complex phenomena in biology.

The remarkable advances in biotechnology over the past two decades have resulted in the generation of a huge amount of experimental data. It is now recognized that, in many cases, to extract information from this data requires the development of computational models. Models can help gain insight on various mechanisms and can be used to process outcomes of complex biological interactions. To do the latter, models must become increasingly complex and, in many cases, they also become mathematically intractable. With the vast increase in computing power these models can now be numerically solved and can be made more and more sophisticated. A number of models can now successfully reproduce detailed observed biological phenomena and make important testable predictions. This naturally raises the question of what we mean by understanding a phenomenon by modelling it computationally. This paper briefly considers some selected examples of how simple mathematical models have provided deep insights into complicated chemical and biological phenomena and addresses the issue of what role, if any, mathematics has to play in computational biology