ABSTRACT
The models and representations considered in the previous chapter provide a framework for thinking about the state of a biochemical network, the reactions that can take place, and the change in state that occurs as a result of particular chemical reactions. As yet, however, little has been said about which reactions are likely to occur or when. The state of a biochemical network evolves continuously through time with discrete changes in state occurring as the result of reaction events. These reaction events are random, governed by probabilistic laws. It is therefore necessary to have a fairly good background in probability theory in order to properly understand these processes. In a short text such as this, it is impossible to provide complete coverage of all of the necessary material. However, this chapter is meant to provide a quick summary of the essential concepts in a form that should be accessible to anyone with a high school mathematics education who has ever studied some basic probability and statistics. Readers with a strong background in probability will want to skip through this chapter. Note, however, that particular emphasis is placed on the properties of the exponential distribution, as these turn out to be central to understanding the various stochastic simulation algorithms that will be examined in detail in later chapters.
