We now have a grounding in elementary probability theory and an understanding of stochastic simulation. The only remaining theory required before studying the dynamics of genetic and biochemical networks (and chemical kinetics more generally) is an introduction to the theory of stochastic processes. A stochastic process is a random variable (say, the state of a biochemical network) which evolves through time. The state may be continuous or discrete, and it can evolve through time in a discrete or continuous way. A Markov process is a stochastic process which possesses the property that the future behaviour depends only on the current state of the system. Put another way, given information about the ...
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