Biochemical systems can often be viewed as discrete-event systems, i.e., as systems that make stochastic state transitions at a strictly increasing sequence of random times. We survey a number of topics pertinent to modeling and simulation of such systems. We first describe several basic models for discrete-event systems, such as generalized semi-Markov processes, stochastic Petri nets, and continuous time Markov chains, and discuss the interplay between the choice of modeling formalism, the compactness of the model representation, and the computational complexity of the resulting simulation algorithm. We then outline a collection of techniques for increasing the efficiency of a simulation, as well as for efficiently estimating the sensitivity of a discrete-event system model with respect to one or more model parameters.