Before revision:  The simpleststochastic models of such networks treat the system as a continuous time Markov chain with the state being the number of molecules of each species and with reactions modeled as possible transitions of the chain. For such models , there is typically a wide variation in temporal and other quantitative scales. In this multi-scale setting it is typically an extremely difficult task to perform approximations, such as Langevin approximations or law of large number type arguments, to simplify a system. Therefore, numerical methodsoftentimes are the only reasonable means by which such models can be understood in real time .
After revision:  The simpleststochastic models of such networks treat the system as a continuous time Markov chains. Simulation of such models is often relatively straightforward as there are easily implementable methods for the generation of exact sample paths. However, when using ensemble averages to approximate expected values, the computational complexity can become prohibitive as the number of computations per path scales linearly with the number of jumps, or reactions, of the process. When such methods become computationally intractable, approximate methods, which introduce a bias, can become advantageous .