Reacting systems away from the thermodynamic limit cannot be accurately modeled with ordinary differential equations. These continuous-deterministic modeling formalisms, traditionally developed and used by chemical engineers can be distinctly false if the number of molecules of reacting chemical species is very small, or if reaction events are very rare. Then stochastic-discrete representations are appropriate. Importantly, in cases where in a network of reactions there are some parts that must be modeled discretely and stochastically, yet others can be modeled continuously and deterministically, the need for development of multiscale models emerges naturally. In computational synthetic biology, such cases arise often. In this work we present the development of multiscae models for synthetic biology applications, demonstrating accuracy, computational efficiency and utility.