Spurred by the rise of systems biology in the last decade and a half, network-based approaches have gained prominence as an efficient and insightful way to analyze complex biochemical reaction systems, such as MAPK signaling cascades and gene regulatory networks. Surprisingly, network-based methods are often able to make dynamical and steady state predictions independent of the initial conditions, rate parameters, and even rate form. In this talk, I will outline some recent applications of generalized network theory to biochemical reaction systems. In a generalized network, there are two networks with the same topological structure: one for the stoichiometry, and one for the kinetics. Examples of biochemical reaction systems with dynamically equivalent but better structured generalized networks will be presented.