Here, we developed a method that integrates the representation of CRNs as graphs with the application of network measures to model synthetically-designed CRNs (Fig. ). Briefly, any chemical system can be described by a set of elementary reactions but to create insights into the system, chemists typically use conceptual models to visualize the interactions as positive and a negative feedback loops (Fig. , left panel). To illustrate, Supplementary Information, Fig. shows a previously developed enzymatically-driven CRN for which a mass-action model was developed. Building on this foundation, we designed a procedure (illustrated in Fig. as steps I-IV) that could translate the reactions in this model into a species-species network comprising of nodes and edges. This procedure not only allows for an explicit illustration of the relationships among the nodes, but also for the application of network measures commonly employed in network science. We show that measures for clustering and centrality could reveal the underlying network statistics important for the temporal existence of feedback loops. To the best of our knowledge, this work is the first combination of abstract network methods with an experimentally verified CRN.