Autocatalysis is a phenomenon in which a reaction or sequence of reactions produces a compound catalyzed by one or more products of the reaction(s) in a system (e.g., ). Kinetically, autocatalysis leads to super-linear growth of a species or network. However, as discussed in 103 for a CRN, computationally identifying whether a compound produces more than one copy of itself is not straightforward. Although formalized definitions of autocatalysis that can be generally applied to CRNs remains to be defined, we here searched for topologically formal autocatalytic motifs, i.e., for those in which a cyclic set of reactions produces two or more copies of one of the cycle component compounds, as inferred from the network topology. We used an imperative programming approach to identify autocatalytic cycles in which a graph algorithm was written to find motifs, using the Ford-Fulkerson algorithm 104 . A data loader routine was written in Python to import the MØD-generated chemical network data into a graph query database, Neo4j so that pattern match queries could be built. The loader routine ensured that no distinct molecule or reaction node was repeated, which gave a high-level representation on the molecule and reaction node (bipartite, or two-label graph per Neo4j) graph. Ensuring node distinctness allowed the network model to be lighter to improve computational performance since the pathways were being examined, and not the raw quantity of molecules in the network. The network model could then be thought of as a model of every distinct pathway extracted from the simulated chemical network from MØD (whose network model is physically closer to the actual chemical network representation, since this model is a level of abstraction on the MØD network). As the database populates, additional information, such as when the molecule was first generated, is stored in the .txt files. If a molecule is found again in a subsequent generation, that molecule's node is not re-recorded. Similarly, if a reaction of a certain rule already exists between molecules, that edge is not repeated. Based on the rules of the loader script, the Neo4j graph model is a species-only graph, rather than the bipartite molecule and reaction graph. The bipartite graph is useful for describing complex patterns that require constraints on the relationships between reactions and molecules, while the species-only graph is useful for network centrality measures.