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Graph transformation approaches have been successfully used to analyse anddesign chemical and biological systems. Here we build on top of a DPOframework, in which molecules are modelled as typed attributed graphs andchemical reactions are modelled as graph transformations. Edges and vertexescan be labelled with first-order terms, which can be used to encode, e.g.,steric information of molecules. While targeted to chemical settings, thecomputational framework is intended to be very generic and applicable to theexploration of arbitrary spaces derived via iterative application of rewriterules, such as process calculi like Milner's {\pi}-calculus. To illustrate thegenerality of the framework, we introduce EpiM: a tool for computing executionspaces of {\pi}-calculus processes. EpiM encodes {\pi}-calculus processes astyped attributed graphs and then exploits the existing DPO framework to computetheir dynamics in the form of graphs where nodes are {\pi}-calculus processesand edges are reduction steps. EpiM takes advantage of the graph-basedrepresentation and facilities offered by the framework, like efficientisomorphism checking to prune the space without resorting to explicitstructural equivalences. EpiM is available as an online Python-based tool.

A Graph-Based Tool to Embed the {\pi}-Calculus into a Computational DPO Framework