Generalization of natural computing models: variants of fusion grammars and reaction systems over categories

This dissertation contributes to theoretical computer science by combining and developing the two research areas of natural computing and graph transformation.
Natural computing covers information processing and computational models based on systems of nature.
Graph transformation deals with rule-based manipulation of graphs (or objects of related categories).
In this thesis, two natural computing models are generalized using graph-transformation and category theory.

The first part of the thesis covers variants of fusion grammars, which are a novel approach to hypergraph language generation.
The second part covers reaction systems over categories, generalizing set- and graph-based reaction systems.