Approximate model reductions for combinatorial reaction systems

The paper considers a model reduction technique that is well-suited for biochemical reaction systems giving rise to the assembly of a large number of different molecular species. The reduction is performed by grouping species with common properties, directly from the model specification in terms of a rule-based language. In recent works, general algorithms for the exact reductions of rule-based models were established, but the state space often remains combinatorial. We extend this line of research by introducing approximate reductions, and an error measure which allows us to quantitatively study the effect of approximate model reductions.