Automated transformation between modeling languages with different expressiveness: challenges and results from a use case with SBML and ML-Rules

Automated transformation between modeling languages is often useful, e.g., to make tools (like simulators) based on one language applicable to models defined in other languages. However, several problems arise when the expressive powers of the modeling languages differ. We consider the automated transformation between models specified in the systems biology markup language (SBML) and ML-Rules, a rule-based multilevel modeling language. While both languages allow for modeling aspects that cannot be expressed in its counterpart and thus prevent a complete and fully automated transformation, it is still possible to transform many useful classes of models. Even more models can be transformed by relying on certain heuristics or user input.