An Approximate Execution of Rule-Based Multi-level Models

In cell biology, models increasingly capture dynamics at different organizational levels. Therefore, new modeling languages are developed, e. g., like ML-Rules, that allow a compact and concise description of these models. However, the more complex models become the more important is an efficient execution of these models. tau-leaping algorithms can speed up the execution of biochemical reaction models significantly by introducing acceptable inaccurate results. Whereas those approximate algorithms appear particularly promising to be applied to hierarchically structured models, the dynamic nested structures cause specific challenges. We present a tau-leaping algorithm for ML-Rules which tackles these specific challenges and evaluate the efficiency and accuracy of this adapted tau-leaping based on a recently developed visual analysis technique.