A semantics, energy-based approach to automate biomodel composition

Hierarchical modelling is essential to achieving complex, large-scale models. However, not all modelling schemes support hierarchical composition, and correctly mapping points of connection between models requires comprehensive knowledge of each model's components and assumptions. To address these challenges in integrating biosimulation models, we propose an approach to automatically and confidently compose biosimulation models. The approach uses bond graphs to combine aspects of physical and thermodynamics-based modelling with biological semantics. We improved on existing approaches by using semantic annotations to automate the recognition of common components. The approach is illustrated by coupling a model of the Ras-MAPK cascade to a model of the upstream activation of EGFR. Through this methodology, we aim to assist researchers and modellers in readily having access to more comprehensive biological systems models.

Automated summary

Hierarchical modelling is essential for complex and large-scale models. To address the challenges in integrating biosimulation models, an approach to automatically and confidently compose biosimulation models using bond graphs and semantic annotations is proposed. By coupling a model of the Ras-MAPK cascade and a model of upstream activation of EGFR, this approach aims to provide researchers and modellers with readily accessible comprehensive biological systems models.