A semantics, energy-based approach to automate biomodel composition

• Published in: PloS one Vol. 17; no. 6; p. e0269497
• Main Authors: Shahidi, Niloofar, Pan, Michael, Tran, Kenneth, Crampin, Edmund J, Nickerson, David P
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.06.2022; Public Library of Science (PLoS)
• Subjects: Analysis; Annotations; Automation; Biology and life sciences; Bond graphs; Chemical elements; Chemical reactions; Composition; Computer and Information Sciences; Energy; Large-scale models; MAP kinase; Mechanization; Medical research; Methods; Modelling; Multilevel analysis; Physical Sciences; Physics; Scale models; Scholarships & fellowships; Semantics; Thermodynamics; Australia; New Zealand
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0269497

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.