A semantics, energy-based approach to automate biomodel composition.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2022
2022
Historique:
received:
04
01
2022
accepted:
20
05
2022
entrez:
3
6
2022
pubmed:
4
6
2022
medline:
9
6
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 35657966
doi: 10.1371/journal.pone.0269497
pii: PONE-D-21-39103
pmc: PMC9165793
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0269497Subventions
Organisme : NIBIB NIH HHS
ID : P41 EB023912
Pays : United States
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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