RIMBO - An Ontology for Model Revision Databases
Paper i proceeding, 2023

The use of computational models is growing throughout most scientific domains. The increased complexity of such models, as well as the increased automation of scientific research, imply that model revisions need to be systematically recorded. We present RIMBO (Revisions for Improvements of Models in Biology Ontology), which describes the changes made to computational biology models. The ontology is intended as the foundation of a database containing and describing iterative improvements to models. By recording high level information, such as modelled phenomena, and model type, using controlled vocabularies from widely used ontologies, the same database can be used for different model types. The database aims to describe the evolution of models by recording chains of changes to them. To make this evolution transparent, emphasise has been put on recording the reasons, and descriptions, of the changes. We demonstrate the usefulness of a database based on this ontology by modelling the update from version 8.4.1 to 8.4.2 of the genome-scale metabolic model Yeast8, a modification proposed by an abduction algorithm, as well as thousands of simulated revisions. This results in a database demonstrating that revisions can successfully be modelled in a semantically meaningful and storage efficient way. We believe such a database is necessary for performing automated model improvement at scale in systems biology, as well as being a useful tool to increase the openness and traceability for model development. With minor modifications the ontology can also be used in other scientific domains. The ontology is made available at https://github.com/filipkro/rimbo and will be continually updated.

Ontology

Semantic web

Knowledge representation

Database

Computational biology

Författare

Filip Kronström

Chalmers, Data- och informationsteknik, Data Science och AI

Alexander Gower

Chalmers, Data- och informationsteknik, Data Science och AI

Ievgeniia Tiukova

Kungliga Tekniska Högskolan (KTH)

Chalmers, Life sciences, Infrastrukturer

Ross King

Alan Turing Institute

University of Cambridge

Chalmers, Data- och informationsteknik, Data Science och AI

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 14276 LNAI 523-534
9783031452741 (ISBN)

26th International Conference on Discovery Science, DS 2023
Porto, Portugal,

Ämneskategorier

Datavetenskap (datalogi)

DOI

10.1007/978-3-031-45275-8_35

Mer information

Senast uppdaterat

2023-11-06