Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization
Misirli, Goksel and Cavaliere, Matteo and Waites, William and Pocock, Matthew and Madsen, Curtis and Gilfellon, Owen and Honorato-Zimmer, Ricardo and Zuliani, Paolo and Danos, Vincent and Wipat, Anil (2016) Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization. Bioinformatics, 32 (6). pp. 908-917. ISSN 1367-4803 (https://doi.org/10.1093/bioinformatics/btv660)
Preview |
Text.
Filename: Misirli_etal_Bioinformatics_2015_Annotation_of_rule_based_models_with_formal_semantics_to_enable_creation.pdf
Final Published Version License: Download (1MB)| Preview |
Abstract
Motivation: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. Results: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. Availability and implementation: The annotation ontology for rule-based models can be found at http://purl.org/rbm/rbmo. The krdf tool and associated executable examples are available at http://purl.org/rbm/rbmo/krdf. Contact: or vdanos@inf.ed.ac.uk.
-
-
Item type: Article ID code: 84416 Dates: DateEvent31 March 2016Published11 November 2015Published Online3 November 2015AcceptedSubjects: Science > Mathematics > Electronic computers. Computer science > Other topics, A-Z Department: Faculty of Science > Computer and Information Sciences Depositing user: Pure Administrator Date deposited: 27 Feb 2023 12:03 Last modified: 12 Aug 2024 00:56 URI: https://strathprints.strath.ac.uk/id/eprint/84416