Annotation-Based Feature Extraction from Sets of SBML Models

Abstract

Background: Model repositories such as BioModels Database provide computational models of biological systems for the scientific community. These models contain rich semantic annotations that link model entities to concepts in well-established bio-ontologies such as Gene Ontology. Consequently, thematically similar models are likely to share similar annotations. Based on this assumption, we argue that semantic annotations are a suitable tool to characterize sets of models. These characteristics improve model classification, allow to identify additional features for model retrieval tasks, and enable the comparison of sets of models. Results:In this paper we discuss four methods for annotation-based feature extraction from model sets. We tested all methods on sets of models in SBML format which were composed from BioModels Database. To characterize each of these sets, we analyzed and extracted concepts from three frequently used ontologies, namely Gene Ontology, ChEBI and SBO. We find that three out of the methods are suitable to determine characteristic features for arbitrary sets of models: The selected features vary depending on the underlying model set, and they are also specific to the chosen model set. We show that the identified features map on concepts that are higher up in the hierarchy of the ontologies than the concepts used for model annotations. Our analysis also reveals that the information content of concepts in ontologies and their usage for model annotation do not correlate. Conclusions:Annotation-based feature extraction enables the comparison of model sets, as opposed to existing methods for model-to-keyword comparison, or model-to-model comparison.

Identifying frequent patterns in biochemical reaction networks - a workflow

“…Two downsides of this approach have to be considered: First, only 116 out of 575 (R29) models have reaction networks annotated with SBO terms. Second, as [40] states, the specificity of SBO-annotations varies among models. Taken together, the remaining reaction networks are less complex, allowing us to retrieve 176 patterns contained by at least 12 out of 116 valid models.…”

“…Current approaches for model clustering only incorporate semantic annotation and meta-information [40,41]. Our work is a first step towards creating structural similarity measures for biological models.…”

Combining computational models, semantic annotations and simulation experiments in a graph database

“…Both SBML and CellML use bio-ontologies to enrich model descriptions with semantic annotations, using Resource Description Framework (RDF) triples ( 46 ). In BioModels Database, models carry between 3 and 800 annotations, but on average 71 annotations, per model ( 47 ). For example, an annotation could be added to the SBML species , linking it to the ontology term in ChEBI ( 48 ) (ID CHEBI:15422).…”

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“…The sum of semantic annotations in a model describes its biological and mathematical background. In BioModels Database, models carry between three and 800 annotations, but on average 71 annotations, per model (Alm et al, 2014).…”

“…We can thus easily retrieve all models that are annotated with a particular ontology term. This is, for example, helpful in the classication of models (Alm et al, 2014).…”