Harmonizing semantic annotations for computational models in biology

Abstract

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

Executable Simulation Model of the Liver

“…Semantic annotations describe the computational or biological meaning of models and data via machine-readable links to knowledge resource terms. These annotations help to find models and datasets, accelerate model composition and enable knowledge integration between models and experimental data [19]. Within the EXSIMO it is tested that the minimum information requested in the annotation of biochemical models (MIRIAM [7]) is fulfilled.…”

NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories

“…SemGen , OpenCOR (Garny and Hunter, 2015), and Saint (Lister et al, 2009), which can provide class ontologies suggestion based on the available ontology databases may take advantage of NLIMED. Following the COMBINE recommendation about standardisation of biosimulation model annotation (Neal et al, 2019), this work can be directed to provide a comprehensive search interface to discover model entities from various biosimulation model repositories .…”

“…Thus, this discoverability supports communities in biology and physiology through reusability and reproducibility. Currently, the utilisation of the RDF in biosimulation models semantic annotation has been formalised as the only standard by the COmputational Modeling in BIology NEtwork (COMBINE) community (Neal et al, 2019).…”

Model Annotation and Discovery with the Physiome Model Repository

“…The subject references the element being annotated, the predicate defines the assertion that is being made, and the object is the value being asserted. The COmputational Modeling in BIology NEtwork (COMBINE, 2018) community has recently recommended the use of RDF for storing annotations on models (Neal et al, 2018a).…”

“…This initial cohort of well annotated models serves as a proof of concept for testing our implementation. According to the recent community agreement on the harmonization of annotation across the computational modelling in biology community (Neal et al, 2018a), we expect the repository of available annotated models to rapidly grow as the community begins to populate this repository. With community adoption of the harmonized annotation guidelines, we will be able to utilize our platform to query across all available repositories and model encoding formats.…”