Harmonizing semantic annotations for computational models in biology

Neal, Maxwell Lewis; König, Matthias; Nickerson, David; Mısırlı, Göksel; Kalbasi, Reza; Dräger, Andreas; Atalag, Koray; Chelliah, Vijayalakshmi; Cooling, Michael T.; Cook, Daniel L.; Crook, Sharon; Alba, Miguel de; Friedman, Samuel H.; Garny, Alan; Gennari, John H.; Gleeson, Padraig; Golebiewski, Martin; Hucka, Michael; Juty, Nick; Myers, Chris J.; Olivier, Brett G.; Sauro, Herbert M.; Scharm, Martin; Snoep, Jacky L.; Touré, Vasundra; Wipat, Anil; Wolkenhauer, Olaf; Waltemath, Dagmar

doi:10.1093/bib/bby087

Cited by 72 publications

(63 citation statements)

References 94 publications

(95 reference statements)

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“…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.…”

Section: Datamentioning

confidence: 99%

Executable Simulation Model of the Liver

König

2020

Preprint

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To address the issue of reproducibility in computational modeling we developed the concept of an executable simulation model (EXSIMO). An EXSIMO combines model, data and code with the execution environment to run the computational analysis in an automated manner using tools from software engineering. Key components are i) models, data and code for the computational analysis; ii) tests for models, data and code; and iii) an automation layer to run tests and execute the analysis. An EXSIMO combines version control, model, data, units, annotations, analysis, reports, execution environment, testing, continuous integration and release. We applied the concept to perform a replication study of a computational analysis of hepatic glucose metabolism in the liver. The corresponding EXSIMO is available from https://github.com/matthiaskoenig/exsimo.

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Section: Datamentioning

confidence: 99%

Executable Simulation Model of the Liver

König

2020

Preprint

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“…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 .…”

Section: Possible Implementationmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Munarko

Atalag

et al. 2019

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Motivation: Semantic annotation is a crucial step to assure reusability and reproducibility of biosimulation models in biology and physiology. For this purpose, the COmputational Modeling in BIology NEtwork (COMBINE) community recommend the use of the Resource Description Framework (RDF). The RDF implementation provides the flexibility of model entity searching (e.g. flux of sodium across apical plasma membrane) by utilising SPARQL. However, the rigidity and complexity of SPARQL syntax and the nature of semantic annotation which is not merely as a simple triple yet forming a tree-like structure may cause a difficulty. Therefore, the availability of an interface to convert a natural language query to SPARQL is beneficial. Results: We propose NLIMED, a natural language query to SPARQL interface to retrieve model entities from biosimulation models. Our interface can be applied to various repositories utilising RDF such as the PMR and Biomodels. We evaluate our interface by collecting RDF in the biosimulation models coded using CellML in PMR. First, we extract RDF as a tree structure and then store each subtree of a model entity as a modified triple of a model entity name, path, and class ontology into the RDF Graph Index. We also extract class ontology's textual metadata from the BioPortal and CellML and manage it in the Text Feature Index. With the Text Feature Index, we annotate phrases resulted by the NLQ Parser (Stanford parser or NLTK parser) into class ontologies. Finally, the detected class ontologies then are composed as SPARQL by incorporating the RDF Graph Index. Our annotator performance is far more powerful compared to the available service provided by BioPortal with F-measure of 0.756 and our SPARQL composer can find all possible SPARQL in the collection based on the annotation results. Currently, we already implement our interface in Epithelial Modelling Platform tool. Availability: https://github.com/napakalas/NLIMED Contact: ymun794@aucklanduni.ac.nz, d.nickerson@auckland.ac.nz

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“…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).…”

Section: Rdf and Sparqlmentioning

confidence: 99%

“…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.…”

Section: Biological Annotationmentioning

confidence: 99%

Model Annotation and Discovery with the Physiome Model Repository

Sarwar

Kalbasi

Gennari

et al. 2018

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Motivation:Mathematics and physics based computational models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models encoded in computable form help to ensure that such models are accessible, discoverable, and reusable. To this end, researchers have developed tools and standards to encode mathematical models of biological systems enabling reproducibility and reuse; tools and guidelines to facilitate semantic description of mathematical models; and repositories in which to archive, share, and discover models. Biologists and clinicians can leverage these resources to investigate specific questions and hypotheses. Results: We have comprehensively annotated a cohort of models with biological semantics. These annotated models are freely available in the Physiome Model Repository (PMR). To demonstrate the benefits of this approach, we have developed a web-based tool which enables users to discover models relevant to their work, with a particular focus on epithelial transport. In helping a user to discover relevant models this tool will provide users with suggestions for similar or alternative models they may wish to explore or utilize in their model based on the models they would like to use. The semantic annotation and the web tool we have developed is a new contribution enabling scientists to discover relevant models in the PMR as candidates for reuse in their own scientific endeavours. We believe that this approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research. Availability and implementation: https://github.com/dewancse/model-discovery-tool Contact: d.nickerson@auckland.ac.nz ✐ ✐ "output" -2018/12/20 -23:44 -page 2 -#2

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Harmonizing semantic annotations for computational models in biology

Cited by 72 publications

References 94 publications

Executable Simulation Model of the Liver

Executable Simulation Model of the Liver

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

Model Annotation and Discovery with the Physiome Model Repository

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