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

Henkel, Ron; Walthemath, Dagmar

doi:10.7287/peerj.preprints.376

Cited by 3 publications

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“…The main advantage of the previously described concept is its possibility to define flexible links between the data domains. In concordance with previous considerations ( 16 , 18 ), we incorporate the following types of links: links between annotations (in SBML, CellML and SED-ML) and ontology concepts, links between models (in SBML or CellML format) and SED-ML, links between model entities and SED-ML variables and links between model entities from different model representation formats. …”

Section: A Graph Database For Simulation Models and Associated Datamentioning

confidence: 97%

“…Consequently, the information contained in these meta-data items is not accessible for any kind of comparison or reasoning. Meta-data that are in principle available, but not retrievable include the structure of the model ( 16 ), model versions ( 17 ) and simulation setups ( 18 ). One consequence of inaccessible meta-data is that modeling results may become irreproducible, because the information on the simulation experiment is not associated with the model code ( 19 ).…”

Section: Introductionmentioning

confidence: 99%

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

2015

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Model repositories such as the BioModels Database, the CellML Model Repository or JWS Online are frequently accessed to retrieve computational models of biological systems. However, their storage concepts support only restricted types of queries and not all data inside the repositories can be retrieved. In this article we present a storage concept that meets this challenge. It grounds on a graph database, reflects the models’ structure, incorporates semantic annotations and simulation descriptions and ultimately connects different types of model-related data. The connections between heterogeneous model-related data and bio-ontologies enable efficient search via biological facts and grant access to new model features. The introduced concept notably improves the access of computational models and associated simulations in a model repository. This has positive effects on tasks such as model search, retrieval, ranking, matching and filtering. Furthermore, our work for the first time enables CellML- and Systems Biology Markup Language-encoded models to be effectively maintained in one database. We show how these models can be linked via annotations and queried.Database URL: https://sems.uni-rostock.de/projects/masymos/

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Section: A Graph Database For Simulation Models and Associated Datamentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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

2015

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“…Simulation Experiment Description Markup Language files can be linked to model descriptions in other formats, notably SBML or CellML, to ensure reproducibility of experiments presented in scientific publications. The links can, for example, be instantiated on the storage layer (Henkel et al, 2015 ), via the provision of files in a COMBINE archive (Bergmann et al, 2014 ), or through provision via public model repositories such as BioModels Database (Li et al, 2010 ) or the Physiome Repository (Yu et al, 2011 ).…”

Section: The Major Standards In Combine Todaymentioning

confidence: 99%

Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative

Hucka

Nickerson

Bader

et al. 2015

Front. Bioeng. Biotechnol.

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The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE’s aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models.

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Annotation-Based Feature Extraction from Sets of SBML Models

Alm

Waltemath

Wolkenauer

et al. 2014

Lecture Notes in Computer Science

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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.

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

Cited by 3 publications

References 1 publication

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

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

Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative

Annotation-Based Feature Extraction from Sets of SBML Models

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