SemanticSBML: a tool for annotating, checking, and merging of biochemical models in SBML format

Semantic annotations in SBML (systems biology markup language) enable computer programs to check and process biochemical models based on their biochemical meaning. Annotations are an important prerequisite for model merging, which would be a major step towards the construction of large-scale cell models. The software tool semanticSBML allows users to check and edit MIRIAM annotations and SBO terms, the most common forms of annotation in SBML models. It uses a large collection of biochemical names and database identifiers to support modellers in finding the right annotations. Annotated SBML models can also be built from lists of chemical reactions. In model merging, semanticSBML suggests a preliminary merged model based on MIRIAM annotations in the original models. This model provides a starting point for manually aligning the elements of all input models. To resolve conflicting element properties, conflicts are highlighted and categorised. The user can navigate through the models, change the matching of model elements, check the conflicts between them and decide how they should be resolved. Alternatively, the software can resolve all conflicts automatically, selecting each time the attribute value from the input model with highest priority.
URL: "http://www.semanticsbml.org/":http://www.semanticsbml.org

Retrieval, alignment, and clustering of computational models based on semantic annotations

As the number of computational systems biology models increases, new methods are needed to explore their content and build connections with experimental data. In this Perspective article, the authors propose a flexible semantic framework that can help achieve these aims

A framework for mapping, visualisation and automatic model creation of signal-transduction networks

An intuitive formalism for reconstructing cellular networks from empirical data is presented, and used to build a comprehensive yeast MAP kinase network. The accompanying rxncon software tool can convert networks to a range of standard graphical formats and mathematical models

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

A simple clustering of the BioModels database using semanticSBML

The BioModels database contains biochemical network models in SBML format, in which the biochemical meaning of elements is specified by MIRIAM-compliant RDF annotations. We used these annotations to define a similarity measure for models, scoring the overlap of the biochemical systems described. Based on this score, we used two-way clustering to detect groups of similar models and groups of co-occuring model elements. To recognize and compare biochemical elements, we used routines from the software semanticSBML. A Python script extracts all MIRIAM annotations (regardless of their qualifiers) using the semanticSBML annotation classes. The result is a matrix in which the rows represent the models (e.g. BioModel 001), while the columns represent specific annotations (e.g. urn:miriam:reactome:REACT_15422). A matrix element is set to 1 if an identifier occurs in a model and to 0 otherwise. This matrix was used as an input for a hierarchical clustering algorithm (implemented in Matlab) and the clustered matrix was visualized. Model clustering allows to detect models describing similar biochemical processes (e.g. glycolysis) and their specific common elements. This may help to find candidate models for completing a given initial model, which could then be merged using semanticSBML

Die Berliner Volkszeitung digital erforschen: Digitales Kuratieren, Metadaten, Text Mining: Praktiken und Potentiale historischer Presseforschung in digitalen Kontexten

Mit digitalisierten Zeitungen und digitalen Zeitungsportalen hat sich im Laufe der letzten zwei Jahrzehnte für die historische Presseforschung eine grundlegend neue Forschungs- und Quellensituation herausgebildet. Trotzdem fallen digitale Methoden und Werkzeuge in der pressehistorischen Methodenpraxis bislang kaum ins Gewicht und ebenso wird die Quellenpraxis mit digitalisierten Zeitungen selten thematisiert. In diesem Sinne stellt der Beitrag die Fragen, welche spezifischen epistemischen und quellenkritischen Praktiken für den Umgang mit Zeitungen vonnöten sind, wenn sie als digitale Objekte bzw. Ressourcen vorliegen, und wie pressehistorische Forschungsprozesse in digitalen Kontexten modelliert und operationalisiert werden können. Als Beispiel dient hierfür die digitalisierte Ausgabe der Berliner Volkszeitung (1890-1930), die das Zeitungsinformationssystem ZEFYS der Staatsbibliothek zu Berlin - Preußischer Kulturbesitz zum europäischen Zeitungsportal Europeana Newspapers metadaten- und volltexterschlossen beigesteuert hat. Neben einer medien- und zeithistorischen Einordnung werden entlang der praktischen digitalen Forschung mit dem Zeitungskorpus drei Forschungsdimensionen systematisch diskutiert und mit Beispielen illustriert: (1.) Digitales Kuratieren, (2.) Metadaten und (3.) Text Mining

SemanticSBML – state of affairs

A number of new functions in the software tool semanticSBML make extensive use of MIRIAM-compliant annotations in SBML models. Semantic model search allows to obtain, from BioModels Database, a ranked list of models resembling a given query model or data set. Model clustering allows for exploring and visualising similarities among large numbers of SBML models. Parameter balancing uses collected kinetic constants - retrieved by using semantic annotations - to infer consistent sets of model parameters