Abstract

BackgroundThe information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web. In fact, such models are shared to provide the characterization of molecular processes, while biomedical ontologies detail a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Indeed, having different objectives and different abstraction levels, most of these resources "speak" different languages. Semantic web technologies are here explored as a means to address some of these problems.MethodsEmploying an extensible collection of interpreters, we developed OREMP (Ontology Reasoning Engine for Molecular Pathways), a system that abstracts the information from different resources and combines them together into a coherent ontology. Continuing this effort we present OREMPdb; once different pathways are fed into OREMP, species are linked to the external ontologies referred and to reactions in which they participate. Exploiting these links, the system builds species-sets, which encapsulate species that operate together. Composing all of the reactions together, the system computes all of the reaction paths from-and-to all of the species-sets.ResultsOREMP has been applied to the curated branch of BioModels (2011/04/15 release) which overall contains 326 models, 9244 reactions, and 5636 species. OREMPdb is the semantic dictionary created as a result, which is made of 7360 species-sets. For each one of these sets, OREMPdb links the original pathway and the link to the original paper where this information first appeared.

Highlights

  • The information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web

  • Current advances in both topics suggest an information integration cycle based on shared knowledge-bases, but because of different languages spoken by the data sources and different abstraction levels, there is a lack of an overall frame capable of identifying overlaps and

  • There is a lack of strategies for the integration of quantitative biological sources based on different formats (e.g., Systems Biology Markup Language (SBML), CellML, and MML [16]); this means that annotated by means of the same ontologies, computational pathway model repositories based on different formats cannot be unified preserving model dynamics

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Summary

Introduction

The information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web Such models are shared to provide the characterization of molecular processes, while biomedical ontologies detail a semantic context to the majority of those pathways. Biomedical ontologies are being created to provide a semantic context for the molecular species and reactions that they contain Current advances in both topics suggest an information integration cycle based on shared knowledge-bases, but because of different languages (i.e., the data formats) spoken by the data sources and different abstraction levels, there is a lack of an overall frame capable of identifying overlaps and of the discrepancies between two SBML [14] models is SemanticSBML [15], which exploits machine-readable information and the user input to create a merged SBML model. We report that this system is currently employed in the MIT Cytosolve Project http://cytosolve.mit.edu where it is used to ensure correct parallel simulations

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