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Integrating Systems Biology Models and Biomedical Ontologies View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Aberystwyth Research Portal Aberystwyth University Integrating systems biology models and biomedical ontologies Hoehndorf, Robert; Dumontier, Michel; Gennari, John H.; Wimalaratne, Sarala; de Bono, Bernard; Cook, Daniel L.; Gkoutos, Georgios V. Published in: BMC Systems Biology DOI: 10.1186/1752-0509-5-124 Publication date: 2011 Citation for published version (APA): Hoehndorf, R., Dumontier, M., Gennari, J. H., Wimalaratne, S., de Bono, B., Cook, D. L., & Gkoutos, G. V. (2011). Integrating systems biology models and biomedical ontologies. BMC Systems Biology, 5(1), [124]. https://doi.org/10.1186/1752-0509-5-124 General rights Copyright and moral rights for the publications made accessible in the Aberystwyth Research Portal (the Institutional Repository) are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the Aberystwyth Research Portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the Aberystwyth Research Portal Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. tel: +44 1970 62 2400 email: [email protected] Download date: 03. Oct. 2019 Hoehndorf et al. BMC Systems Biology 2011, 5:124 http://www.biomedcentral.com/1752-0509/5/124 METHODOLOGYARTICLE Open Access Integrating systems biology models and biomedical ontologies Robert Hoehndorf1*, Michel Dumontier2,3, John H Gennari4, Sarala Wimalaratne5, Bernard de Bono5, Daniel L Cook6,7 and Georgios V Gkoutos1 Abstract Background: Systems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration of data and are often used to annotate biosimulation models in systems biology. Results: We provide a framework to integrate representations of in silico systems biology with those of in vivo biology as described by biomedical ontologies and demonstrate this framework using the Systems Biology Markup Language. We developed the SBML Harvester software that automatically converts annotated SBML models into OWL and we apply our software to those biosimulation models that are contained in the BioModels Database. We utilize the resulting knowledge base for complex biological queries that can bridge levels of granularity, verify models based on the biological phenomenon they represent and provide a means to establish a basic qualitative layer on which to express the semantics of biosimulation models. Conclusions: We establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms. Background predict and simulate complex systems’ behavior. Key Systems biology focuses on the analysis of whole biologi- challenges that systems biology research faces today are cal systems and interactions occuring within them. For to integrate data within and across domains and levels of this purpose, it transcends classical boundaries between granularity, to integrate data that is available in different domains and levels of granularity and follows an integra- formats and locations, to provide validation procedures tive approach towards the discovery of biological knowl- for computational biosimulation models as well as to edge. Instead of reducing complex biological phenomena develop standards for the exchange and integration of to their basic parts, systems biologists perceive and study models, simulations and results [1-4]. these phenomena as components of a network of interre- To address these challenges, the research community lated processes spanning multiple domains [1]. The has proposed standard languages for modelling and methods used to investigate systems’ behavior rely on the model exchange, and has developed common commu- integration of multi-scale data across levels of granularity, nity standards for the annotation of models. In particu- the “integration of in-silico, in-vitro, and in-vivo research” lar, the Systems Biology Markup Language (SBML) [5], [2], and the construction of computational models to the Cell Markup Language (CellML) [6] and the BioPAX standard [7] are standardized model exchange languages * Correspondence: [email protected] that aim to facilitate basic interoperability between the 1Department of Genetics, University of Cambridge, Downing Street, research results that are produced by different research Cambridge, CB2 3EH, UK communities. To support the adoption of standard Full list of author information is available at the end of the article © 2011 Hoehndorf et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Hoehndorf et al. BMC Systems Biology 2011, 5:124 Page 2 of 16 http://www.biomedcentral.com/1752-0509/5/124 languages, tools that are capable of validating models the possibility for model verification, may lead to a gen- with respect to their specification language [8-11] or uine standardization of the semantics of modelling lan- additional guidelines and constraints associated with a guages based on the biology represented through their modelling standard [12] are available. Model annotation use: modelling languages would contain a knowledge- standards include the Minimum Information Required based layer that formally represents the structure of and in the Annotation of Models (MIRIAM) [13]. MIRIAM the interactions within a biological system. specifies which information should be added as meta- Here, we demonstrate a method to integrate biosimu- data to systems biology models and other resources. A lation models in systems biology with representations of part of this metadata is intended to refer to the biologi- in vivo biology as described by biomedical ontologies in cal phenomena that a model intends to represent. For a common formal framework such that information can thispurpose,theannotationscanbemadeusingan flow between both disciplines. To evaluate this frame- identifier that belong to an external resources such as a work, we apply it to all computational models in the biomedical terminology or ontology. Ontologies focus BioModels Database [16] (release 18) that are available on a qualitative description of the biomedical domain in SBML [5] and annotated with biomedical ontologies and are commonly represented in formal languages using the MIRIAM community standard [13]. We pro- which facilitate automated discovery of contradictory vide an ontological analysis of the SBML and specify its statements and flexible access to knowledge through ontological commitment [15], i.e., we formalize how the automated reasoning. structure of a model in SBML determines the structure Most efforts in using ontologies in systems biology of the biological phenomena it represents. We imple- modelling are currently limited to the annotation of ment the result of our analysis and developed the SBML models with ontologies in which the terms from ontolo- Harvester software, which converts annotated SBML gies are treated as metadata. We argue that an integra- models into OWL. We use the SBML Harvester to cre- tion of the research areas of systems biology modelling ate a formal representation of 269 computational models and biomedical ontologies can establish an information contained in the BioModels Database. Based on this flow [14] between them. Such an integration can make a representation, we verify the consistency of the models significant contribution towards achieving the aims of and their annotations with respect to the biological phe- system biology while addressing some of the key chal- nomena they represent. After establishing the consis- lenges systems biology faces today. Information flow tency of both, we use the converted models to from biomedical ontologies to systems biology would demonstrate how reasoning over biomedical ontologies enable biosimulation modelling and simulation frame- can address data integration problems that systems biol- works in systems biology to access the information that ogy faces today: we use the knowledge base of computa- is available in data repositories through biomedical tional models to perform complex biological queries ontologies and use this information both to enrich and that cross multiple domains and levels of granularity constrain the scientific analyses of biological systems. and we demonstrate how
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