Browsing by Author "Toure, Vasundra"

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    Evolution of computational models in BioModels Database and the Physiome Model Repository
    (BioMed Central, 2018-04-12) Scharm, Martin; Gebhardt, Tom; Toure, Vasundra; Bagnacani, Andrea; Salehzadeh-Yazdi, Ali; Wolkenhauer, Olaf; Waltemath, Dagmar
    ENGLISH SUMMARY : Background: A useful model is one that is being (re)used. The development of a successful model does not finish with its publication. During reuse, models are being modified, i.e. expanded, corrected, and refined. Even small changes in the encoding of a model can, however, significantly affect its interpretation. Our motivation for the present study is to identify changes in models and make them transparent and traceable. Methods: We analysed 13734 models from BioModels Database and the Physiome Model Repository. For each model, we studied the frequencies and types of updates between its first and latest release. To demonstrate the impact of changes, we explored the history of a Repressilator model in BioModels Database. Results: We observed continuous updates in the majority of models. Surprisingly, even the early models are still being modified. We furthermore detected that many updates target annotations, which improves the information one can gain from models. To support the analysis of changes in model repositories we developed MoSt, an online tool for visualisations of changes in models. The scripts used to generate the data and figures for this study are available from GitHub github.com/binfalse/BiVeS-StatsGenerator and as a Docker image at hub.docker.com/r/binfalse/bives-statsgenerator. The website most.bio.informatik.uni-rostock.de provides interactive access to model versions and their evolutionary statistics. Conclusion: The reuse of models is still impeded by a lack of trust and documentation. A detailed and transparent documentation of all aspects of the model, including its provenance, will improve this situation. Knowledge about a model’s provenance can avoid the repetition of mistakes that others already faced. More insights are gained into how the system evolves from initial findings to a profound understanding. We argue that it is the responsibility of the maintainers of model repositories to offer transparent model provenance to their users.
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    Harmonizing semantic annotations for computational models in biology
    (Oxford University Press, 2019) Neal, Maxwell Lewis; Konig, Matthias; Nickerson, David; Mısırlı, Goksel; Kalbasi, Reza; Drager, Andreas; Atalag, Koray; Chelliah, Vijayalakshmi; Cooling, Michael T.; Cook, Daniel L.; Crook, Sharon; De Alba, Miguel; Friedman, Samuel H.; Garny, Alan; Gennari, John H.; Gleeson, Padraig; Golebiewski, Martin; Hucka, Michael; Juty, Nick; Myers, Chris; Olivier, Brett G.; Sauro, Herbert M.; Scharm, Martin; Snoep, Jacky L.; Toure, Vasundra; Wipat, Anil; Wolkenhauer, Olaf; Waltemath, Dagmar
    Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol.Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.