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MAGPIE: Simplifying access and execution of computational models in the life sciences

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  • Christoph Baldow
  • Sebastian Salentin
  • Michael Schroeder
  • Ingo Roeder
  • Ingmar Glauche

Abstract

Over the past decades, quantitative methods linking theory and observation became increasingly important in many areas of life science. Subsequently, a large number of mathematical and computational models has been developed. The BioModels database alone lists more than 140,000 Systems Biology Markup Language (SBML) models. However, while the exchange within specific model classes has been supported by standardisation and database efforts, the generic application and especially the re-use of models is still limited by practical issues such as easy and straight forward model execution. MAGPIE, a Modeling and Analysis Generic Platform with Integrated Evaluation, closes this gap by providing a software platform for both, publishing and executing computational models without restrictions on the programming language, thereby combining a maximum on flexibility for programmers with easy handling for non-technical users. MAGPIE goes beyond classical SBML platforms by including all models, independent of the underlying programming language, ranging from simple script models to complex data integration and computations. We demonstrate the versatility of MAGPIE using four prototypic example cases. We also outline the potential of MAGPIE to improve transparency and reproducibility of computational models in life sciences. A demo server is available at magpie.imb.medizin.tu-dresden.de.

Suggested Citation

  • Christoph Baldow & Sebastian Salentin & Michael Schroeder & Ingo Roeder & Ingmar Glauche, 2017. "MAGPIE: Simplifying access and execution of computational models in the life sciences," PLOS Computational Biology, Public Library of Science, vol. 13(12), pages 1-11, December.
  • Handle: RePEc:plo:pcbi00:1005898
    DOI: 10.1371/journal.pcbi.1005898
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    References listed on IDEAS

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    1. Katja Rateitschak & Felix Winter & Falko Lange & Robert Jaster & Olaf Wolkenhauer, 2012. "Parameter Identifiability and Sensitivity Analysis Predict Targets for Enhancement of STAT1 Activity in Pancreatic Cancer and Stellate Cells," PLOS Computational Biology, Public Library of Science, vol. 8(12), pages 1-14, December.
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    Cited by:

    1. Hans H Diebner & Anna Kather & Ingo Roeder & Katja de With, 2020. "Mathematical basis for the assessment of antibiotic resistance and administrative counter-strategies," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-22, September.

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