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

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

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.

Details

OriginalspracheEnglisch
Seiten (von - bis)e1005898
FachzeitschriftPLoS Computational Biology
Jahrgang13
Ausgabenummer12
PublikationsstatusVeröffentlicht - Dez. 2017
Peer-Review-StatusJa

Externe IDs

Scopus 85039903466
PubMed 29244826
PubMedCentral PMC5747461
ORCID /0000-0003-2848-6949/work/141543333
ORCID /0000-0002-2524-1199/work/142251487

Schlagworte

Schlagwörter

  • Biological Science Disciplines/statistics & numerical data, Computational Biology, Computer Simulation, Humans, Models, Biological, Models, Statistical, Programming Languages, Reproducibility of Results, Software, Systems Biology