MAGPIE: Simplifying access and execution of computational models in the life sciences
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
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
Original language | English |
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Pages (from-to) | e1005898 |
Journal | PLoS Computational Biology |
Volume | 13 |
Issue number | 12 |
Publication status | Published - Dec 2017 |
Peer-reviewed | Yes |
External IDs
Scopus | 85039903466 |
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PubMed | 29244826 |
PubMedCentral | PMC5747461 |
ORCID | /0000-0003-2848-6949/work/141543333 |
ORCID | /0000-0002-2524-1199/work/142251487 |
Keywords
Keywords
- Biological Science Disciplines/statistics & numerical data, Computational Biology, Computer Simulation, Humans, Models, Biological, Models, Statistical, Programming Languages, Reproducibility of Results, Software, Systems Biology