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MAGPIE: Simplifying access and execution of computational models in the life sciences
Christoph Baldow1, Sebastian Salentin2, Michael Schroeder2
1Institute for Medical Informatics and Biometry, Medizinische Fakultät Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Plos Computational Biology
|December 16, 2017
Summary
MAGPIE is a new platform for executing and publishing computational models in life sciences, overcoming limitations of existing Systems Biology Markup Language (SBML) platforms. It supports diverse programming languages, enhancing model re-use and reproducibility.
Area of Science:
- Life Sciences
- Computational Biology
- Systems Biology
Background:
- Quantitative methods linking theory and observation are crucial in life sciences.
- Numerous computational models exist, but re-use is hindered by execution challenges.
- Existing platforms often focus on specific model types like Systems Biology Markup Language (SBML).
Purpose of the Study:
- To introduce MAGPIE, a versatile platform for computational model execution and publication.
- To address the limitations of generic model application and re-use.
- To provide a flexible yet user-friendly solution for computational modeling in life sciences.
Main Methods:
- Development of MAGPIE, a Modeling and Analysis Generic Platform with Integrated Evaluation.
- Integration of diverse computational models regardless of programming language.
- Demonstration of MAGPIE's capabilities through four prototypic example cases.
Main Results:
- MAGPIE enables unrestricted publishing and execution of computational models.
- The platform supports a wide range of models, from simple scripts to complex computations.
- MAGPIE enhances flexibility for programmers and ease of use for non-technical users.
Conclusions:
- MAGPIE bridges the gap in computational model execution and re-use.
- The platform has the potential to significantly improve transparency and reproducibility in life science research.
- MAGPIE offers a universal solution beyond traditional SBML platforms.
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