Researcher profile

Ion I. Moraru

· UConn Health

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Reproducible computational biology experiments with SED-ML - The Simulation Experiment Description Markup Language
2011 · BMC Systems Biology · DOI 10.1186/1752-0509-5-198

BACKGROUND: The increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools. RESULTS: In this article, we present the Simulation Experiment Description Markup Language (SED-ML). SED-ML encodes in a computer-readable exchange format the information required by MIASE to enable reproduction of simulation experiments. It has been developed as a community project and it is defined in a detailed technical specification and additionally provides an XML schema. The version of SED-ML described in this publication is Level 1 Version 1. It covers the description of the most frequent type of simulation experiments in the area, namely time course simulations. SED-ML documents specify which models to use in an experiment, modifications to apply on the models before using them, which simulation procedures to run on each model, what analysis results to output, and how the results should be presented. These descriptions are independent of the underlying model implementation. SED-ML is a software-independent format for encoding the description of simulation experiments; it is not specific to particular simulation tools. Here, we demonstrate that with the growing software support for SED-ML we can effectively exchange executable simulation descriptions. CONCLUSIONS: With SED-ML, software can exchange simulation experiment descriptions, enabling the validation and reuse of simulation experiments in different tools. Authors of papers reporting simulation experiments can make their simulation protocols available for other scientists to reproduce the results. Because SED-ML is agnostic about exact modeling language(s) used, experiments covering models from different fields of research can be accurately described and combined.

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Co-authors

Michael Hucka

California Institute of Technology

1 shared publication
Nicolas Le Novère

University of Cambridge

1 shared publication
Fedor Kolpakov

1 shared publication
Sven Sahle

Heidelberg University

1 shared publication
Jacky L. Snoep

Stellenbosch University

1 shared publication
Dagmar Waltemath

University of Rostock

1 shared publication
Richard R. Adams

University of Edinburgh

1 shared publication
Frank Bergmann

California Institute of Technology

1 shared publication
Andrew K. Miller

Auckland Institute of Studies

1 shared publication
David Nickerson

Auckland Institute of Studies

1 shared publication