Reproducible computational biology experiments with SED-ML - The Simulation Experiment Description Markup Language

Waltemath, Dagmar; Adams, Richard R.; Bergmann, Frank; Hucka, Michael; Kolpakov, Fedor A.; Miller, Andrew; Moraru, Ion I.; Nickerson, David; Sahle, Sven; Snoep, Jacky L.; Novère, Nicolas Le

doi:10.1186/1752-0509-5-198

Cited by 232 publications

(198 citation statements)

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“…It supports construction, simulation, and exchange of models in standard formats. In addition, JWS Online offers curation services for scientific journals, with secure access to models under review, and it allows the linking of models to experimental data files stored in the FAIRDOMHub (Wolstencroft et al, 2016), (https://fairdomhub.org/).…”

Section: Introductionmentioning

confidence: 99%

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The JWS online simulation database

et al. 2017

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Summary JWS Online is a web-based platform for construction, simulation and exchange of models in standard formats. We have extended the platform with a database for curated simulation experiments that can be accessed directly via a URL, allowing one-click reproduction of published results. Users can modify the simulation experiments and export them in standard formats. The Simulation database thus lowers the bar on exploring computational models, helps users create valid simulation descriptions and improves the reproducibility of published simulation experiments. Availability and Implementation The Simulation Database is available on line at https://jjj.bio.vu.nl/models/experiments/.

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Section: Introductionmentioning

confidence: 99%

“…The Simulation Experiment Description Markup Language, SED-ML (Waltemath et al, 2011), describes all necessary steps to simulate models and to compute and plot results. The second standard is the COMBINE archive (Bergmann et al, 2014),…”

Section: Introductionmentioning

confidence: 99%

The JWS online simulation database

et al. 2017

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“…The SED-ML format is defined by an XML Schema [Fallside et al, 2001]. Level 1 Version 2 is the successor of Level 1 Version 1, which is described in [Waltemath et al, 2011b]. …”

Section: Introductionmentioning

confidence: 99%

Simulation Experiment Description Markup Language (SED-ML) Level 1 Version 2

Bergmann

Cooper

Novère

et al. 2015

Journal of Integrative Bioinformatics

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SummaryThe number, size and complexity of computational models of biological systems are growing at an ever increasing pace. It is imperative to build on existing studies by reusing and adapting existing models and parts thereof. The description of the structure of models is not sufficient to enable the reproduction of simulation results. One also needs to describe the procedures the models are subjected to, as recommended by the Minimum Information About a Simulation Experiment (MIASE) guidelines. This document presents Level 1 Version 2 of the Simulation Experiment Description Markup Language (SED-ML), a computer-readable format for encoding simulation and analysis experiments to apply to computational models. SED-ML files are encoded in the Extensible Markup Language (XML) and can be used in conjunction with any XML-based model encoding format, such as CellML or SBML. A SED-ML file includes details of which models to use, how to modify them prior to executing a simulation, which simulation and analysis procedures to apply, which results to extract and how to present them. Level 1 Version 2 extends the format by allowing the encoding of repeated and chained procedures.* To whom correspondence should be addressed. Email: sed-ml-editors@lists.sourceforge.net doi:10.2390/biecoll-jib-2015-262 1 The latest release of the Level 1 Version 2 specification is available at http://identifiers.org/combine.specifications/sed-ml.level-1.version-2To discuss any aspect of the current SED-ML specification as well as language details, please send your messages to the mailing list sed-ml-discuss@lists.sourceforge.net.To get subscribed to the mailing list, please write to the same address sed-ml-discuss@lists.sourceforge.net.To contact the authors of the SED-ML specification, please write to sed-ml-editors@lists.sourceforge.net

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“…This confrontation often generates new hypotheses, and many if not most new models arise by modification of existing ones (Smith et al, 2007;Waltemath et al, 2013). However, most virtual experiments are not built to be reproducible (Waltemath et al, 2011b), and thus die with the paper they are published in. This inhibits the critical scrutiny of models, as models are seldom subjected to the same simulation experiments as their predecessors, or revisited later in the light of new data.…”

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A call for virtual experiments: Accelerating the scientific process

Cooper

Vik

Waltemath

2015

Progress in Biophysics and Molecular Biology

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Experimentation is fundamental to the scientific method, whether for exploration, description or explanation. We argue that promoting the reuse of virtual experiments (the in silico analogues of wet-lab or field experiments) would vastly improve the usefulness and relevance of computational models, encouraging critical scrutiny of models and serving as a common language between modellers and experimentalists. We review the benefits of reusable virtual experiments: in specifying, assaying, and comparing the behavioural repertoires of models; as prerequisites for reproducible research; to guide model reuse and composition; and for quality assurance in the translational application of models. A key step towards achieving this is that models and experimental protocols should be represented separately, but annotated so as to facilitate the linking of models to experiments and data. Lastly, we outline how the rigorous, streamlined confrontation between experimental datasets and candidate models would enable a "continuous integration" of biological knowledge, transforming our approach to systems biology. Dear Sirs,We enclose a manuscript titled "A call for virtual experiments: accelerating the scientific process" for inclusion within the Special Issue on "Multi-bio and multi-scale systems biology" in Progress in Biophysics and Molecular Biology. In it we propose the concept of "virtual experiments" as the in silico analogues of wet-lab or field experiments, and argue that this concept deserves as much attention as the computational models to which such experiments are applied. Promoting the reuse of virtual experiments would vastly improve the usefulness and relevance of computational models, encouraging critical scrutiny of models and serving as a common language between modellers and experimentalists.Experiments are fundamental to the scientific method, whether for exploration, description or explanation. Experiments should therefore be as central in computational biology as in other sciences, for studying the implications of theories or hypotheses encoded as computational models. In current practice, however, experiments applied to models are usually embedded in opaque computer code, which is often not available for public scrutiny. We discuss how the protocol for a computational experiment should be viewed as an entity in its own right, and cleanly separated from the description of the model itself, and from surrounding code. Annotations can then facilitate the linking of models to experiment descriptions and any corresponding data available.This approach can provide many benefits for scientific progress: in specifying, assaying, and comparing the behavioural repertoires of models; as a prerequisite for reproducible research; to guide model reuse and composition; and for quality assurance in the application of computational biology models. The paper lays out these benefits to motivate wider adoption and tool support for virtual experiments. We also present a "vision of the future" in which virtual experim...

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Reproducible computational biology experiments with SED-ML - The Simulation Experiment Description Markup Language

Cited by 232 publications

References 23 publications

The JWS online simulation database

The JWS online simulation database

Simulation Experiment Description Markup Language (SED-ML) Level 1 Version 2

A call for virtual experiments: Accelerating the scientific process

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