Browsing by Author "Le Novere, Nicolas"

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    The evolution of standards and data management practices in systems biology
    (EMBO, 2015-12) Stanford, Natalie J.; Wolstencroft, Katherine; Golebiewski, Martin; Kania, Renate; Juty, Nick; Tomlinson, Christopher; Owen, Stuart; Butcher, Sarah; Hermjakob, Henning; Le Novere, Nicolas; Mueller, Wolfgang; Snoep, Jacky; Goble, Carole
    Systems biology involves the integration of multiple heterogeneous data sets, in order to model and predict biological processes. The domain's interdisciplinary nature requires data, models and other research assets to be formatted and described in standard ways to enable exchange and reuse. Infrastructure for Systems Biology Europe (ISBE) is a project to establish essential, centralized services for systems biology researchers throughout the systems biology lifecycle. A key component of ISBE is to support the management, integration and exchange of data, models, results and protocols. To inform further ISBE development, we surveyed the community to evaluate the uptake of available standards, and current practices of researchers in data and model management.
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    Identifiers for the 21st century : how to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data
    (Public Library of Science, 2017-06-29) McMurry, Julie A.; Juty, Nick; Blomberg, Niklas; Burdett, Tony; Conlin, Tom; Conte, Nathalie; Courtot, Melanie; Deck, John; Dumontier, Michel; Fellows, Donal K.; Gonzalez-Beltran, Alejandra; Gormanns, Philipp; Grethe, Jeffrey; Hastings, Janna; Heriche, Jean-Karim; Hermjakob, Henning; Ison, Jon C.; Jimenez, Rafael C.; Jupp, Simon; Kunze, John; Laibe, Camille; Le Novere, Nicolas; Malone, James; Martin, Maria Jesus; McEntyre, Johanna R.; Morris, Chris; Muilu, Juha; Muller, Wolfgang; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Sariyar, Murat; Snoep, Jacky L.; Soiland-Reyes, Stian; Stanford, Natalie J.; Swainston, Neil; Washington, Nicole; Williams, Alan R.; Wimalaratne, Sarala M.; Winfree, Lilly M.; Wolstencroft, Katherine; Goble, Carole; Mungall, Christopher J.; Haende, Melissa A.; Parkinson, Helen
    In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.
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    Minimum information about a simulation experiment (MIASE)
    (PLOS, 2011-04) Waltemath, Dagmar; Adams, Richard; Beard, Daniel A.; Bergmann, Frank T.; Balla, Upinder S.; Britten, Randall; Chelliah, Vijayalakshmi; Cooling, Michael T.; Cooper, Jonathan; Crampin, Edmund J.; Garny, Alan; Hoops, Stefan; Hucka, Michael; Hunter, Peter; Klipp, Edda; Laibe, Camille; Miller, Andrew K.; Moraru, Ion; Nickerson, David; Nielsen, Poul; Nikolski, Macha; Sahle, Sven; Sauro, Herbert M.; Schmidt, Henning; Snoep, Jacky L.; Tolle, Dominic; Wolkenhauer, Olaf; Le Novere, Nicolas
    Reproducibility of experiments is a basic requirement for science. Minimum Information (MI) guidelines have proved a helpful means of enabling reuse of existing work in modern biology. The Minimum Information Required in the Annotation of Models (MIRIAM) guidelines promote the exchange and reuse of biochemical computational models. However, information about a model alone is not sufficient to enable its efficient reuse in a computational setting. Advanced numerical algorithms and complex modeling workflows used in modern computational biology make reproduction of simulations difficult. It is therefore essential to define the core information necessary to perform simulations of those models. The Minimum Information About a Simulation Experiment describes the minimal set of information that must be provided to make the description of a simulation experiment available to others. It includes the list of models to use and their modifications, all the simulation procedures to apply and in which order, the processing of the raw numerical results, and the description of the final output. MIASE allows for the reproduction of any simulation experiment. The provision of this information, along with a set of required models, guarantees that the simulation experiment represents the intention of the original authors. Following MIASE guidelines will thus improve the quality of scientific reporting, and will also allow collaborative, more distributed efforts in computational modeling and simulation of biological processes.
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    Reproducible computational biology experiments with SED-ML -The Simulation Experiment Description Markup Language
    (BioMed Central, 2011-12) Waltemath, Dagmar; Adams, Richard; Bergmann, Frank T.; Hucka, Michael; Kolpakov, Fedor; Miller, Andrew K.; Moraru, Ion I.; Nickerson, David; Sahle, Sven; Snoep, Jacky L.; Le Novere, Nicolas
    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.