Browsing by Author "Waltemath, Dagmar"

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    Minimum Information About a Simulation Experiment (MIASE)
    Waltemath, Dagmar; Adams, Richard; Beard, Daniel A.; Bergmann, Frank T.; Bhalla, Upinder S.; Britten, Randall; Chelliah, Vijayalakshmi; Cooling, Michael T.; Cooper, Jonathan; Crampin, Edmund J.; Garny, Alan; Hoops, Stefan; Hucka, Michael; Hunger, 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 Novère, Nicolas (Public Library of Science, 2011-04-28)
    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 (MIASE, Glossary in Box 1) 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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    SBML Level 3: an extensible format for the exchange and reuse of biological models
    Keating, Sarah M.; Waltemath, Dagmar; Koenig, Matthias; Zhang, Fengkai; Draeger, Andreas; Chaouiya, Claudine; Bergmann, Frank T.; Finney, Andrew; Gillespie, Colin S.; Helikar, Tomas; Hoops, Stefan; Malik-Sheriff, Rahuman S.; Moodie, Stuart L.; Moraru, Ion I.; Myers, Chris J.; Naldi, Aurelien; Olivier, Brett G.; Sahle, Sven; Schaff, James C.; Smith, Lucian P.; Swat, Maciej J.; Thieffry, Denis; Watanabe, Leandro; Wilkinson, Darren J.; Blinov, Michael L.; Begley, Kimberly; Faeder, James R.; Gomez, Harold F.; Hamm, Thomas M.; Inagaki, Yuichiro; Liebermeister, Wolfram; Lister, Allyson L.; Lucio, Daniel; Mjolsness, Eric; Proctor, Carole J.; Raman, Karthik; Rodriguez, Nicolas; Shaffer, Clifford A.; Shapiro, Bruce E.; Stelling, Joerg; Swainston, Neil; Tanimura, Naoki; Wagner, John; Meier-Schellersheim, Martin; Sauro, Herbert M.; Palsson, Bernhard; Bolouri, Hamid; Kitano, Hiroaki; Funahashi, Akira; Hermjakob, Henning; Doyle, John C.; Hucka, Michael (2020-08)
    Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developedSBMLLevel 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades ofSBMLand a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and howSBMLLevel 3 provides the foundation needed to support this evolution.