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Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices

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Yeast Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2049))

Abstract

Computational systems biology involves integrating heterogeneous datasets in order to generate models. These models can assist with understanding and prediction of biological phenomena. Generating datasets and integrating them into models involves a wide range of scientific expertise. As a result these datasets are often collected by one set of researchers, and exchanged with others researchers for constructing the models. For this process to run smoothly the data and models must be FAIR—findable, accessible, interoperable, and reusable. In order for data and models to be FAIR they must be structured in consistent and predictable ways, and described sufficiently for other researchers to understand them. Furthermore, these data and models must be shared with other researchers, with appropriately controlled sharing permissions, before and after publication. In this chapter we explore the different data and model standards that assist with structuring, describing, and sharing. We also highlight the popular standards and sharing databases within computational systems biology.

Natalie J. Stanford and Martin Scharm share joint lead authorship.

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Notes

  1. 1.

    http://www.vph-institute.org/

  2. 2.

    http://www.vmh.life

  3. 3.

    http://www.virtual-liver.de/

  4. 4.

    A website that collects and monitors retracted research results: http://retractionwatch.com/

  5. 5.

    http://co.mbine.org

  6. 6.

    https://fairsharing.org/collection/MIBBI

  7. 7.

    https://fairdom.eu

  8. 8.

    https://fairsharing.org/collection/FAIRDOM

  9. 9.

    http://project.isbe.eu/

  10. 10.

    https://www.w3.org/XML/

  11. 11.

    https://www.w3.org/Math/

  12. 12.

    http://sbml.org/SBML_Software_Guide/SBML_Software_Matrix

  13. 13.

    https://www.cellml.org/tools

  14. 14.

    https://sed-ml.github.io/showcase.html

  15. 15.

    https://github.com/numl/numl

  16. 16.

    https://github.com/NuML/NuML/tree/master/libnuml

  17. 17.

    https://www.w3.org/TR/2014/REC-rdf11-concepts-20140225/

  18. 18.

    https://www.ebi.ac.uk/sbo/main/

  19. 19.

    http://co.mbine.org/standards/kisao

  20. 20.

    http://co.mbine.org/standards/teddy/

  21. 21.

    http://jermontology.org/

  22. 22.

    http://copasi.org/

  23. 23.

    http://opencor.ws

  24. 24.

    http://jjj.biochem.sun.ac.za/

  25. 25.

    https://opencobra.github.io/

  26. 26.

    https://neo4j.com/

  27. 27.

    https://bitbucket.org/jummp/jummp

  28. 28.

    https://git-scm.com/

  29. 29.

    https://fairdomhub.org/

  30. 30.

    https://semsproject.github.io/BiVeS/

  31. 31.

    http://www.opensourcebrain.org/

  32. 32.

    http://bigg.ucsd.edu

  33. 33.

    https://cat.bio.informatik.uni-rostock.de/

  34. 34.

    http://sysbioapps.dyndns.org/SED-ML_Web_Tools/

  35. 35.

    http://vcell.org

  36. 36.

    https://github.com/SemsProject/CombineArchiveShowCase/

  37. 37.

    http://validation.scienceexchange.com/

  38. 38.

    http://www.dataone.org

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Authors and Affiliations

  1. School of Computer Science, University of Manchester, Manchester, UK

    Natalie J. Stanford, Paul D. Dobson, Stuart Owen, Carole Goble & Jacky Snoep

  2. Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany

    Martin Scharm

  3. Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany

    Martin Golebiewski & Ulrike Wittig

  4. Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

    Michael Hucka

  5. Signalling ISP, The Babraham Institute, Cambridge, UK

    Varun B. Kothamachu

  6. Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand

    David Nickerson

  7. BIOMS/BioQuant, Heidelberg University, Heidelberg, Germany

    Jürgen Pahle

  8. Centre for Quantitative Medicine, University of Connecticut, Farmington, CT, USA

    Pedro Mendes

  9. Biochemistry, Stellenbosch University, Stellenbosch, South Africa

    Jacky Snoep

  10. Medical Informatics, University Medicine Greifswald, Greifswald, Germany

    Dagmar Waltemath

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  1. Natalie J. Stanford
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    Stephen G. Oliver

  2. Genetadi Biotech, Parque Tecnológico de Bizkaia, Biscay, Spain

    Juan I. Castrillo

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Stanford, N.J. et al. (2019). Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices. In: Oliver, S.G., Castrillo, J.I. (eds) Yeast Systems Biology. Methods in Molecular Biology, vol 2049. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9736-7_17

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  • DOI: https://doi.org/10.1007/978-1-4939-9736-7_17

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9735-0

  • Online ISBN: 978-1-4939-9736-7

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