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

Natalie J. Stanford, Martin Scharm, Paul D. Dobson, Martin Golebiewski, Michael Hucka, Varun B. Kothamachu, David Nickerson, Stuart Owen, Jürgen Pahle*, Ulrike Wittig, Dagmar Waltemath, Carole Goble, Pedro Mendes, Jacky Snoep

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

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.

Original languageEnglish
Title of host publicationYeast Systems Biology
Subtitle of host publicationMethods and Protocols
EditorsStephen G. Oliver, Juan I. Castrillo
PublisherHumana Press Inc
Pages285-314
Number of pages30
ISBN (Electronic)9781493997367
ISBN (Print)9781493997350, 9781493997381
DOIs
Publication statusPublished - 2019

Publication series

NameMethods in Molecular Biology
Volume2049
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Keywords

  • Data storage
  • Databases
  • FAIR
  • Metadata
  • Model storage
  • Reproducible research
  • Standards

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