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

AI Article Synopsis

  • Computational systems biology combines diverse datasets to create models that help in understanding and predicting biological processes.
  • The process involves collaboration among researchers, where data is shared and integrated into these models while ensuring that the data is FAIR—findable, accessible, interoperable, and reusable.
  • The chapter discusses standards for structuring, describing, and sharing data and models, as well as highlights key sharing databases in the field.

Article 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.

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Source
http://dx.doi.org/10.1007/978-1-4939-9736-7_17DOI Listing

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