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

Natalie J Stanford¹, Martin Scharm², Paul D Dobson¹

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

Methods in Molecular Biology (Clifton, N.J.)

|October 12, 2019

Summary

This summary is machine-generated.

Computational systems biology relies on FAIR data and models for seamless integration and prediction. This involves using established standards and databases for findability, accessibility, interoperability, and reusability.

Keywords:

Data storage Databases FAIR Metadata Model storage Reproducible research Standards

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Area of Science:

Computational systems biology
Bioinformatics
Data Science

Background:

Integrating diverse datasets is crucial for building predictive models in computational systems biology.
Data and model sharing among researchers is essential for collaborative model construction.
Ensuring data and models are FAIR (Findable, Accessible, Interoperable, Reusable) is key for effective research.

Purpose of the Study:

To explore data and model standards that facilitate structuring, describing, and sharing in computational systems biology.
To highlight popular standards and databases used for data and model sharing in the field.

Main Methods:

Review of existing literature on data and model standards in computational systems biology.
Identification and discussion of commonly used standards and sharing platforms.
Analysis of FAIR principles in the context of data and model management.

Main Results:

Various data and model standards exist to improve the FAIRness of research outputs.
Specific databases and standards are widely adopted within the computational systems biology community.
Consistent structuring and clear descriptions are vital for data and model interoperability.

Conclusions:

Adherence to data and model standards is essential for advancing computational systems biology.
The use of standardized formats and accessible databases enhances research reproducibility and collaboration.
Promoting FAIR data principles empowers researchers to effectively share and reuse biological data and models.