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A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

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Published on: February 8, 2017

Version control of pathway models using XML patches.

Peter Saffrey1, Richard Orton

  • 1Department of Computing Science, University of Glasgow, Glasgow, G12 8QQ, UK. pzs@dcs.gla.ac.uk

BMC Systems Biology
|March 19, 2009
PubMed
Summary
This summary is machine-generated.

Managing complex biological pathway models requires specialized version control. This study introduces an XML patch-based method for efficient model versioning, simplifying development and reducing errors.

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Area of Science:

  • Computational biology
  • Systems biology
  • Bioinformatics

Background:

  • Computational modeling is crucial for understanding complex biological systems like signaling pathways.
  • Increasing model complexity necessitates effective techniques for managing model versions and their interrelationships.
  • Pathway model version control differs from software version control, requiring a tailored solution.

Purpose of the Study:

  • To develop and present a novel method for controlling versions of biological pathway models.
  • To address the challenges posed by the increasing size and complexity of computational biological models.

Main Methods:

  • A model version control method based on XML patches.
  • A prototype implementation of the proposed method.
  • Application of the method to the Epidermal Growth Factor Receptor (EGFR)/RAS/RAF signaling pathway.

Main Results:

  • The developed method facilitates rapid and user-friendly storage of diverse model variations.
  • The system allows for detailed explanations and documentation of these model variations.
  • Demonstrated successful application to a specific biological pathway.

Conclusions:

  • The proposed XML patch-based method offers an efficient solution for managing biological model versions.
  • This approach significantly streamlines development, reducing frustration and human error associated with manual methods.
  • Enables thorough documentation and understanding of model evolution in computational biology.