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An algorithm to detect and communicate the differences in computational models describing biological systems.

Martin Scharm1, Olaf Wolkenhauer2, Dagmar Waltemath1

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Summary
This summary is machine-generated.

A new algorithm and open-source library, BiVeS, accurately detects differences between computational model versions. This enhances model reuse, reproducibility, and provenance tracking in repositories.

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Area of Science:

  • Computational biology
  • Systems biology
  • Bioinformatics

Background:

  • Model repositories are crucial for reusing computational models and ensuring publication transparency.
  • Thousands of models exist in repositories like BioModels, necessitating tools for version comparison and combination.
  • Current repositories lack robust algorithms for tracking model evolution and detecting discrepancies.

Purpose of the Study:

  • To develop and implement an algorithm for detecting and describing differences between computational model versions.
  • To provide a framework for tracking model development, aiding in error detection and inconsistency identification.
  • To facilitate model reuse, extension, and collaborative efforts in systems biology.

Main Methods:

  • Developed an algorithm focusing on Systems Biology Markup Language (SBML) and CellML formats.
  • Implemented the algorithm in an open-source library named BiVeS.
  • Demonstrated BiVeS capabilities through a web interface, BudHat.

Main Results:

  • The BiVeS algorithm accurately detects and describes differences in model encoding, biological network structure, and mathematical expressions.
  • BiVeS aids in identifying and characterizing changes, contributing to model history documentation.
  • Facilitates better reproducibility of modeling results and addresses model provenance challenges.

Conclusions:

  • BiVeS enhances the usability and reliability of computational models in repositories.
  • The tool supports collaborative modeling and improves the transparency of scientific publications.
  • Contributes significantly to the long-term management and understanding of complex biological models.