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Exploring the evolution of biochemical models at the network level.

Tom Gebhardt1, Vasundra Touré2, Dagmar Waltemath3

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Tracking biochemical model evolution is challenging. A new method visualizes network-level differences between model versions, aiding collaborative development and alignment.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Tracking the evolution of biochemical models is difficult due to non-linear, collaborative development processes.
  • Current tools can detect differences between model versions but do not visualize structural changes at the network level.

Purpose of the Study:

  • To develop a method for effectively visualizing structural differences between biochemical model versions.
  • To facilitate the discussion and alignment of model versions in collaborative research.

Main Methods:

  • Developed a JSON schema to represent network-level differences.
  • Extended the BiVeS software tool to incorporate the JSON schema.
  • Created DiVil, a web-based tool using D3.js for interactive network visualization of model differences.

Main Results:

  • The method effectively communicates structural differences between model versions.
  • DiVil provides an interactive interface with automatic layout for improved visualization.
  • The network visualizations can be exported in standardized formats.

Conclusions:

  • The developed method and DiVil tool support the collaborative and non-linear nature of biochemical model development.
  • Effective visualization of network-level differences enhances understanding and discussion of model changes.