Exploring the evolution of biochemical models at the network level

PLoS One. 2022 Mar 21;17(3):e0265735. doi: 10.1371/journal.pone.0265735. eCollection 2022.

Abstract

The evolution of biochemical models is difficult to track. At present, it is not possible to inspect the differences between model versions at the network level. Biochemical models are often constructed in a distributed, non-linear process: collaborators create model versions on different branches from novel information, model extensions, during curation and adaption. To discuss and align the versions, it is helpful to abstract the changes to the network level. The differences between two model versions can be detected by the software tool BiVeS. However, it cannot show the structural changes resulting from the differences. Here, we present a method to visualise the differences between model versions effectively. We developed a JSON schema to communicate the differences at the network level and extended BiVeS accordingly. Additionally, we developed DiVil, a web-based tool to represent the model and the differences as a standardised network using D3. It combines an automatic layout with an interactive user interface to improve the visualisation and to inspect the model. The network can be exported in standardised formats as images or markup language. Our method communicates the structural differences between model versions. It facilitates the discussion of changes and thus supports the collaborative and non-linear nature of model development. Availability and implementation: DiVil prototype: https://divil.bio.informatik.uni-rostock.de, Code on GitHub: https://github.com/Gebbi8/DiVil, licensed under Apache License 2.0. Contact: url="tom.gebhardt@uni-rostock.de.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Software*

Grants and funding

This work was started as part of SEMS (funded by the German Federal Ministry of Education and Research in the e:Bio programme SEMS, FKZ 031 6194). Maintenance and further development were part of INCOME (funded by the German Federal Ministry of Education and Research in the e:Med programme FKZ 01ZX1705C) and EU-Stands4PM (funded by the Horizon2020 framework programme, Grant Agreement #825843). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.