An Algorithm to Detect and Communicate the Differences in Computational Models Describing Biological Systems

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2015 Oct 23

PMID 26490504

Citations 13

Authors

Martin Scharm

Olaf Wolkenhauer

Dagmar Waltemath

Affiliations

Soon will be listed here.

Abstract

Motivation: Repositories support the reuse of models and ensure transparency about results in publications linked to those models. With thousands of models available in repositories, such as the BioModels database or the Physiome Model Repository, a framework to track the differences between models and their versions is essential to compare and combine models. Difference detection not only allows users to study the history of models but also helps in the detection of errors and inconsistencies. Existing repositories lack algorithms to track a model's development over time.

Results: Focusing on SBML and CellML, we present an algorithm to accurately detect and describe differences between coexisting versions of a model with respect to (i) the models' encoding, (ii) the structure of biological networks and (iii) mathematical expressions. This algorithm is implemented in a comprehensive and open source library called BiVeS. BiVeS helps to identify and characterize changes in computational models and thereby contributes to the documentation of a model's history. Our work facilitates the reuse and extension of existing models and supports collaborative modelling. Finally, it contributes to better reproducibility of modelling results and to the challenge of model provenance.

Availability And Implementation: The workflow described in this article is implemented in BiVeS. BiVeS is freely available as source code and binary from sems.uni-rostock.de. The web interface BudHat demonstrates the capabilities of BiVeS at budhat.sems.uni-rostock.de.

Citing Articles

Systems Biology in ELIXIR: modelling in the spotlight.

Martins Dos Santos V, Anton M, Szomolay B, Ostaszewski M, Arts I, Benfeitas R F1000Res. 2024; 11.

PMID: 36742342 PMC: 9871403. DOI: 10.12688/f1000research.126734.2.

Exploring the evolution of biochemical models at the network level.

Gebhardt T, Toure V, Waltemath D, Wolkenhauer O, Scharm M PLoS One. 2022; 17(3):e0265735.

PMID: 35312734 PMC: 8936491. DOI: 10.1371/journal.pone.0265735.

Relating simulation studies by provenance-Developing a family of Wnt signaling models.

Budde K, Smith J, Wilsdorf P, Haack F, Uhrmacher A PLoS Comput Biol. 2021; 17(8):e1009227.

PMID: 34351901 PMC: 8407594. DOI: 10.1371/journal.pcbi.1009227.

A consensus-based and readable extension of near de for eaction ules (LiCoRR).

Kellman B, Zhang Y, Logomasini E, Meinhardt E, Godinez-Macias K, Chiang A Beilstein J Org Chem. 2020; 16:2645-2662.

PMID: 33178355 PMC: 7607430. DOI: 10.3762/bjoc.16.215.

SBML Level 3: an extensible format for the exchange and reuse of biological models.

Keating S, Waltemath D, Konig M, Zhang F, Drager A, Chaouiya C Mol Syst Biol. 2020; 16(8):e9110.

PMID: 32845085 PMC: 8411907. DOI: 10.15252/msb.20199110.

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