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

Authors :
Scharm M
Wolkenhauer O
Waltemath D
Source :
Bioinformatics (Oxford, England) [Bioinformatics] 2016 Feb 15; Vol. 32 (4), pp. 563-70. Date of Electronic Publication: 2015 Oct 21.
Publication Year :
2016

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.<br />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.<br />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.<br /> (© The Author 2015. Published by Oxford University Press.)

Details

Language :
English
ISSN :
1367-4811
Volume :
32
Issue :
4
Database :
MEDLINE
Journal :
Bioinformatics (Oxford, England)
Publication Type :
Academic Journal
Accession number :
26490504
Full Text :
https://doi.org/10.1093/bioinformatics/btv484