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

机译:一种检测并传达描述生物系统的计算模型中的差异的算法

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摘要

>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.>Contact: >Supplementary information: are available at Bioinformatics online.
机译:>动机:存储库支持模型的重用,并确保与这些模型链接的出版物中结果的透明性。诸如BioModels数据库或Physiome Model Repository之类的存储库中有成千上万的模型可用,跟踪模型及其版本之间差异的框架对于比较和组合模型至关重要。差异检测不仅允许用户研究模型的历史,而且还有助于检测错误和不一致。现有存储库缺少算法来跟踪模型随时间的发展。>结果:针对SBML和CellML,我们提出了一种算法,该算法可以准确地检测和描述模型的共存版本之间关于(i)模型的编码,(ii)生物网络的结构和(iii)数学表达式。该算法在称为BiVeS的全面开放源代码库中实现。 BiVeS有助于识别和表征计算模型的变化,从而有助于记录模型的历史记录。我们的工作促进了现有模型的重用和扩展,并支持协作建模。最后,它有助于提高建模结果的可重复性,并为模型出处带来挑战。>可用性和实现:本文中描述的工作流程是在BiVeS中实现的。 BiVeS可从sems.uni-rostock.de免费获得,可作为源代码和二进制文件获得。 Web界面BudHat在budhat.sems.uni-rostock.de上展示了BiVeS的功能。>联系方式: >补充信息:可在在线生物信息学中获得。

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