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Evolution of computational models in BioModels Database and the Physiome Model Repository

机译：生物模型数据库和生理组模型库中计算模型的演变

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A useful model is one that is being (re)used. The development of a successful model does not finish with its publication. During reuse, models are being modified, i.e. expanded, corrected, and refined. Even small changes in the encoding of a model can, however, significantly affect its interpretation. Our motivation for the present study is to identify changes in models and make them transparent and traceable. We analysed 13734 models from BioModels Database and the Physiome Model Repository. For each model, we studied the frequencies and types of updates between its first and latest release. To demonstrate the impact of changes, we explored the history of a Repressilator model in BioModels Database. We observed continuous updates in the majority of models. Surprisingly, even the early models are still being modified. We furthermore detected that many updates target annotations, which improves the information one can gain from models. To support the analysis of changes in model repositories we developed MoSt, an online tool for visualisations of changes in models. The scripts used to generate the data and figures for this study are available from GitHub https://github.com/binfalse/BiVeS-StatsGenerator and as a Docker image at https://hub.docker.com/r/binfalse/bives-statsgenerator/ . The website https://most.bio.informatik.uni-rostock.de/ provides interactive access to model versions and their evolutionary statistics. The reuse of models is still impeded by a lack of trust and documentation. A detailed and transparent documentation of all aspects of the model, including its provenance, will improve this situation. Knowledge about a model’s provenance can avoid the repetition of mistakes that others already faced. More insights are gained into how the system evolves from initial findings to a profound understanding. We argue that it is the responsibility of the maintainers of model repositories to offer transparent model provenance to their users.
机译：一种有用的模型是正在（重新）使用的模型。一个成功的模型的开发并没有随着它的发布而结束。在重用期间，对模型进行修改，即扩展，校正和改进。但是，即使模型的编码发生很小的变化也会极大地影响其解释。我们进行本研究的动机是识别模型中的更改，并使它们透明且可追溯。我们分析了来自BioModels数据库和Physiome模型库的13734个模型。对于每种模型，我们研究了其第一版和最新版之间的更新频率和类型。为了证明变化的影响，我们在BioModels数据库中探索了Repressilator模型的历史。我们观察到大多数模型都在不断更新。令人惊讶的是，甚至早期的模型仍在修改中。此外，我们检测到许多更新都针对目标注释，从而改善了人们可以从模型中获取的信息。为了支持对模型库更改的分析，我们开发了MoSt，这是一种在线工具，用于可视化模型更改。可以从GitHub https://github.com/binfalse/BiVeS-StatsGenerator获得用于生成本研究数据和图形的脚本，也可以在https://hub.docker.com/r/binfalse/bives上将其作为Docker映像使用。 -statsgenerator /。网站https://most.bio.informatik.uni-rostock.de/提供了对模型版本及其演化统计信息的交互式访问。缺乏信任和文档仍然阻碍了模型的重用。有关模型各个方面（包括其来源）的详细，透明的文档将改善这种情况。有关模型出处的知识可以避免重复别人已经遇到的错误。从系统的初步发现到深刻理解，我们获得了更多的见识。我们认为，模型存储库的维护者有责任向其用户提供透明的模型出处。

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