Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA, USA.
Department of Bioengineering, University of Washington, Seattle, WA, USA.
Curr Opin Biotechnol. 2023 Jun;81:102922. doi: 10.1016/j.copbio.2023.102922. Epub 2023 Mar 31.
The reproducibility of scientific research is crucial to the success of the scientific method. Here, we review the current best practices when publishing mechanistic models in systems biology. We recommend, where possible, to use software engineering strategies such as testing, verification, validation, documentation, versioning, iterative development, and continuous integration. In addition, adhering to the Findable, Accessible, Interoperable, and Reusable modeling principles allows other scientists to collaborate and build off of each other's work. Existing standards such as Systems Biology Markup Language, CellML, or Simulation Experiment Description Markup Language can greatly improve the likelihood that a published model is reproducible, especially if such models are deposited in well-established model repositories. Where models are published in executable programming languages, the source code and their data should be published as open-source in public code repositories together with any documentation and testing code. For complex models, we recommend container-based solutions where any software dependencies and the run-time context can be easily replicated.
科学研究的可重复性对于科学方法的成功至关重要。在这里,我们回顾了当前在系统生物学中发布机械模型的最佳实践。我们建议在可能的情况下使用软件工程策略,如测试、验证、确认、文档编制、版本控制、迭代开发和持续集成。此外,遵守可发现性、可访问性、互操作性和可重用性建模原则可以允许其他科学家进行协作并在彼此的工作基础上进行构建。现有的标准,如系统生物学标记语言、CellML 或仿真实验描述标记语言,可以大大提高发布模型可重复性的可能性,特别是如果这些模型被存储在成熟的模型存储库中。如果模型以可执行编程语言发布,则应将源代码及其数据与任何文档和测试代码一起作为开源发布到公共代码存储库中。对于复杂模型,我们建议使用基于容器的解决方案,其中任何软件依赖项和运行时上下文都可以轻松复制。