Porubsky Veronica L, Goldberg Arthur P, Rampadarath Anand K, Nickerson David P, Karr Jonathan R, Sauro Herbert M
Department of Bioengineering, University of Washington, Seattle, WA 98105, USA.
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Cell Syst. 2020 Aug 26;11(2):109-120. doi: 10.1016/j.cels.2020.06.012.
Like many scientific disciplines, dynamical biochemical modeling is hindered by irreproducible results. This limits the utility of biochemical models by making them difficult to understand, trust, or reuse. We comprehensively list the best practices that biochemical modelers should follow to build reproducible biochemical model artifacts-all data, model descriptions, and custom software used by the model-that can be understood and reused. The best practices provide advice for all steps of a typical biochemical modeling workflow in which a modeler collects data; constructs, trains, simulates, and validates the model; uses the predictions of a model to advance knowledge; and publicly shares the model artifacts. The best practices emphasize the benefits obtained by using standard tools and formats and provides guidance to modelers who do not or cannot use standards in some stages of their modeling workflow. Adoption of these best practices will enhance the ability of researchers to reproduce, understand, and reuse biochemical models.
与许多科学学科一样，动态生化建模受到不可重复结果的阻碍。这使得生化模型难以理解、信任或重用，从而限制了其效用。我们全面列出了生化模型构建者应遵循的最佳实践，以构建可重复的生化模型工件——模型所使用的所有数据、模型描述和定制软件——这些工件应易于理解和重用。这些最佳实践为典型生化建模工作流程的所有步骤提供建议，在该工作流程中，建模者收集数据；构建、训练、模拟和验证模型；利用模型的预测来推进知识；并公开共享模型工件。最佳实践强调使用标准工具和格式所带来的好处，并为那些在建模工作流程的某些阶段未使用或无法使用标准的建模者提供指导。采用这些最佳实践将提高研究人员复制、理解和重用生化模型的能力。
