Standards, dissemination, and best practices in systems biology

Abstract

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.

Figure 1:

Repeatability and reproducibility lie on a spectrum. Using more standards like SBML, SED-ML, or COMBINE archive files, and good dissemination practices (publishing code in a open-source repository and annotating the data) increases the reproducibility of models.

Figure 2:

Strategies for making findable, accessible, interoperable, and reusable models.

Figure 3:

Models can be scored on a scale of 10, where 0 means not reproducible and 10 means fully reproducible. Points are awarded if the models meet the listed criteria.