Best Practices for Making Reproducible Biochemical Models

Veronica L. Porubsky, Arthur P. Goldberg, Anand K. Rampadarath, David P. Nickerson, Jonathan R. Karr, Herbert M. Sauro

Research output: Contribution to journalReview articlepeer-review

20 Scopus citations


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. Irreproducible biochemical models limit understanding, trust, and reuse. They impede scientific discovery and medical advancements which require reliable, accurate, and predictive biochemical models. We have outlined several best practices to assist modelers in building reproducible biochemical models to overcome these obstacles. These best practices provide advice covering all stages of a typical biochemical modeling workflow, can be implemented as a whole or in part, and consider both standardized and general-purpose methods and tools—enabling flexible adoption by the modeler.

Original languageEnglish
Pages (from-to)109-120
Number of pages12
JournalCell Systems
Issue number2
StatePublished - 26 Aug 2020


  • COmputational Modeling in BIology NEtwork
  • FAIR principles
  • biochemical models
  • modeling
  • reproducibility
  • standards
  • systems biology


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