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Best Practices for Making Reproducible Biochemical Models.

Veronica L Porubsky¹, Arthur P Goldberg², Anand K Rampadarath³

¹Department of Bioengineering, University of Washington, Seattle, WA 98105, USA.

|August 28, 2020

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Reproducible biochemical modeling requires following best practices for data, model descriptions, and software. This ensures models are understandable, trustworthy, and reusable, advancing scientific knowledge.

Area of Science:

Biochemistry
Computational Biology
Systems Biology

Background:

Dynamical biochemical modeling is crucial for understanding cellular processes.
Irreproducible results hinder the utility, trust, and reuse of biochemical models.
Lack of standardization limits collaborative research and knowledge advancement.

Purpose of the Study:

To outline best practices for creating reproducible biochemical model artifacts.
To guide modelers in enhancing the understandability, trustworthiness, and reusability of their models.
To promote the adoption of standards in biochemical modeling workflows.

Main Methods:

Comprehensive review of biochemical modeling workflows.
Identification of best practices for data collection, model construction, training, simulation, and validation.

Keywords:

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

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Guidance on sharing model artifacts, including data, descriptions, and software.

Emphasis on the use of standard tools and formats.

Main Results:

A detailed list of best practices covering the entire modeling lifecycle.
Strategies for ensuring reproducibility even when full standardization is not feasible.
Recommendations for public sharing of model artifacts to foster reuse.
Highlighting the benefits of using standard tools and formats.

Conclusions:

Adopting these best practices significantly improves the reproducibility of biochemical models.
Enhanced reproducibility facilitates better understanding, trust, and reuse of models.
This work aims to advance the field of biochemical modeling by promoting robust and shareable research artifacts.