From FAIR to CURE: guidelines for computational models of biological systems

Guidelines for managing scientific data have been established under the FAIR principles, requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility; the clarity of model descriptions and annotations for understandability; adherence to standards and open science practices for reproducibility; and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community should strive to automate as many of the guidelines as possible.

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Publication Details

Journal
npj Systems Biology and Applications
Published
2026-03-27
DOI
https://doi.org/10.1038/s41540-026-00651-0
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
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article

From FAIR to CURE: guidelines for computational models of biological systems

Thomas E. Gorochowski, Fabian Fröhlich, John H. Gennari, Matthias König et al.
npj Systems Biology and Applications
Scientific Computing and Data Management
article

From FAIR to CURE: guidelines for computational models of biological systems

Thomas E. Gorochowski, Fabian Fröhlich, John H. Gennari, Matthias König, Leslie M. Loew, James R. Faeder, Jacky L. Snoep, James C. Schaff, James A. Glazier, Andrew D. McCulloch, Eran Agmon, Lealem Mulugeta, Rajanikanth Vadigepalli, Herbert M. Sauro, Sarah Keating, Anna Niarakis, Brett G. Olivier, Pedro Mendes, Lucian P. Smith, Jerry G. Myers, Carlos Fernandez Lopez, Nicole Erika Radde, Bartholomew Jardine, Patrick M. Boyle, Johann M. Rohwer, William W. Lytton, David Phillip Nickerson, Reinhard Laubenbacher, Janis Shin, Song Feng, Peter J. Hunter, Michael L. Blinov, Ion Moraru, Tomáš Helikar, Chris J. Myers, Elebeoba E. May, Alexander A. Patrie, Adel Heydarabadipour, J. Freire, Andreas Dräger, H. Steven Wiley, Princess I. Imoukhuede, Ellen M. Quardokus, Stefan Hoops, Henning Hermjakob, Gary D. Bader, Falk Schreiber, Frank T. Bergmann, David D. van Niekerk, T. J. Sego, Rahuman S. Malik-Sheriff, Dagmar Waltemath, Sven Sahle, Joseph L. Hellerstein
article en

Abstract

Guidelines for managing scientific data have been established under the FAIR principles, requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility; the clarity of model descriptions and annotations for understandability; adherence to standards and open science practices for reproducibility; and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community should strive to automate as many of the guidelines as possible.

npj Systems Biology and Applications
University of Stuttgart (DE), Cañada College (US), University of Nebraska–Lincoln (US), European Bioinformatics Institute (GB), Université Toulouse III - Paul Sabatier (FR), University of Wisconsin–Madison (US), Pacific Northwest National Laboratory (US), Thomas Jefferson University (US), University of Auckland (NZ), University of Pittsburgh (US), SUNY Downstate Health Sciences University (US), University of Toronto (CA), University of Colorado Boulder (US), University of Konstanz (DE), University of Washington (US), Heidelberg University (DE), Stellenbosch University (ZA), Universitätsmedizin Greifswald (DE), Glenn Research Center (US), The Francis Crick Institute (GB), Environmental Molecular Sciences Laboratory (US), University of Washington Medical Center (US), Wisconsin Institutes for Discovery (US), University of Florida (US), University of Bristol (GB), University of California San Diego (US), Humboldt-Universität zu Berlin (DE), MED Institute (US), Kings County Hospital Center (US), Insilicos (United States) (US), Indiana University Bloomington (US), Centre Inria de Saclay (FR), Biocom (US), University College London (GB), University of Virginia (US), Monash University (AU), New York University (US), Martin Luther University Halle-Wittenberg (DE), UConn Health (US), Heidelberg University (US), Vrije Universiteit Amsterdam (NL)
Openalex Percentile: Top 5%
Scientific Computing and Data Management
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