Making Models That Matter: How to Build Trustworthy and Useful Systems Biology Models
Abstract
Computational models supporting mechanistic understanding of (complex) biological systems, systems behaviour prediction, and experimental design are becoming more and more embedded in research on complex biological systems. Reuse and refinement of models, rather than continuous reinvention, is becoming increasingly important as models' demands on computational infrastructure increase. However published models - despite the variety of efforts taken so far - are frequently difficult to reproduce or reuse, substantially limiting their scientific value. Here we address the requirements for model reusability in the light of the field-specific CURE framework (Credible, Understandable, Reproducible, Extensible) and the more general FAIR principles (Findable, Accessible, Interoperable, Reusable). Considering published guidance we identify broad agreement on requirements for findability, accessibility, and interoperability, but continued lack of clarity and consensus around reusability. Focusing on the scientific quality and usability of computational models we discuss six key practices underpinning model sharing and re-use. Mapping the FAIR and CURE principles onto the model lifecycle we propose ten recommendations for building and sharing systems biology models that are both FAIR- and CURE-compliant.
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BibTeX
@misc{hancock2026making,
title = {{Making Models That Matter: How to Build Trustworthy and Useful Systems Biology Models}},
author = {John M. Hancock and Mihail Anton and Frank T. Bergmann and Irina Balaur and Carissa Bleker and Thomas C. Collin and Oscar Dias and Elena Dominguez-Romero and Chris Evelo and Gavin Farrell and Martina Kutmon and Vitor Martins dos Santos and Anna Matuszynska and Sebastien Moretti and Sara Morsy and Anna Niarakis and Marek Ostaszewski and Miguel Rocha and David Safranek and William Scott and Rahuman Sheriff and Silvio Tosatto and Dagmar Waltemath and Ulrike Wittig and Jan Zrimec and Anze Zupanic},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.13127},
eprint = {2609.13127},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.13127},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Making Models That Matter: How to Build Trustworthy and Useful Systems Biology Models
AU - John M. Hancock
AU - Mihail Anton
AU - Frank T. Bergmann
AU - Irina Balaur
AU - Carissa Bleker
AU - Thomas C. Collin
AU - Oscar Dias
AU - Elena Dominguez-Romero
AU - Chris Evelo
AU - Gavin Farrell
AU - Martina Kutmon
AU - Vitor Martins dos Santos
AU - Anna Matuszynska
AU - Sebastien Moretti
AU - Sara Morsy
AU - Anna Niarakis
AU - Marek Ostaszewski
AU - Miguel Rocha
AU - David Safranek
AU - William Scott
AU - Rahuman Sheriff
AU - Silvio Tosatto
AU - Dagmar Waltemath
AU - Ulrike Wittig
AU - Jan Zrimec
AU - Anze Zupanic
PY - 2026
DA - 2026/09/11
PB - arXiv
DO - 10.48550/arxiv.2609.13127
UR - https://arxiv.org/abs/2609.13127
AB - Computational models supporting mechanistic understanding of (complex) biological systems, systems behaviour prediction, and experimental design are becoming more and more embedded in research on complex biological systems. Reuse and refinement of models, rather than continuous reinvention, is becoming increasingly important as models' demands on computational infrastructure increase. However published models - despite the variety of efforts taken so far - are frequently difficult to reproduce or reuse, substantially limiting their scientific value. Here we address the requirements for model reusability in the light of the field-specific CURE framework (Credible, Understandable, Reproducible, Extensible) and the more general FAIR principles (Findable, Accessible, Interoperable, Reusable). Considering published guidance we identify broad agreement on requirements for findability, accessibility, and interoperability, but continued lack of clarity and consensus around reusability. Focusing on the scientific quality and usability of computational models we discuss six key practices underpinning model sharing and re-use. Mapping the FAIR and CURE principles onto the model lifecycle we propose ten recommendations for building and sharing systems biology models that are both FAIR- and CURE-compliant.
ER -