PEtab Select: Specification standard and supporting software for automated model selection
Journal article, 2026
A central question in mathematical modeling of biological systems is determining which processes are most relevant and how they can be described. There are often competing hypotheses, which yield different models. Model comparison requires parameter optimization and sampling methods. Yet, standards for the specification of model selection problems and the swift evaluation of a broad spectrum of approaches are not available. PEtab Select addresses this challenge by providing a concise, standardized specification of model selection and its associated calibration problems through a new file format standard and software package. The standard facilitates the compact representation of even very large model selection problems; in one example, billions of model alternatives. PEtab Select builds on the PEtab standard for the specification of parameter estimation problems, and enables the use of state-of-the-art modelling and calibration workflows utilizing COPASI, Data2Dynamics, PEtab.jl, and pyPESTO. PEtab Select supports common model selection criteria (e.g., Akaike and Bayesian information criteria) and can be easily extended to use others. To ensure flexibility, PEtab Select implements several model space exploration approaches, including basic brute-force, forward, and backward selection, and also advanced, flexible selection methods. PEtab Select introduces the first standardization of model selection tasks, filling a critical gap in existing computational pipelines. It constitutes an essential contribution to FAIR research software in systems biology by promoting interoperability and reusability in model selection.
Author
Dilan Pathirana
University of Bonn
Frank T. Bergmann
Heidelberg University
Domagoj Doresic
University of Bonn
Polina Lakrisenko
University of Bonn
Helmholtz Association of German Research Centres
Technical University of Munich
Sebastian Persson
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
University of Gothenburg
Niklas Neubrand
University of Freiburg
Jens Timmer
University of Freiburg
Clemens Kreutz
University of Freiburg
Harald Binder
University of Freiburg
Marija Cvijovic
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
University of Gothenburg
Daniel Weindl
University of Bonn
Helmholtz Association of German Research Centres
Jan Hasenauer
University of Bonn
Helmholtz Association of German Research Centres
Technical University of Munich
PLoS Computational Biology
1553-734X (ISSN) 1553-7358 (eISSN)
Vol. 22 9 e1014774Subject Categories (SSIF 2025)
Probability Theory and Statistics
Computer Sciences
DOI
10.1371/journal.pcbi.1014774
PubMed
42814919
Related datasets
Supporting Information [dataset]
DOI: 10.5281/zenodo.17185040