PEtab Select: Specification standard and supporting software for automated model selection
Artikel i vetenskaplig tidskrift, 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.
Författare
Dilan Pathirana
Universität Bonn
Frank T. Bergmann
Universität Heidelberg
Domagoj Doresic
Universität Bonn
Polina Lakrisenko
Universität Bonn
Helmholtz-Gemeinschaft Deutscher Forschungszentren
Technische Universität München
Sebastian Persson
Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik
Göteborgs universitet
Niklas Neubrand
Albert-Ludwigs-Universität Freiburg
Jens Timmer
Albert-Ludwigs-Universität Freiburg
Clemens Kreutz
Albert-Ludwigs-Universität Freiburg
Harald Binder
Albert-Ludwigs-Universität Freiburg
Marija Cvijovic
Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik
Göteborgs universitet
Daniel Weindl
Universität Bonn
Helmholtz-Gemeinschaft Deutscher Forschungszentren
Jan Hasenauer
Universität Bonn
Helmholtz-Gemeinschaft Deutscher Forschungszentren
Technische Universität München
PLoS Computational Biology
1553-734X (ISSN) 1553-7358 (eISSN)
Vol. 22 9 e1014774Ämneskategorier (SSIF 2025)
Sannolikhetsteori och statistik
Datavetenskap (datalogi)
DOI
10.1371/journal.pcbi.1014774
PubMed
42814919
Relaterade dataset
Supporting Information [dataset]
DOI: 10.5281/zenodo.17185040