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Pathirana, D.* ; Bergmann, F.* ; Dorešić, D.* ; Lakrisenko, P. ; Persson, S.* ; Neubrand, N.* ; Timmer, J.* ; Kreutz, C.* ; Binder, H.* ; Cvijović, M.* ; Weindl, D. ; Hasenauer, J.

PEtab Select: Specification standard and supporting software for automated model selection.

PLoS Comput. Biol. 22:e1014774 (2026)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
ISSN (print) / ISBN 1553-734X
e-ISSN 1553-7358
Zeitschrift PLoS Computational Biology
Quellenangaben Band: 22, Heft: 9, Seiten: , Artikelnummer: e1014774 Supplement: ,
Verlag Public Library of Science (PLoS)
Verlagsort 1160 Battery Street, Ste 100, San Francisco, Ca 94111 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen German Network for Bioinformatics Infrastructure - de.NBI
HORIZON EUROPE European Research Council
Stiftelsen fr Strategisk Forskning
Deutsche Forschungsgemeinschaft
Vetenskapsrdet
Bundesministerium fr Bildung und Forschung