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

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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Publication Details

Journal
PLoS Computational Biology
Published
2026-09-30
DOI
https://doi.org/10.1371/journal.pcbi.1014774
Primary Topic
Cell Image Analysis Techniques
Type
article
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article

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

Niklas Neubrand, Daniel Weindl, Marija Cvijović, Dilan Pathirana et al.
PLoS Computational Biology
Cell Image Analysis Techniques
article

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

Niklas Neubrand, Daniel Weindl, Marija Cvijović, Dilan Pathirana, Jan Hasenauer, Domagoj Dorešić, Frank Bergmann, Clemens Kreutz, Sebastian Persson, Polina Lakrisenko, Harald Binder, with the PEtab Select community, Jens Timmer
article en

Abstract

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.

PLoS Computational BiologyVol. 22(9)
University of Bonn (DE), University of Freiburg (DE), Heidelberg University (DE), Helmholtz Zentrum München (DE), Technical University of Munich (DE), Chalmers University of Technology (SE)
Partnerships for the goals
Openalex Percentile: Top 13%
Cell Image Analysis Techniques
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