1100 synthetic benchmark problems for dynamic modeling of cellular processes.
4 affiliations
- 1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg 79104, Germany.
- 2Center for Data Analysis, Modeling and AI (FDMAI), University of Freiburg, Freiburg 79104, Germany.
- 3Institute of Physics, University of Freiburg, Freiburg 79104, Germany.
- 4Centre for Integrative Biological Signalling Studies (CIBSS), University of Freiburg, Freiburg 79104, Germany.
Abstract
Motivation
Systems biology strives to unravel the complex dynamics of cellular processes using ordinary differential equations. However, the sparsity of measured data and the strong nonlinearity of common ordinary differential equations introduce severe numerical problems in modeling tasks. This motivated the development of computational algorithms that must be systematically evaluated to ensure optimal method choices. The number of well-curated models for such benchmarking efforts is insufficient, as building and calibrating biologically reasonable models from experiments requires years of work.Results
We present a large-scale collection of 1100 synthetic modeling problems, generated based on the systems of ordinary differential equations and experimental designs of 22 published modeling problems. This is achieved by extending a recent method for simulation of time-course data for randomly generated observation functions to also include realistic measurement patterns across multiple experimental conditions. By analyzing data and model characteristics, optimization performance, and parameter identifiability, we show that the synthetic problems provide both a realistic and diverse extension of the existing problem space. Hence, the synthetic collection provides a valuable resource for benchmarking in dynamic modeling.Availability and implementation
Benchmark problems and algorithm are publicly available at https://github.com/niklasneubrand/1100SyntheticBenchmarksODE and https://doi.org/10.5281/zenodo.23211070.
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BibTeX
@article{neubrand20261100,
title = {{1100 synthetic benchmark problems for dynamic modeling of cellular processes.}},
author = {Niklas Neubrand and Timo Rachel and Tim Litwin and Jens Timmer and Clemens Kreutz and Moritz Hess},
journal = {Bioinformatics advances},
year = {2026},
month = oct,
doi = {10.1093/bioadv/vbag307},
url = {https://europepmc.org/article/MED/42948737},
note = {CC BY},
}
RIS
TY - JOUR
TI - 1100 synthetic benchmark problems for dynamic modeling of cellular processes.
AU - Niklas Neubrand
AU - Timo Rachel
AU - Tim Litwin
AU - Jens Timmer
AU - Clemens Kreutz
AU - Moritz Hess
PY - 2026
DA - 2026/10/10
JO - Bioinformatics advances
DO - 10.1093/bioadv/vbag307
UR - https://europepmc.org/article/MED/42948737
AB - Motivation Systems biology strives to unravel the complex dynamics of cellular processes using ordinary differential equations. However, the sparsity of measured data and the strong nonlinearity of common ordinary differential equations introduce severe numerical problems in modeling tasks. This motivated the development of computational algorithms that must be systematically evaluated to ensure optimal method choices. The number of well-curated models for such benchmarking efforts is insufficient, as building and calibrating biologically reasonable models from experiments requires years of work.Results We present a large-scale collection of 1100 synthetic modeling problems, generated based on the systems of ordinary differential equations and experimental designs of 22 published modeling problems. This is achieved by extending a recent method for simulation of time-course data for randomly generated observation functions to also include realistic measurement patterns across multiple experimental conditions. By analyzing data and model characteristics, optimization performance, and parameter identifiability, we show that the synthetic problems provide both a realistic and diverse extension of the existing problem space. Hence, the synthetic collection provides a valuable resource for benchmarking in dynamic modeling.Availability and implementation Benchmark problems and algorithm are publicly available at https://github.com/niklasneubrand/1100SyntheticBenchmarksODE and https://doi.org/10.5281/zenodo.23211070.
SN - 2635-0041
ER -