1100 Synthetic Benchmark Problems for Dynamic Modeling of Cellular Processes

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ID: 332050
2026
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Abstract
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 non-linearity 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 amount 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.
Reference Key
openalex_W7221347040 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Niklas Neubrand, Timo Rachel, Tim Litwin, Jens Timmer, Clemens Kreutz, Moritz Hess
Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag307
URL
https://doi.org/10.1093/bioadv/vbag307
Keywords Keywords not found

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