Neubrand, N.; Rachel, T.; Litwin, T.; Timmer, J.; Kreutz, C.; Hess, M.

Motivation: Systems biology strives to unravel the complex dynamics of cellular processes, often with the help of ordinary differential equations (ODEs). However, the sparsity of measured data and the strong non-linearity of common ODEs introduce severe numerical problems in typical modeling tasks. This gave rise to the development of many computational algorithms that must be systematically evaluated to ensure optimal method choices. Currently, the amount of well curated models for such benchmarking efforts is insufficient, as building and calibrating biologically reasonable models based on experiments requires years of work. Results: We present a large-scale collection of 1100 synthetic modeling problems, generated based on the ODE systems 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://zenodo.org/records/14008247. Keywords: systems biology, benchmarking, differential equations, mathematical modeling