1100 synthetic benchmark problems for dynamic modeling of cellular processes

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.

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.

Benchmark problems and algorithm are publicly available at https://github.com/niklasneubrand/1100SyntheticBenchmarksODE and https://doi.org/10.5281/zenodo.23211070.