A data-driven evaluation of Optimization techniques in Cell Culture Media

Optimizing cell-culture media for cultivated meat and cellular agriculture is both experimentally demanding and expensive. Traditional approaches employ Design of Experiments (DOE) to iterate through candidate formulations until a satisfactory medium emerges. Data-driven methods, and in particular Bayesian optimization (BO), can reach higher objective function values (here, titer) with fewer evaluations than classical DOE. Prior comparisons of optimizers on cell-media data have, however, relied largely on computational simulations rather than measured formulations, leaving researchers without evidence-based guidance on which strategy to adopt.

We introduce two complementary benchmark protocols and apply them to six published cell-culture datasets. In the "Hide-the-Label" protocol each optimizer begins with only a partial view of the data and sequentially reveals hidden formulations, singly or in batches, reproducing the stepwise acquisition of costly laboratory measurements; the "Open Race" instead measures search efficiency of a method under a fixed evaluation budget. Both protocols were run across four generative surrogate classes (Gaussian process (GP), random forest, neural network, and Bayesian neural network), two difficulty regimes, and batch sizes of one to 20. GP-based BO was consistently the most sample-efficient strategy, locating hidden targets in substantially fewer evaluations than classical DOE designs or random search; its advantage narrowed but remained under injected noise and multi-modality, and optimizers built on more complex surrogates (deeper networks or adaptive ensembles) performed worse rather than better on these harder landscapes. We detail the mathematical basis of the benchmark, criteria for selecting suitable datasets, and how to obtain statistically robust conclusions in the presence of measurement bias and heterogeneity, and we provide a roadmap for selecting an optimization strategy in practice.

Source code and the benchmark datasets are freely available at https://github.com/Amii-Applied-AI/amii-cell-ag-tools/tree/main/active-learning-for-cell-media and archived at https://doi.org/10.5281/zenodo.21501659.

Supplementary data are available at Bioinformatics online.