biostudies-literatureUnknown2Kim MMNatural Sciences and Engineering Research Council (NSERC) of CanadaSubstitution of serum and other clinically incompatible reagents is requisite for controlling product quality in a therapeutic cell manufacturing process. However, substitution with chemically defined compounds creates a complex, large-scale optimization problem due to the large number of possible factors and dose levels, making conventional process optimization methods ineffective. We present a framework for high-dimensional optimization of serum-free formulations for the expansion of human hematopoietic cells. Our model-free approach utilizes evolutionary computing principles to drive an experiment-based feedback control platform. We validate this method by optimizing serum-free formulations for first, TF-1 cells and second, primary T-cells. For each cell type, we successfully identify aCommunications biology48https://www.ebi.ac.uk/biostudies/studies/S-EPMC6358607biostudies-literatureOn-demand serum-free media formulations for human hematopoietic cell expansion using a high dimensional search algorithm.PMC6358607Kim MMAudet JfalseOn-demand serum-free media formulations for human hematopoietic cell expansion using a high dimensional search algorithm.Substitution of serum and other clinically incompatible reagents is requisite for controlling product quality in a therapeutic cell manufacturing process. However, substitution with chemically defined compounds creates a complex, large-scale optimization problem due to the large number of possible factors and dose levels, making conventional process optimization methods ineffective. We present a framework for high-dimensional optimization of serum-free formulations for the expansion of human hematopoietic cells. Our model-free approach utilizes evolutionary computing principles to drive an experiment-based feedback control platform. We validate this method by optimizing serum-free formulations for first, TF-1 cells and second, primary T-cells. For each cell type, we successfully identify a2019-01-01T00:00:00Z20192026-05-07T06:24:38.71Z2019-03-26T22:50:52ZS-EPMC63586073072918610.1038/s42003-019-0296-7