Biology-aware machine learning for culture medium optimization

Abstract

Cell culture technologies are widely used in academia and industry, yet optimizing culture media remains an art due to the complexity of cell-medium interactions. Machine learning has emerged as a promising solution, but it is hindered by biological fluctuations and experimental errors. To address these issues, we developed a medium optimization platform that integrates simplified and effective experimental manipulation, error-aware data processing for model training, predictive model construction to enhance accuracy and avoid local optimization, and an efficient optimization framework of active learning. Using this approach, we fine-tuned a 57-component serum-free medium for CHO-K1 cells, in which a total of 364 media were experimentally tested. The reformulated medium achieved approximately 60 % higher cell concentration than commercial alternatives. The improved cell culture is definitive toward CHO-K1, underscoring the platform's precision in targeted cell culture optimization. Our approach offers a robust tool for optimizing complex systems in cell culture and broader experimental studies, as well as in biomedical engineering applications.

Keywords: Active learning; Biological fluctuation; Error-aware data processing; Experimental error; Machine learning; Medium optimization; Serum-free.