Leveraging machine learning to dissect role of combinations of amino acids in modulating the effect of zinc on mammalian cell growth.

Abstract

Although the contributions of individual components of cell culture media are largely known, their combinatorial effects are far less understood. Experiments varying one component at a time cannot identify combinatorial effects, and analysis of the large number of experiments required to decipher such effects is challenging. Machine learning algorithms can help in the analysis of such datasets to identify multi-component interactions. Zinc toxicity in vitro is known to change depending on amino acid concentration in the extracellular medium. Multiple amino acids are known to be involved in this protection. Thirty-two amino acid compositions were formulated to evaluate their effect on the growth of CHO cells under high zinc conditions. A sequential machine learning analysis methodology was used, which led to the identification of a set of amino acids (threonine, proline, glutamate, aspartate, asparagine, and tryptophan) contributing to protection from zinc. Our results suggest that a decrease in availability of these set of amino acids due to consumption may affect cell growth in media formulated with high zinc concentrations, and in contrast, normal levels of these amino acids are associated with better tolerance to high zinc concentration. Our sequential analysis method may be similarly employed for high throughput medium design and optimization experiments to identify interactions among a large number of cell culture medium components.