Publications by authors named "Motohisa Kamei"

Induced differentiation is one of the most experience- and skill-dependent experimental processes in regenerative medicine, and establishing optimal conditions often takes years. We developed a robotic AI system with a batch Bayesian optimization algorithm that autonomously induces the differentiation of induced pluripotent stem cell-derived retinal pigment epithelial (iPSC-RPE) cells. From 200 million possible parameter combinations, the system performed cell culture in 143 different conditions in 111 days, resulting in 88% better iPSC-RPE production than that obtained by the pre-optimized culture in terms of the pigmentation scores.

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Cell culturing is a basic experimental technique in cell biology and medical science. However, culturing high-quality cells with a high degree of reproducibility relies heavily on expert skills and tacit knowledge, and it is not straightforward to scale the production process due to the education bottleneck. Although many automated culture systems have been developed and a few have succeeded in mass production environments, very few robots are permissive of frequent protocol changes, which are often required in basic research environments.

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