Designing Efficient DOE Strategies for Complex Cell Media Development (2026-EU-30MP-2813)

Cell culture media development often involves a daunting challenge: screening across dozens of factors –sometimes 50 or more – while operating under strict experimental resource constraints. Traditional design of experiments (DOE) approaches struggle to scale in such high-dimensional spaces, and fully automated methods can lead to impractical or non-executable designs.

In this session, we explore how JMP can help navigate these challenges by comparing advanced design strategies, including modern space filling designs and Bayesian D-optimal designs, with leveraging prior knowledge to guide factor selections. Beyond the algorithms, we address the practical realities of executing experiments in the lab, where statistical efficiency must meet operational feasibility.

Finally, we emphasize the critical role of cross-functional collaboration and brainstorming sessions: successful DOE in this context requires close interaction between statisticians, data scientists, process engineers, and laboratory personnel. By combining advanced DOE methods with collaborative problem solving and practical insights, teams can accelerate cell media development while making the most of available resources.