utilize statistical concepts to improve efficiences across bioassay design, development, validation and monitoring

This course will emphasize the statistical concepts needed for bioassays. Key ideas will be illustrated with useful and relevant examples. Other examples will illustrate how these statistical concepts give insight into the properties of bioassays and how these ideas and properties inform the choices we make in bioassay development, bioassay design, validation, and monitoring. Examples (mostly from cell-based bioassays, some using robotics) will illustrate strategic ways to design bioassays that are practical in the lab and statistically sound. A modular approach to assay design supports use of design of experiments (DOE), easy tuning of assay size to achieve different performance targets for different intended uses, facilitates monitoring, and helps standardize many elements of bioassay design across different assay systems. Examples will highlight how DOE strategies change through bioassay development, when measuring the capability of a bioassay, when demonstrating robustness, when improving an existing bioassay, and when doing validation. We will cover ways that these strategic assay design considerations, when combined with good assay analysis methods, also support good assay monitoring with graphical and quantitative tools as part of a lifecycle approach.