Optimizing biological assay conditions is a demanding process that scientists face every day. The requirement is to develop high-quality, robust assays that work across a wide range of biological conditions. The demand is to do this within a short development time frame. To overcome these obstacles, automated systems are often required to accommodate large numbers of samples. Setting up a model that systematically studies key experimental parameters, each across a defined range, is a challenge. Traditionally, one-factor-at-a-time (OFAT) testing studies individual experimental conditions, but this approach is time-consuming and tedious. More important, measuring how the change of one single factor affects the assay leaves the experimenter blind to interactions that may exist between two or more experimental factors. The information lost in OFAT designs may significantly reduce assay quality and robustness.

To overcome these hurdles, scientists are now adopting design of experiments (DOE) methodology. Using mathematical models, they’re evaluating experimental parameters in order to optimize assay conditions. The newly created designs evaluate main effects and interactions between experimental factors such as sample concentration, reagent types and incubation time. These experimental parameters represent only a few of the many influencing assay factors that are currently being studied using DOE.

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