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Introducing Automated Assay Optimization on the Biomek FXP workstation.
Maps plates automatically or manually Automated Assay Optimization (AAO) Software breaks the bottleneck when developing new assays and converting them to effective high-throughput screens. The unique software package for the Biomek FXP Liquid Handling System uses a wizard to study assay constraints and variables rapidly and effectively through statistical experimental design (DOE). AAO can cut assay development from months to days by converting the designed experiments into plate maps and ultimately the Biomek FXP system software methods for automated pipetting. This eliminates the mundane and tedious tasks of manual pipetting or writing complex liquid handling methods. AAO also provides data management and analysis tools used to create more robust and cost-effective screens.
Statistically Designed Experiments
Design your experiments in your favorite statistics package using two-level fraction factorial designs, or any number of other complex multi-level designs. Then import the experiment set into AAO, and add your own quality control wells, experimental protocols, and well restrictions. The result is a targeted experimental design set for the optimization study at hand.
Automatic Plate Mapping and Pipetting Methods
Once designed, AAO automatically maps the experiment into randomized plate wells and creates the Biomek FXP methods. This includes the creation of buffer mixtures either in intermediate labware or directly in the assay plates, either 96- or 384-well plates. The researcher has complete control over all pipetting parameters and the order of additions. After a few simple inputs, reagent liquid handling methods are created seamlessly in the background.
Post-Assay Data Collection and Analysis
Once the assay has been completed, data collection can be automated from one to several plate readers or data can be directly imported into Microsoft* Excel. Data can also be correlated back to the original experiment and formatted for use in the existing statistical package. Researchers are quickly able to see which factors are critical for assay performance and the optimal settings for those factors.
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