Uncovering Variation in Cell Culture
This video shows the use of Riffyn to assess and identify the sources of undesired variation in your experimental methods. This experiment examines a simple 3-step cell growth process with optical density as a final measurement. It is designed to assess the impact of suspected causes of growth variation (temperature, shaking speed, treatment concentration), and to isolate unknown causes of error (i.e., find the step that contributes the most error). It is structured with a full-factorial DoE on the first step, triplicate replication on the second step, and triplicate measurement on the third step. Riffyn integrates all experimental parameters and measured data into a data frame. Using ANOVA, variance components analysis and multivariate regression in JMP, the main contributors to experimental variation are identified.
High Throughput Screening & Hit Picking
This video illustrates how you can use Riffyn for high-throughput screening activities including hit picking, sample tracking, and uploading of screening or plate map data. In this experiment, 384 cell lines are transformed with plasmid and screened in a three-tier screening process. Riffyn allows the scientist to select hits, pass the hits from tier to tier, track their parent-child lineage and plate position throughout the process. All the screening data is captured together alongside the sample tracking information for easy integration and analysis by Riffyn's analytical engine.
Animal Pharmacokinetics Study
This video illustrates the use of Riffyn to execute an Animal PK study, including multivariate data analysis. The aim of this study is to determine the impact of various animal and dosing characteristics on the absorption of a therapeutic compound into the bloodstream. Analytical data on blood concentration is collected into Riffyn alongside study data characterizing the animal physiology and dosing parameters. Riffyn is used to automatically integrate the data into a statistical data frame and then feed the data frame to JMP for multivariate analysis. Two factors are determined to have a statistically significant impact on adsorption, one is shown to be irrelevant.
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