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RNA interference has been widely used to identify genes involved in the production of particular biological phenotypes. This type of gene silencing technology has been used in plants, invertebrates and mammalian systems . The availability of the sequences of large numbers of genes has allowed large libraries of siRNAs to be produced. To effectively use these libraries in screens, high-throughput robotic screening methodologies have been devised. The identification of meaningful results from any screening system requires the analyst to identify and discount variances in output that arise from the devices used in the screen as well as variances that arise from biological components in the screen that are unrelated to gene silencing. In this developing technology, this analytical task is made difficult by variances in up-take of the siRNAs by the cells, variations in the magnitude of the silencing effect, and mechanical effects that can produce systematic alterations in cell delivery and cell growth during the experiment. To examine how the analysis can be optimized, models of the screening process have been built using estimates of the various noise and signal variances derived from available screen data. Synthetic data was then generated from this model and used to test the capability of a number of data normalization methods to reduce noise and allow signal detection.