FranceRoseStudent, UCSD

Skaggs School of Pharmacy and Pharmaceutical Sciences

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A bottleneck of our high-throughput screening facility is the image analysis step. As a computational biology student I work on improving the current methods used to analyze the fluorescence microscopy images acquired in the Tripasonoma cruzi cell-based assay. My goal is to balance image processing parameters to improve reliability of this robust, fast and precise method. I troubleshoot different situations which the users may potentially encounter during image processing. In the context of high-content drug discovery screening, having access to a precise infection ratio and a number of parasites per cell distribution will provide better insight into compound potency.

My past research on the Computational biology team of Auguste Genovesio at the IBENS (Biology Insitute of the Ecole Normale Superieure, Paris) was focused on segmentation and time detection of biological events, in a high-throughput context. Computation wise, it is a demanding process because you need to face a large amount of raw data, with their defects and diversity. Moreover, I see the development of these methods as a needed tool for research in diverse fields of Biology.