UCSF

High-throughput screening (HTS) leverages laboratory automation and advanced analytics in order to perform experiments much more quickly than what can be accomplished by hand. Since its inception in the 1990s, HTS has revolutionized the field of drug discovery, and has been adopted by the leaders of the industry. However, the application of HTS technology has largely been limited to purified protein or cell-culture based assays, with detrimental consequences for the viability of lead compounds identified using this approach. In this dissertation, I discuss my work in developing new tools to implement HTS techniques in whole-organism models such as zebrafish and C. elegans. Whole organisms can be used to create more biologically-relevant disease models and to find cures for diseases with unknown etiology. Additionally, the use of whole-organism HTS opens up new possibilities in fields such as forward genetic screens. I describe how automated microscopy can be combined with computer vision and machine learning techniques in order to streamline the complicated logistics of imaging and analyzing whole organisms and whole-organism data. It is my hope that the tools and workflows detailed will increase adoption of whole-organism HTS and lead to improved drug development success rates as well as to facilitate scientific discovery.