Aidan Olman
Research Mentor(s): Robert Kennedy
Mentor Department: Professor of Chemistry
Authors: Laura Penabad, Aidan Olman
Session: Session 6 (3:00pm – 3:50pm)
Presentation Type: Poster 10
Abstract
There is a lack of label-free workflows in the high-throughput regime that are applicable to biocatalyst screening in directed evolution (DE) workflows. Screening throughput is the primary bottleneck of enzyme engineering campaigns. By incorporating droplet microfluidics, it is possible to screen under 1 Hz. However, this shifts the bottleneck toward analyzing the data rather than screening it. This paper presents the development of a comprehensive data analytics suite designed to streamline processing, visualization, and interpretation of large-scale datasets. The suite automates key steps such as peak detection and correlated isomer intensities. It also addresses common challenges such as peak splitting, merging, and mislabeling due to variability in droplet behavior. The program reads from an excel sheet of recorded intensities at various time intervals. From this it identifies the peaks and the associated isomer intensities. Throughout the dataset, it keeps track of each peak’s duration and uses this information to mark which droplets could have merged or split. It then calculates valuable metrics such as yield, intensity ratios, and calibrated intensities for each peak. This tool can prove to be extremely useful in reducing the time for manual analysis of the dataset, enabling researchers to efficiently extract meaningful insights from complex datasets.
