Research Overview
Dr. Reid leads multidisciplinary research initiatives that bridge chemical engineering principles with biomedical applications. His work focuses on developing innovative approaches to cancer research, from molecular-level analysis using advanced optical techniques to the design of automated synthesis equipment and the application of machine learning for diagnostic equity.
The research portfolio encompasses three main areas: lipid metabolism analysis in cancer cells, artificial intelligence applications for reducing diagnostic disparities, and the development of cost-effective automated synthesis equipment for peptide therapeutics.
Kinetic Analysis of Lipid Metabolism in Breast Cancer Cells via Nonlinear Optical Microscopy
This research combines coherent Raman scattering microscopy with deuterium labeling to provide unprecedented insight into lipid metabolism in live cancer cells during cancer development and progression. The dynamic metabolic data are modeled using Michaelis-Menten kinetics to independently quantify lipid synthesis and utilization in cancer cells. Changes in lipid levels originate from de novo lipid synthesis using glucose as a source, lipid consumption from Beta-oxidation, and lipid consumption from cell proliferation processes that can be separately analyzed with the Michaelis-Menten model. The research isolates fatty acid synthesis and consumption rates and elucidates effects of altered lipid metabolism in T47D breast cancer cells in response to 17Beta-estradiol stimulation and etomoxir treatment. This work enables observation of dynamic processes that cannot be easily observed without the application of appropriate kinetic models, providing new insights into cancer cell metabolism that could inform therapeutic strategies.
Identifying Racial Discrepancies in Uterine Cancer Diagnosis with Convolutional Neural Networks
Uterine cancer is the fourth most common cancer overall in women in the United States. Unlike most cancers, rising incidences and mortalities associated with uterine cancer have been reported, with certain subgroups at a disproportionately greater risk of developing the more aggressive forms. That subgroup of women are more likely to die from uterine cancer due to diagnoses being made at a later, less treatable stage. Studies attribute this to a variety of factors, including lack of accurate and precise diagnosis during early stages of the cancer. A possible solution is the use of machine learning (ML) in the diagnosis of uterine cancer. Machine learning techniques have been shown to be excellent at diagnosing cancer through the analysis of data and images such as x-rays, magnetic resonance imaging (MRIs) and ultrasounds. However, a major limitation of machine learning techniques is the data on which they are trained. If the models are trained on a biased set of data, the diagnoses are likely to be inherently biased. Similarly, if diagnostic devices, such as sensors, are designed without an understanding of ethnic differences in biological behavior, the devices will also have biases in their outcomes. This study aims to 1) identify and analyze machine learning models that can recognize uterine cancer through the analysis of existing medical scans; and 2) apply machine learning models to a multiplex diagnostic with spectral and electrochemical capabilities, to identify endometrial uterine cancer across all individuals.
Design and Fabrication of an Automated 3D-printed Miniature Peptide Synthesizer
This project addresses the high cost and environmental impact of traditional peptide synthesis by developing a low-cost, automated, miniature peptide synthesizer using 3D printing technology. Traditional peptide synthesis machinery costs exceed $30,000 and generates multi-ton waste per kilogram of peptide produced, creating barriers to research and therapeutic development. The design utilizes a 32-to-one selector valve system with cam and follower mechanisms, controlled by an Arduino UNO R3 microcontroller. The reactor incorporates multiple components: an automated valve network, a miniaturized reaction chamber, waste management system, and motorized control system. The design significantly reduces the use of toxic reagents like dimethylformamide (DMF) while maintaining synthesis capability. Key innovations include 3D-printed valve components for higher reproducibility and lower costs, a vertical tower platform for increased portability, and an integrated waste containment system. The system can synthesize peptides at scales less than 100 micromoles, making peptide research more accessible to smaller laboratories and underserved communities while reducing environmental impact through optimized reagent usage.