Biomedical Sciences presents, “Development and Application of Novel Insect Olfaction-Based 'Cyborg' Gas Sensors for Disease Detection”
Presented by:
Debajit Saha, Ph.D.
Assistant Professor
Department of Biomedical Engineering
Michigan State University
Wednesday, May 20, 2026, at 10 a.m. in F-118
For further information, call 330.325.6293
Summary
There is substantial evidence that the presence of cancer alters cellular metabolic processes, leading to distinct profiles of volatile organic compounds (VOCs) emitted by cancer cells. These metabolic changes are reflected in the composition of VOCs found in exhaled breath, which has been shown to contain potential biomarkers for various diseases, including cancer. Despite this promising correlation, there is currently no breath-based cancer detection technology approved or widely used in clinical settings for routine screening. The primary barrier to clinical translation lies in the limitations of existing gas sensor technologies, which lack the sensitivity and specificity required to reliably distinguish between disease states based on VOC signatures in exhaled breath.
Biology has already solved the challenge of volatile chemical sensing through the evolution of highly sophisticated olfactory systems. Insects, in particular, possess an extraordinary sense of smell, capable of detecting odor molecules at extremely low concentrations—even in the parts-per-trillion range. Here, we take a novel forward engineering approach by developing an insect olfactory neural circuit-based VOC sensor for disease detection. We obtained oral cancer cell culture and lung cancer cell culture VOC-evoked extracellular neural responses from in vivo insect (locust and honeybee) antennal lobe neurons. We employed biological neural computation rules of the antennal lobe circuitry to generate spatiotemporal neuronal response templates corresponding to each cell culture VOC mixture. These neuronal templates were then used to distinguish between oral or lung cancer cell lines and non-cancer cell lines. Our results demonstrate that different human cancers can be robustly distinguished from each other and non-cancer cell lines. This brain-based cancer detection approach is fast, as it can differentiate between VOC mixtures within 250 ms of stimulus onset. We have also employed the sensor for endometriosis and environmental PFAS detection. Our cyborg disease detection system comprises a novel VOC sensing methodology that incorporates entire biological chemosensory arrays, biological signal transduction, and neuronal computations in the form of a forward-engineered technology for cancer VOC detection.