Medical Engineering Defense, Heather Lukas

In-person: 105 Annenberg
Zoom: https://caltech.zoom.us/j/85048824691
Meeting ID: 850 4882 4691

Abstract: The rise of wearable sensing through smartwatches and continuous glucose monitors has made health data more widely accessible. Advances in machine learning have also been pivotal in identifying personalized health insights from biometric data streams. However, continuous biochemical data has been limited in sensor design by the availability of oxidoreductases (e.g. glucose oxidase, lactate dehydrogenase) to a given target. The challenge in engineering diverse oxidoreductase enzymes has led to the exploration of other generalized approaches to continuous electrochemical biosensing. To meet this need, we have explored a variety of bioaffinity sensing schemes using broad bioreceptor classes including antibodies, nucleic acids, and periplasmic binding proteins. We present a case study in electrochemical sensor design utilizing high affinity antibodies for the rapid diagnosis of COVID-19 disease states. We then investigate the potential of nucleic acid-based electrochemical sensors for continuous sensing with a focus on structure-switching nucleic acid aptamers. The utility of aptamer sensors is demonstrated in the development of a serotonin aptamer sensor embedded in an ingestible capsule for continuous biosensing in the gastrointestinal tract. Applying the principles of electrochemical aptamer-based sensing, we explored the development of an electrochemical protein-based sensor for nicotine, which exploits the hinge-like binding motion of periplasmic binding proteins while also capitalizing on decades of protein evolution and characterization research. With the goal of continuous, noninvasive biochemical sensing, we evaluate the design considerations and translatability of these sensors for wearable sweat analysis. These biosensing techniques may enable the future hardware necessary to expand accessible biomedical data for the next wave of personalized health monitoring.