Brandon Alexander Holt
BME Ph.D. Proposal Presentation
Date & Time: Friday, November 15, 2019 at 11:00am
Location: Marcus Nanotechnology Building Room 1116
Gabriel A. Kwong, PhD
Andrés J. García, PhD
Kyle R. Allison, PhD
Melissa L. Kemp, PhD
Peng Qiu, PhD
Sam Brown, PhD
Title: Activity-based molecular circuits for programmable medicine
Rapid advances in engineered biological circuits are motivating the design of new treatment and detection platforms for practical applications in programmable medicine. To date, the foundational paradigm behind synthetic biological circuits is "sense-and-respond", which involves the creation of modular parts that employ Boolean logic to transduce, process, or output signals (i.e., therapeutic response). By contrast, applying biocircuits to diagnostic applications (i.e., inference) requires alternative forms of computing, such as probabilistic or analog-based information processing of complex biological states. Biology consists of dynamic processes that are largely driven by enzymes, including proteases. Proteases are a family of pleiotropic, promiscuous enzymes that specialize in the hydrolysis of peptide bonds and are drivers of complex processes in health (e.g., immunity, blood homeostasis) and disease (e.g., cancer, infectious disease). Ideally, the next generation of medicine will comprise programmable, activity-based circuits, enabling autonomous therapies and diagnostics that operate on biological information in real-time.
Accordingly, this thesis is focused on implementing circuits that are actuated by biological activity (e.g., enzymes, bacteria, etc.). Inspired by computational and statistical principles, we developed a unifying framework for using protease activity as the information carrier in both classical (i.e., Boolean, "sense-and-respond") and probabilistic circuits. To establish the design principles behind this framework, this thesis will pursue three distinct aims. (1) Defining biological activity as operable bits of information, which we apply to construct logic gates (e.g., analog-to-digital converter, comparator, etc.) that can autonomously count and kill bacterial populations. (2) Constructing therapeutic circuits with peptide-based prodrugs that are activated by bacterial proteases, which will be used to treat urinary tract infections. (3) Constructing diagnostic circuits that employ compressive sensing to sample endogenous protease activity, which will be used for the early detection and differentiation of various types of cancers (e.g., melanoma, glioblastoma, etc.). Achieving these aims will broaden the potential applications of biological circuits and advance the paradigm of activity-based medicine.