Event

PhD Dissertation Defense: Kayla Chun

Thursday, May 8, 2025
12:30 p.m.
AJC 5104 (5th floor conference room)
Rachel Chang
301 405 8268
rachel53@umd.edu

Title: Development of Computationally Aided Methods for Interpreting Molecular Information

Committee members:

Dr. William E. Bentley, Chair

Dr. Elebeoba May

Dr. Sara Molinari

Dr. Gregory F. Payne

Dr. Amy Karlsson, Dean's Representative

Abstract:

Modern electronics have enabled a highly connected world due to the immense amount of information and communication that is readily available. Comparatively, biological information lies within biomolecular states and their transitions rather than in electrons and photons. Thus, rendering the process of understanding and extracting biological information difficult and time consuming; often requiring labor intensive and complex analytical methodologies. The development of rapid approaches for designing, controlling, and monitoring biologics are critical to both clinical and industrial applications of biotechnology. Redox reactions are ubiquitous across biology and offer a bridge to electronically connect into biologics through their electron exchanges. By leveraging redox molecules to interface between biomolecular and electronic communication, we can implement electronic signaling in biological systems to rapidly monitor and induce functional changes. Further,  we can incorporate computational methods to model biological dynamics and parse electronic outputs from these systems, providing approaches for improved biological design and control.

This dissertation focused on developing synthetic means for information propagation within biological bacterial co-cultures that made use of multi-modal molecular signals. We showed how molecular information could be initiated and propagated through these co-cultures, and controlled through modulating species composition. A graph network model was then developed for simulating multi-modal signaling in synthetic bacterial consortia to study the effects of spatial conformation within co-cultures on information transmission. Finally, we developed electrochemical methods for rapidly extracting biological information using redox-mediated “probing”. A novel machine learning methodology was implemented to extract biological information from this electronic data. These methodologies were developed for specific industrial and clinically relevant use cases such as: 1) Biomanufacturing of antibody therapeutics, to quantify a critical nutrient, L-cysteine, and antibody fragmentation as a marker for product quality; 2) Female reproductive health monitoring, to classify vaginal microbiome states, where dysbiosis is linked to higher risk of health complications such as pelvic inflammatory disease and preterm labor. In sum, this work contributes tools for designing and monitoring complex biological systems to predict responses and enhance clinical and industrial manufacturing applications.