(4or) Microbes, Mucus, and Motility: Capturing Dynamic Biofilm Microenvironments Using Multi-Scale Modeling

My Ph.D. research focuses on pairing experimental data and computational approaches spanning over different length and time scales to better model the propagation of pathogenic bacteria, specifically Pseudomonas aeruginosa. While one can characterize infection behaviors of bacteria by their individual qualities (i.e. antibiotic resistance, growth rate, -omics information), in vivo infection conditions place these pathogens in high cell density communities surrounded by a biological substrate. One example of such systems is P. aeruginosa and cystic fibrotic lung mucus where these bacteria are dispersed in a porous gel and develop into chronic respiratory infections. As this development occurs, the P. aeruginosa population may undergo intracellular changes driven by gradually depleting nutrients as well as colonize new, nutrient-rich areas via their active motility, which then introduces dynamic heterogeneity within this population. Such a system makes a computational infection model informed by experimental data valuable for circumventing the material resource bottleneck to physically test all conditions. However, many computational approaches that utilize modern -omics data face limitations in the spatiotemporal scales over which they can effectively monitor P. aeruginosa.

Computational Approaches: To address this limitation, I constructed a multi-scale model that incorporates continuum mathematics and predictions from metabolic network reconstructions to simulate the growth as well as spatial expansion of P. aeruginosa PA14 colonies in a porous gel. Paired with timelapse images and bulk product measurement described in the section below, our model results characterize PA14 primarily having more constrained motility through its gel when mucus glycoproteins are incorporated compared to gels containing synthetic polymer chains. Additionally, I am able to leverage metabolic network reconstructions to inform cytotoxin production rates in PA14 and estimate how final bulk concentration levels are fluctuating due to their motile capabilities through the gel environments.

Beyond these results, the metabolic reconstruction component of this multi-scale model allows for transcriptomic data along with a wide array of metabolites to be incorporated into the simulation and can offer large length scale context for in-silico gene deletion studies aimed to identify drug target candidates against bacteria populations. This model can also be applied to situations outside a bacterial infection setting such as microbial community systems for fermentation process or soil environments.

Experimental Approaches: I designed in parallel with the multi-scale model an experiment platform adapting a bacterial motility assay to quantitatively measure the growth as well as density distribution of multiple PA14 colonies inside various gel compositions. The length scale of the gels allows me to collect timelapse images of PA14 propagating from their inoculation point over 20 hours. The PA14 and gel substrate samples can then be further processed to isolate metabolites of interest.

Teaching Interests

My teaching philosophy aspires to have my students engage with chemical engineering concepts in contexts that extend beyond finding numerical answers to problem set questions and hopefully produce some of the sparks of curiosity that my undergraduate instructors instilled. Over the course of my Ph.D. at the University of Virginia, I have been able to develop my teaching experience in instructor and mentor roles ranging from teaching assistant to graduate co-instructor.

Laboratory Instruction: Generally, I emphasize a teaching style that allows for frequent formative feedback exchanges between me and my students. As a TA for the distillation unit operations lab, I utilized “check-in points” with student teams to openly discuss the data they collected and their informal interpretations. These practices helped me greatly when assessing students’ perspective and conceptual understanding as experiments were carried out, and it subsequently improved my feedback to students on their performance as the learning experience carried on.

Mentoring: I extend the “check-in points” habit when mentoring my 2 undergraduate research assistants. Here, I tailor these discussions to have my undergraduates express more of their perspective especially on what details are not aligning to their conceptual understanding. Overall, I use this feedback to moderate how I introduce increasingly complex information for their projects while having it remain a comfortable challenge level and help build skillsets for them to independently conduct research investigations.

Classroom Instruction: As a Teaching Fellow in UVA Engineering, I designed and delivered lesson plans as graduate student co-instructor for my department’s fluid dynamics course. In addition to ensuring frequent feedback opportunities in the form of surveys and written reflections, I developed a design-based project that had students utilize class concepts to produce an open-ended design of a water treatment plant, which incrementally added new equipment and calculations as more course material was taught.

Scholarship of Teaching and Learning: Along with teaching a student cohort the course curriculum, I am inspired to evaluate and innovate my teaching through engineering education scholarship. In the same co-instructor semester, I developed and implemented a classroom token economy for increasing student motivation to more regularly practice concepts learned at the start of the course. The token economy achieved this by distributing class currency that could be exchanged to reattempt homework or quizzes and receive an extrinsic final grade reward. This experience trained me how to document and publish scholarship of teaching and learning with organizations such as the American Society of Engineering Education.

Future Directions

I am interested in extending my Ph.D. training and continuing to optimize how experimental and computational workflows weave single-cell information together with population level dynamics, especially for investigations involving polymicrobial communities. Specifically, I am currently seeking a post-doc position focusing on metabolic engineering and designing biomanufacturing processes with co-culture systems. I am also aiming to develop my experimental skillsets in bacterial transformation and molecular biology techniques such as single cell sequencing in order to better promote a metabolic function as well as characterize the performance of specific microbial community members. I plan to eventually pursue a chemical engineering faculty member position starting a research group focusing on systems biology techniques to inform metabolic engineering decisions, and continue aspects of scholarship of teaching and learning with other interested colleagues.