Modeling Flow-Field Dynamics in Biological Systems

Optical flow measurements enable a wide variety of technology, from facial recognition to self-driving cars. In this project, I designed an optical-flow-based pipeline to quantify flow-field dynamics in biological systems.

By applying my algorithm to microscope images, I obtained measurements for things like speed and directionality in a variety of cell types. I then built a model of these flow fields with a bimodal mixed von-Mises distribution to parameterize the effect of external forces on the cells.

This algorithm was introduced in Lee*, Campanello*, at al. MBoC 2020 (preprint and manuscript) and will be featured in several upcoming publications.

The original MATLAB code for the MBoC manuscript can be found on github at github.com/LosertLab/FlowClusterTracking, and the updated version at github.com/ljcamp1624/FlowClusterTracking.

Key concepts: computer vision, optimization, modeling, maximum-likelihood estimation, data visualization, time-series analysis.

Applying Excitable-Systems Models to Biological Signaling Networks

Excitability is the phenomena whereby a system can rapidly change states, e.g., the switching between a flat and oscillatitory state in the red line on the movie to the right.

In my doctoral dissertation, I explored the role of excitability in biological systems, and identified key mechanisms that enable excitability during physiological processes such as immune response and wound healing.

One excitable-systems model that demonstrates this concept is the FitzHugh-Nagumo model, an implementation of which can be found on my github: github.com/ljcamp1624/FitzhHughNagumoModel)

Key concepts: nonlinear dynamics, nonlinear systems, coupled system, mathematical modeling, time-series analysis.

Quantification of 3-D Filament Networks.

Filament networks are ubiquitous in biology, from the extracellular matrix that cells use to navigate their environments, to the wiring of the nervous system with axons and dendrites.

In this project, I developed a robust and automatic algorithm to segment (i.e., extract), skeletonize, and disentagle filament networks based on their local topological features.

Some of this work is featured in Campanello, Traver, at al. BioRxiv (2020) and will also be utilized in upcoming papers in preparation.

Simulations of filament-network polymerization and depolymerization

The reaction-diffusion dynamics that mediate biological polymerization and depolymerization are of great interest for their role in cell regulation.

In this work, I utilize an excitable-systems and reaction-diffusion-like model to simulate the polymerization and depolymerization dynamics of Bcl10, a key intracellular protein in T cells.

In this work, I utilize an excitable-systems and reaction-diffusion-like model to simulate the polymerization and depolymerization dynamics of Bcl10, a key intracellular protein in T cells.

Featured in Campanello, Traver, at al. BioRxiv 2020.

Modeling and Visualizing Crime in New Carrollton, MD

I coached a team in the 2019 UMD Data Challenge where we built a model and visualization tools to help police officers in New Carrollton, MD predict where crime and other police indicents are likely to take place.

Using 5 years of recent data, our model identified locations and times of day in which crimes were most likely to take place. We overlaid the model's outputs on a map of New Carrollton, shown here.

This project was awarded by the AWS judges for "Best Presentation."