Modeling Flow-Field Dynamics in Biological Systems

Optical flow measurements enable a wide variety of technologies, 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.

Applying the algorithm to microscope images, I extracted measurements such as speed and directionality across various cell types. I then modeled these flow fields using a bimodal mixed von-Mises distribution, capturing the influence of external forces on cellular behavior.

This algorithm was introduced in Lee*, Campanello*, et al., MBoC 2020 (preprint and manuscript), and was featured in several follow-up publications.

The original MATLAB code for the MBoC manuscript can be found at github.com/LosertLab/FlowClusterTracking, with updates 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 phenomenon whereby a system can rapidly change states, e.g., switching between a damped and continuous oscillatory state, as seen in the red line in the animation to the right.

In my doctoral dissertation, I explored the role of excitability in biological systems, identifying key mechanisms that enable it during processes like immune response and wound healing.

One excitable-systems model that demonstrates this behavior is the FitzHugh-Nagumo model, an implementation of which is available on my GitHub: github.com/ljcamp1624/FitzhHughNagumoModel

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

Quantification of 3D Filament Networks

Filament networks are ubiquitous in biology—from the extracellular matrix that guides cell migration, to the wiring of the nervous system via axons and dendrites.

I developed a robust and automatic algorithm to segment, skeletonize, and disentangle filament networks using their local topological features.

Some of this work is featured in Campanello, Traver, et al., BioRxiv (2020), and will appear in upcoming publications.

Simulations of Filament-Network Polymerization and Depolymerization

Reaction-diffusion dynamics governing polymerization and depolymerization are critical to understanding intracellular regulation.

In this project, I used an excitable-systems and reaction-diffusion-like model to simulate the dynamics of Bcl10, a key intracellular protein involved in T-cell signaling.

Featured in Campanello, Traver, et al., PLOS Computational Biology, 2020.

Modeling and Visualizing Crime in New Carrollton, MD

I coached a team in the 2019 UMD Data Challenge, where we built models and visualization tools to help police officers predict when and where crime incidents were most likely to occur.

Using five years of historical data, our model identified high-risk areas and time windows, which we overlaid onto a city map of New Carrollton, MD (shown at left).

This project was awarded “Best Presentation” by AWS judges.