| Abstract: |
| Many natural and social phenomena involve individual agents coming together to create group dynamics, whether the agents are drivers in a traffic jam, cells in a developing tissue, or locusts in a swarm. Here I will focus on the example of pattern formation in zebrafish, which are named for their dark and light stripes. Mutant zebrafish, on the other hand, feature different skin patterns, including spots and labyrinth curves. All of these patterns form as the fish grow due to the interactions of tens of thousands of pigment cells, making agent-based models a natural approach for describing cell behavior. However, stochastic, microscopic models are often not analytically tractable using traditional techniques, and parameter inference in biologically detailed, spatial agent-based models faces significant challenges. With this motivation, here I will describe how we are combining techniques from topological data analysis and approximate Bayesian inference to quantify structure in messy, cell-based patterns and infer parameter values. |
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