Topologically-based parameter inference for agent-based model selection from spatiotemporal cellular data

TOPAZ accurately infers parameters and distinguishes between models in agent-based simulations of cell movement using topological data analysis.

• Why it matters: Understanding cell population dynamics from complex spatiotemporal data is challenging, limiting mechanistic insights into cellular interactions and behaviors.
• What they did: The study developed TOPAZ, a computational pipeline combining persistent homology with ABC, AABC, and Bayesian model selection, tested on fibroblast movement simulations with 50+ parameters.
• The result: TOPAZ successfully recovered model parameters and differentiated models, demonstrating its potential as a versatile tool for mechanistic inference and model discrimination in spatial single-cell data analysis.