Speaker
Description
Understanding how groups of cells robustly coordinate their behavior represents a key question in developmental biology. Mathematical modeling helps to address this problem by enabling researchers to investigate hypotheses in an abstract setting, yet it remains challenging to link these theoretical frameworks to experimental data. Here, we present a novel computational pipeline that addresses this issue and illustrate its application to the case of zebrafish stripe pattern formation. The pipeline generates labeled point clouds from experimental and theoretical images and extracts information about their spatial patterns by applying tools from spatial data analysis. We demonstrate that statistics which measure spatial organization across multiple length scales are robust to experimental and synthetic replicates, even when synthetic data are sampled from a nearly spatially uniform initial state. Unsupervised clustering of images based on pipeline-derived statistics yields biologically interpretable clusters based on the presence of stripes, spots, or maze-like structures. We envision that our spatial analysis pipeline, which is agnostic to data source and not limited to models of zebrafish pattern formation, will enable future researchers to robustly and accurately link dynamic models of spatially heterogeneous phenomena to experimental data.
