Speaker
Description
Mitochondrial inheritance during cell division is a fundamental biological process that ensures daughter cell viability, yet its governing mechanisms remain incompletely understood. In budding yeast (Saccharomyces cerevisiae), experimental observations reveal substantial variability in how mitochondrial content is partitioned between mother and daughter cells, raising key questions about the relative roles of deterministic regulation and stochasticity. In particular, we investigate whether inheritance can be explained by simple proportional rules or whether it depends on a higher-dimensional cellular state immediately prior to division. To address these questions, we develop a data-driven modeling framework that combines equation learning with
distributional inference. We analyze high-resolution live-cell imaging data that track mitochondrial content, cell size, and additional molecular markers (e.g., Ime1, Erg6, Vma1) across thousands of cell division events, enabling the reconstruction of lineage relationships and pre-division cellular states. We first apply equation learning techniques, including sparse regression and biologically informed neural networks, to infer interpretable relationships between pre-division cellular features and post-division mitochondrial allocation. To account for inter-cellular variability, we further incorporate a distributional calibration framework based on the Prohorov metric, which enables comparison between empirical and model-generated distributions of inheritance outcomes. Together, these methods provide a unified framework for learning interpretable models of mitochondrial inheritance. More broadly, this work illustrates how combining equation discovery with distributional inference can be used to investigate mechanistic hypotheses in complex biological systems where both structure and population heterogeneity play essential roles.
