Learning cell division strategies across diverse organisms

Regulation of cell growth and division is essential to achieve cell-size homeostasis. Recent advances in imaging technologies, such as the "mother machine", have enabled long-term tracking of cell-size dynamics, and led to valuable insights into the mechanisms underlying cell-size control. However, understanding the governing rules of cell growth and division within a dynamical systems framework remains a challenge. To address this challenge, we model the cell-size dynamics using a stochastic differential equation (SDE) with a Poisson process describing cell division events. To account for memory in the cell division process, we employ a Poisson intensity determined by both the current cell size and its history. We develop a computational framework that leverages spectral basis representations and sparse model inference to identify SDE models from experimental data, allowing us to cluster different cell division strategies across various organisms. Our data-driven inference framework opens up new opportunities to investigate the mechanisms underlying cell-size control, beyond the conventional paradigms of "sizers," "adders," and "timers."