Decoding the cellular Allee effect through a stochastic modeling tool for assessing neighborhood and lineage impacts on cell growth

Regulating cell cycle length in human cells is a key mechanism of population control, driven by factors such as the local microenvironment, mother-daughter inheritance, and intercellular interactions. Disentangling these drivers remains a significant experimental challenge. Here, we present an iterative parameter-testing system based on a stochastic cellular automata (CA) model to evaluate how local neighborhoods and generational history affect cell cycle duration. To demonstrate the utility of this computational tool, we used a novel shear-free, diffusive microfluidic platform for long-term time-lapse microscopy to acquire precise tracking data on HeLa cervical cancer cell growth, including movement, death rate, lineage, and cycle duration. As a proof-of-concept, our simulations reveal that, in this specific cell line, inherited generation-to-generation information dictates proliferation significantly more than the local cellular neighborhood. By integrating multiscale experimental data and time-lapse imaging with the CA model, we show how the tool can explicitly quantify competing biological drivers. Ultimately, this developed algorithmic framework provides a robust method for analyzing cell growth dynamics and can be adapted to decode the cellular Allee effect across various biological systems.