Modeling and Tracking of Heterogeneous Cell Populations via Open Multi-Agent Systems.

Abstract

Understanding cellular dynamics represents a critical challenge in biomedical research. Optical microscopy remains a key technique for observing live-cell behaviors in vitro. This paper introduces an enhanced cell-tracking algorithm designed to address dynamic changes in cell populations, including mitosis, migration, and cell-cell interactions, even within complex co-culture models. The proposed method involves three main steps: 1) modeling the movements and interactions of different cell types in co-culture experiments via tailored open multi-agent systems; 2) identifying parameters via real data for a multi-agent, multi-culture framework; 3) embedding the model within an Extended Kalman Filter, to predict the dynamics of heterogeneous cell populations across video frames. To validate the approach, we used a novel dataset involving the interplay between tumor and normal cells, namely osteosarcoma and mesenchymal stromal cells, respectively. This dataset offers a challenging and clinically relevant framework to track cell proliferation and study how cancer cells evolve and interact with stromal cells within their surroundings. Performance metrics demonstrated the effectiveness of the algorithm over state-of-the-art methodologies, highlighting its ability to track heterogeneous cell types, capture their interactions, and generate the estimated cell lineage tree.