Author summary Tissues heal and cancers invade when cells move together, yet we still lack a testable account of how chemical and mechanical cues combine to steer this motion. We created a data driven framework that learns the governing rule directly from live cell movies. Applied to epithelial sheets where MAPK/ERK waves drive collective migration, the learned rule predicts each cell’s next movement from local chemical and mechanical information. Interrogating the rule shows that cells rely on absolute signal levels and on how those signals change across space and time: gradients and rates of change. The framework also reveals cell to cell differences in these migratory rules and a clear front versus interior distinction within the same sheet. Cells at the wound edge respond mainly to the instantaneous spatial gradient, whereas interior cells are more sensitive to recent temporal changes. Finally, we used the learned rule to refine a forward model, improving its ability to reproduce observed behavior. By translating raw imaging data into predictive rules, our approach provides a general tool to connect data with mechanism in multicellular dynamics and to inform quantitative studies of tissue repair and disease.
Introduction
Collective cell migration is a fundamental driver of tissue homeostasis and underpins a variety of biological processes, including wound healing and cancer invasion [1–7]. These coordinated movements are orchestrated by intricate biochem… [43175 chars]
Source: PLOS (Public Library of Science) | Published: 2025-12-29T00:00:00Z
Credit: PLOS (Public Library of Science)
