From images to mechanisms: quantitative analysis and modeling of multicellular systems
Emerging imaging technologies allow us to visualize complex dynamics in multicellular systems at unprecedented resolution, yet mechanistic understanding requires quantitative analysis and modeling to go hand in hand with experiment. In this talk, I will introduce image-based quantification and agent-based modeling approaches developed in our group, and illustrate their power through a study of spherical organoids.
Pancreatic organoids self-organize into hollow monolayers that grow, secrete, and display a remarkable range of collective behaviors. We quantify these behaviors by an integrated imaging and analysis pipeline, and derive a scaling law linking volume oscillations to cell division. Using an agent-based model we reproduce rotational motion. We show that organoids flatten at the poles and drift in the direction of their rotational axis. Incorporating two cell types we predict that passive cells accumulate at the poles, and that rotational stability depends on the fraction of active cells. Together, these results show how cell-level activity gives rise to collective dynamics of organoid tissues.