Speaker
Description
Metastasis is a major determinant of survival and treatment efficacy in cancer, yet the mechanisms by which the competition and interaction of heterogeneous tumor cell clones leads to metastasis remains poorly understood. Prior experiments comparing fluorescently barcoded models of human HER2+ breast cancer show that wild-type, d16, and p95 isoforms differ in their invasion and motility (speed, persistence, mean-square displacement) properties. We developed a generative AI-based pipeline that extracts single-cell trajectories from in vitro live-cell timelapse microscopy videos of mixed-isoform cultures of HER2+ breast cancer cells. Then we construct a computational pipeline to infer agent-based model (ABM) rules describing the interactions between different isoforms using the single-cell trajectories. We simulate data from an ABM that recapitulates the motility characteristics of mixed-isoform in vitro HER2+ breast cancer cell populations. We then apply the Weak Sparse Identification of Nonlinear Dynamics (WSINDy) approach to test whether intercellular interactions governing cell movement can be recovered from simulated data, and the degree to which recovery accuracy depends on ABM initial conditions such as initial cell density and the proportion of cells within each isoform subpopulation (wild-type, d16, p95).
