Speaker
Description
We developed a data-integrative framework to parametrize and validate an agent-based model (ABM) of triple-negative breast cancer (TNBC) metastasis in the lung, focusing on the emergent dynamics of tumor-microenvironment interactions. The model represents tumor cells, fibroblasts, macrophages, and endothelial cells as interacting agents, governed by probabilistic rules for proliferation, death, migration, and phenotypic transitions. Parameterization leverages complementary datasets across scales. Quantitative histology (IHC/IF) provides information on cell type, spatial distribution, and temporal growth dynamics with further constraints on phenotypic composition from flow cytometry. In vitro/vivo assays quantify activation rates underlying tumor–stroma cross-talk, while bulk growth curves constrain system-level dynamics.
These heterogeneous datasets are integrated using Approximate Bayesian Computation to infer parameter distributions consistent with observed multiscale behaviors. We calibrate the model against joint observables, including population growth, phenotypic proportions, and temporal evolution. Validation is performed using chemotherapy and pathway inhibition experiments to assess predictive capacity under intervention. This approach enables identification of constrained interaction rules that reproduce observed metastatic dynamics and provides a principled framework for linking experimental measurements to mechanistic models of metastatic progression.
