Speaker
Description
Cell migration and invasion are key processes underlying cancer metastasis. These behaviors are driven by cell–cell adhesion, chemotaxis, and matrix remodeling. CompuCell3D is a platform for agent-based modeling of cell migration; however, a limitation is that it does not natively support systematic parameter estimation for the stochastic simulations. Traditional Monte Carlo sampling-based approaches to parameter tuning are time-consuming and computational demanding. This work focuses on parameter estimation for agent-based modeling of collective cell invasion for two phenotypes: a network (invasive) phenotype and a spheroid (non-invasive) phenotype emerging from growth of tumor spheroids simulated using CompuCell3D. We adopt a data-centric approach based on Gaussian process surrogate models and Bayesian optimization, leveraging simulation outputs alongside experimental data for invasion dynamics and circularity of cell clusters. This framework efficiently operates with limited simulation data and sequentially proposes new parameter sets to evaluate, prioritizing those expected to provide maximal information gain. Through sequential rounds of parameter selection, agent-based simulations, and comparison with experimental data, the calibrated model reproduces experimental invasion dynamics and circularity. This framework provides an efficient approach for producing realistic invasion dynamics while reducing the need for extensive trial-and-error simulations.
