Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth
Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth
Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity of cell dynamics, and numerous model parameters are fundamental limitations of applying such models to predict tumor dynamics. To overcome …