An agent-based model of the tumor microenvironment predicts hallmarks of cancer and sets the stage for model-driven experimental design of CAR T cell therapy.

Computational models are essential tools that can be used to simultaneously explain and guide biological intuition. My lab employs machine learning, dynamical systems, and agent-based modeling strategies to explain biological observations and uncover fundamental principles that drive both individual cellular decisions and cell population dynamics. We are interested in the inherent multiscale nature of biology, with a specific focus on system-level dynamics that emerge from interactions of simpler individual-level modules. In this presentation, I introduce a multiscale agent-based model of a cell population that integrates subcellular signaling and metabolism, cellular level decision processes, and dynamic vascular architecture and function to interrogate regulation among heterogeneous cell agents. The modeling framework is flexible and can be used to characterize diverse cell populations. It can also be used to inform experimental design and the design of interventions to modulate emergent population level responses.