Computational models are essential tools that can be used to simultaneously explain and guide biological intuition. My lab employs agent-based modeling, machine learning, and dynamical systems to explain biological observations and to uncover design principles that drive individual cellular decisions as well as 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 generic solid tumor microenvironment that integrates subcellular signaling and metabolism, cell-level decision processes, and dynamic vascular architecture and function. We use this modeling framework to interrogate regulation among heterogeneous cell agents in changing microenvironments. The model is open-source and flexible/adaptable (it can characterize countless cell population dynamics!), but it is computationally costly to simulate and analyze at large scales. I highlight these challenges along with strategies to mitigate them, and showcase successes that derive from our model development process. I also describe how the model can be used to inform the design of experiments and interventions that modulate population level responses.