(431g) Building an in silico Representation of the Tumor Microenvironment One Agent at a Time (Invited Speaker)

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 the complex system-level dynamics that emerge from interactions of simpler individual-level modules. Taken together, our research program aims to advance our understanding of basic science and translational medicine through computational modeling.

In this presentation, I introduce a multiscale agent-based model of a generic tumor microenvironment. This model integrates subcellular signaling and metabolism, cellular level decision processes, and dynamic vascular architecture and function to interrogate multilateral regulation among heterogeneous cell agents in a nascent solid tumor. Using this framework, we interrogate emergent dynamics of tumor growth as a function of vascular architecture, cell heterogeneity, nutrient distribution, and immune intervention. In addition to the tumor microenvironment, this flexible model framework can be adapted to represent and analyze a wide variety of cell contexts, including wound healing and stem cell colony formation and fate commitment.