(362g) Hybrid Models Explain Emergent Dynamics in Complex Cell Populations

Computational modeling is an essential tool for integrating and understanding complex biological systems. With increasingly high-resolution, high-throughput, and dynamic experimental data, the in silico systems biology community is able to develop better informed models to interrogate the complex, heterogeneous, and multiscale nature of cellular microenvironments. My lab employs machine learning with dynamical systems and agent-based models to study emergence in heterogeneous cell populations. Specifically, we investigate the impact of intracellular network dynamics on intercellular signaling in context of both cancer and immune systems. Our simulations interrogate how the whole is greater than the sum of its parts by predicting cell population dynamics from the composition of simpler signaling, or biological modules. Elucidating the compositionality problem is fundamental to advancing our understanding of basic science; to promoting the impact of synthetic biology; and to designing precise dynamic therapeutic strategies.