(620g) Uncovering Hidden and Observable Features That Contribute to Tumor Aggression and Progression
- Conference: AIChE Annual Meeting
- Year: 2020
- Proceeding: 2020 Virtual AIChE Annual Meeting
- Group: Computing and Systems Technology Division
- Time: Wednesday, November 18, 2020 - 9:15am-9:30am
Agent-based models (ABMs) are a âbottom-upâ computational framework that predict emergent cell population dynamics from individual cell agent rules. We use this framework to simulate tumor microenvironments by integrating heterogeneous tumor cell agents with healthy cell agents in a dynamic spatiotemporal environment that comprises dynamic vasculature. The ABM provides accurate, high-resolution data describing the behavior of individual cells at every timestep that cannot be captured in vitro or in vivo [Yu et al., Frontiers 2020]. We use the established model to generate simulations that vary cell agent characteristics and their decision rule parameters, providing a landscape of data describing emergent tumor cell population dynamics. Through use of various machine learning algorithms, we uncover key intervention strategies--both at the individual cell and population levels--that contribute to tumor progression and aggression. Our work will inform clinically relevant design features that could prove as effective targets for treatments that minimize tumor aggression and progression.