Interpreting stochastic agent-based models of cell death

Abstract Linking individual cell behavior to changes on the cell population and ultimately tissue scale is a key step towards connecting in vitro laboratory experiments to clinically relevant organ-scale behavior. For example, understanding how an individual tumor cell’s response to drug treatments propagates to the macroscale could help guide clinical decision making when tumors are located in high stakes regions. Based on how dying cells interact, macroscale tumor response can be dominated by tumor shrinkage, tumor damage, change in tumor porosity, or some combination of these phenomena. Here we focus on methods of modeling collective cell behavior with the overall goal of understanding how stochastic cell behavior will ultimately manifest on the cell population scale. To do this, we define and implement an agent-based model problem, introduce novel methods for interpreting simulation results, implement a recently developed technique for conducting a variance-based sensitivity analysis, and investigate key model parameters. Operating within an in silico framework allows us to compare our proposed methods with and without reproducible stochastic variations. This is critical because methods that will be functional for interpreting experimental data must also be able to function when aspects of stochastic variation are not reproducible. To demonstrate the utility of our proposed methods, we identify important simulation parameters for our agent-based model, show that two-dimensional and three-dimensional results are not directly equivalent, and highlight the macroscale implications of cell shrinkage. Looking forward, we anticipate that the methods and results presented here will contribute to significant future research in modeling stochastic cell behavior in the multiscale setting.