AgentBasedModeling.jl: A tool for stochastic simulation of structured population dynamics

Agent-based modeling is a powerful approach for understanding cellular systems. Yet, many existing frameworks treat cell-level and population behaviors separately, overlooking their interplay. We present AgentBasedModeling.jl, a Julia package for simulating stochastic, continuous-time agent-based models that integrate intracellular processes with population dynamics. In our stochastic framework, agents evolve according to general continuous-time jump-diffusion dynamics and interact via continuous-rate jump processes. It supports flexible specification of the underlying measure-valued process of intracellular reaction networks and cell growth, capturing events such as cell division, death, intercellular communication, and environmental interactions. We demonstrate the use of the package by validating it on models of stochastic gene expression in growing cells and providing new insights into cell-cell communication and stochastic phage infection. Our tool provides a flexible and efficient platform for exploring how single-cell stochasticity drives emergent population-level behaviors in structured biological systems.