AgentBasedModeling.jl: A tool for stochastic simulation of structured population dynamics
- 1Department of Mathematics, Imperial College London, London, United Kingdom
Abstract
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.
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BibTeX
@article{piho2026agentbasedmodelingjl,
title = {{AgentBasedModeling.jl: A tool for stochastic simulation of structured population dynamics}},
author = {Paul Piho and Philipp Thomas},
journal = {PLOS Computational Biology},
year = {2026},
month = jul,
doi = {10.1371/journal.pcbi.1014366},
url = {https://doi.org/10.1371/journal.pcbi.1014366},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - AgentBasedModeling.jl: A tool for stochastic simulation of structured population dynamics
AU - Paul Piho
AU - Philipp Thomas
PY - 2026
DA - 2026/07/29
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014366
UR - https://doi.org/10.1371/journal.pcbi.1014366
AB - 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.
SN - 1553-734X
SN - 1553-7358
ER -