Editing Agent-based model (section)

===Multi-scale modelling===
One strength of agent-based modelling is its ability to mediate information flow between scales. When additional details about an agent are needed, a researcher can integrate it with models describing the extra details. When one is interested in the emergent behaviours demonstrated by the agent population, they can combine the agent-based model with a continuum model describing population dynamics. For example, in a study about CD4+ T cells (a key cell type in the adaptive immune system),<ref>{{Cite journal|last1=Wertheim|first1=Kenneth Y.|last2=Puniy|first2=Bhanwar Lal|last3=Fleur|first3=Alyssa La|last4=Shah|first4=Ab Rauf|last5=Barberis|first5=Matteo|last6=Helikar|first6=Tomáš|date=2021-08-03|title=A multi-approach and multi-scale platform to model CD4+ T cells responding to infections|journal=PLOS Computational Biology|language=en|volume=17|issue=8|pages=e1009209|doi=10.1371/journal.pcbi.1009209|pmid=34343169|pmc=8376204|bibcode=2021PLSCB..17E9209W|issn=1553-7358 |doi-access=free }}</ref> the researchers modelled biological phenomena occurring at different spatial (intracellular, cellular, and systemic), temporal, and organizational scales (signal transduction, gene regulation, metabolism, cellular behaviors, and cytokine transport). In the resulting modular model, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models, cell population dynamics are described by an agent-based model, and systemic cytokine concentrations by ordinary differential equations. In this multi-scale model, the agent-based model occupies the central place and orchestrates every stream of information flow between scales.