  Spatial organization is a core challenge for all large agent-based models
with local interactions. In biological tissue models, spatial search and
reinsertion are frequently reported as the most expensive steps of the
simulation. One of the main methods utilized in order to maintain both
favorable algorithmic complexity and accuracy is spatial hierarchies. In this
paper, we seek to clarify to which extent the choice of spatial tree affects
performance, and also to identify which spatial tree families are optimal for
such scenarios. We make use of a prototype of the new BioDynaMo tissue
simulator for evaluating performances as well as for the implementation of the
characteristics of several different trees.

Computational Geometry;Data Structures and Algorithms