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Computational exploration of treadmilling and protrusion growth observed in fire ant rafts

Fig 4

Comparing Protrusion Dynamics.

(A-B) Probability mass functions are shown for (A) the average protrusion widths, W, and (B) growths rates, V of more than 400 experimental observations (grey) and numerical observations (light red) each. Here, R = 0.9 ℓ, η = 0.2 and . (C-D) The direction of motion of free ants on experimental sections of (C) a protrusion and (D) the bulk of a raft are visually illustrated with the color of a free agent representing its direction of travel during one frame-to-frame observation. (E-F) The same visual analysis is made for sections of (E) a protrusion and (F) the bulk of a simulated raft, where the direction of travel is measured between one timestep. (C-F) Colors are assigned according to orientation based on the color wheel depicted in (E). (G-J) 2D velocity distributions are shown, courtesy of Wagner, et al. (2021) [14]. (G-H) correspond to (C-D), respectively, while (I-J) are the ensembled results from 11 in silico protrusions and on the order of 100,000 discrete velocity observations, each. A simulated protrusion at the start (K) and end (L) of a roughly 21 min duration exhibits how directional motion on protrusions culminates in clustering of freely active agents (black circles) at the tip and rapid, anisotropic growth. (A-B,C-D,G-H) Experimental results are courtesy of Wagner, et al. (2021) [14]. Scale bars in (C,E,K,L) represent 10 ℓ. All simulated rafts were initiated as circles such that the in silico protrusion growths depicted (and from which data were collected) occurred stochastically.

doi: https://doi.org/10.1371/journal.pcbi.1009869.g004