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Persistence and Adaptation in Immunity: T Cells Balance the Extent and Thoroughness of Search.

Authors :
Fricke, G. Matthew
Letendre, Kenneth A.
Moses, Melanie E.
Cannon, Judy L.
Source :
PLoS Computational Biology; 3/18/2016, Vol. 12 Issue 3, p1-23, 23p, 6 Charts, 5 Graphs
Publication Year :
2016

Abstract

Effective search strategies have evolved in many biological systems, including the immune system. T cells are key effectors of the immune response, required for clearance of pathogenic infection. T cell activation requires that T cells encounter antigen-bearing dendritic cells within lymph nodes, thus, T cell search patterns within lymph nodes may be a crucial determinant of how quickly a T cell immune response can be initiated. Previous work suggests that T cell motion in the lymph node is similar to a Brownian random walk, however, no detailed analysis has definitively shown whether T cell movement is consistent with Brownian motion. Here, we provide a precise description of T cell motility in lymph nodes and a computational model that demonstrates how motility impacts T cell search efficiency. We find that both Brownian and Lévy walks fail to capture the complexity of T cell motion. Instead, T cell movement is better described as a correlated random walk with a heavy-tailed distribution of step lengths. Using computer simulations, we identify three distinct factors that contribute to increasing T cell search efficiency: 1) a lognormal distribution of step lengths, 2) motion that is directionally persistent over short time scales, and 3) heterogeneity in movement patterns. Furthermore, we show that T cells move differently in specific frequently visited locations that we call “hotspots” within lymph nodes, suggesting that T cells change their movement in response to the lymph node environment. Our results show that like foraging animals, T cells adapt to environmental cues, suggesting that adaption is a fundamental feature of biological search. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1553734X
Volume :
12
Issue :
3
Database :
Complementary Index
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
113873096
Full Text :
https://doi.org/10.1371/journal.pcbi.1004818