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Persistence and Adaptation in Immunity: T Cells Balance the Extent and Thoroughness of Search.
- 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]
- Subjects :
- T cells
IMMUNE system
IMMUNE response
DENDRITIC cells
LYMPH nodes
BROWNIAN motion
Subjects
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