title: Persistence and Adaptation in Immunity: T Cells Balance the Extent and Thoroughness of Search.
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.
authors.affiliation: Department of Computer Science, The University of New Mexico, Albuquerque, New Mexico, United States of America.
authors.forename: G Matthew
authors.initials: GM
authors.lastname: Fricke
authors.affiliation: Department of Biology, The University of New Mexico, Albuquerque, New Mexico, United States of America.
authors.forename: Kenneth A
authors.initials: KA
authors.lastname: Letendre
authors.affiliation: Department of Computer Science, The University of New Mexico, Albuquerque, New Mexico, United States of America.|Department of Biology, The University of New Mexico, Albuquerque, New Mexico, United States of America.|External Faculty, Santa Fe Institute, Santa Fe, New Mexico, United States of America.
authors.forename: Melanie E
authors.initials: ME
authors.lastname: Moses
authors.affiliation: Department of Molecular Genetics and Microbiology, The University of New Mexico School of Medicine, Albuquerque, New Mexico, United States of America.|Department of Pathology, The University of New Mexico School of Medicine, Albuquerque, New Mexico, United States of America.
authors.forename: Judy L
authors.initials: JL
authors.lastname: Cannon
mesh_terms: D000223:Adaptation, Psychological / Q000502:physiology; D056704:Adaptive Immunity / Q000276:immunology*; D000818:Animals; D002465:Cell Movement / Q000276:immunology*; D003198:Computer Simulation; D006801:Humans; D007113:Immunity, Innate / Q000276:immunology; D008198:Lymph Nodes / Q000276:immunology* / Q000473:pathology; D018448:Models, Immunological; D015233:Models, Statistical; D013601:T-Lymphocytes / Q000276:immunology*