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.

Geographic and cross-national variation in the frequency of intrastate armed conflict and civil war is a subject of great interest. Previous theory on this variation has focused on the influence on human behaviour of climate, resource competition, national wealth, and cultural characteristics. We present the parasite-stress model of intrastate conflict, which unites previous work on the correlates of intrastate conflict by linking frequency of the outbreak of such conflict, including civil war, to the intensity of infectious disease across countries of the world. High intensity of infectious disease leads to the emergence of xenophobic and ethnocentric cultural norms. These cultures suffer greater poverty and deprivation due to the morbidity and mortality caused by disease, and as a result of decreased investment in public health and welfare. Resource competition among xenophobic and ethnocentric groups within a nation leads to increased frequency of civil war. We present support for the parasite-stress model with regression analyses. We find support for a direct effect of infectious disease on intrastate armed conflict, and support for an indirect effect of infectious disease on the incidence of civil war via its negative effect on national wealth. We consider the entanglements of feedback of conflict into further reduced wealth and increased incidence of disease, and discuss implications for international warfare and global patterns of wealth and imperialism.

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Desert seed-harvester ants, genus Pogonomyrmex, are central place foragers that search for resources collectively. We quantify how seed harvesters exploit the spatial distribution of seeds to improve their rate of seed collection. We find that foraging rates are significantly influenced by the clumpiness of experimental seed baits. Colonies collected seeds from larger piles faster than randomly distributed seeds. We developed a method to compare foraging rates on clumped versus random seeds across three Pogonomyrmex species that differ substantially in forager population size. The increase in foraging rate when food was clumped in larger piles was indistinguishable across the three species, suggesting that species with larger colonies are no better than species with smaller colonies at collecting clumped seeds. These findings contradict the theoretical expectation that larger groups are more efficient at exploiting clumped resources, thus contributing to our understanding of the importance of the spatial distribution of food sources and colony size for communication and organization in social insects.

Cell motility is a fundamental process crucial for function in many cell types, including T cells. T cell motility is critical for T cell-mediated immune responses, including initiation, activation, and effector function. While many extracellular receptors and cytoskeletal regulators have been shown to control T cell migration, relatively few signaling mediators have been identified that can modulate T cell motility. In this study, we find a previously unknown role for PKCθ in regulating T cell migration to lymph nodes. PKCθ localizes to the migrating T cell uropod and regulates localization of the MTOC, CD43 and ERM proteins to the uropod. Furthermore, PKCθ-deficient T cells are less responsive to chemokine induced migration and are defective in migration to lymph nodes. Our results reveal a novel role for PKCθ in regulating T cell migration and demonstrate that PKCθ signals downstream of CCR7 to regulate protein localization and uropod formation.

Two-photon (2P) microscopy provides immunologists with 3D video of the movement of lymphocytes in vivo. Motility parameters extracted from these videos allow detailed analysis of lymphocyte motility in lymph nodes and peripheral tissues. However, standard parametric statistical analyses such as the Student’s t-test are often used incorrectly, and fail to take into account confounds introduced by the experimental methods, potentially leading to erroneous conclusions about T cell motility. Here, we compare the motility of WT T cell versus PKCθ-/-, CARMA1-/-, CCR7-/-, and PTX-treated T cells. We show that the fluorescent dyes used to label T cells have significant effects on T cell motility, and we demonstrate the use of factorial ANOVA as a statistical tool that can control for these effects. In addition, researchers often choose between the use of “cell-based” parameters by averaging multiple steps of a single cell over time (e.g. cell mean speed), or “step-based” parameters, in which all steps of a cell population (e.g. instantaneous speed) are grouped without regard for the cell track. Using mixed model ANOVA, we show that we can maintain cell-based analyses without losing the statistical power of step-based data. We find that as we use additional levels of statistical control, we can more accurately estimate the speed of T cells as they move in lymph nodes as well as measure the impact of individual signaling molecules on T cell motility. As there is increasing interest in using computational modeling to understand T cell behavior in in vivo, these quantitative measures not only give us a better determination of actual T cell movement, they may prove crucial for models to generate accurate predictions about T cell behavior.

Organisms that can more effectively exploit information about their environments to improve foraging success have a competitive and selective advantage over others. Thus, animals are expected to evolve strategies that use information to improve foraging success. We study how desert seed harvesters use information to improve the rate they collect seeds, which contributes to the colony's fitness. Through field studies and computer simulations, we manipulated the information available to the ants in the spatial distribution of seeds and measured the resulting foraging rates. In field observations, seeds were collected faster when seeds could be found with less information. The increase in foraging rate with clustering was indistinguishable across three related species that vary over an order of magnitude in colony size. Computer simulations show similar systematic increases in foraging rates when information about the food location is communicated among nestmates.

Evolutionary algorithms can adapt the behavior of individuals to maximize the fitness of cooperative multi-agent teams. We use a genetic algorithm (GA) to optimize behavior in a team of simulated robots that mimic foraging ants, then transfer the evolved behaviors into physical iAnt robots. We introduce positional and resource detection error models into our simulation to characterize the empirically-measured sensor error in our physical robots. Physical and simulated robots that live in a world with error and use parameters adapted specifically for an error-prone world perform better than robots in the same error-prone world using parameters adapted for an error-free world. Additionally, teams of robots in error-adapted simulations collect resources at the same rate as the physical robots. Our approach extends state-of-theart biologically-inspired robotics, evolving high-level behaviors that are robust to sensor error and meaningful for phenotypic analysis. This work demonstrates the utility of employing evolutionary methods to optimize the performance of distributed robot teams in unknown environments.