A spatial model of the efficiency of T cell search in the influenza-infected lung

Abstract

Emerging strains of influenza, such as avian H5N1 and 2009 pandemic H1N1, are more virulent than seasonal H1N1 influenza, yet the underlying mechanisms for these differences are not well understood. Subtle differences in how a given strain interacts with the immune system are likely a key factor in determining virulence. One aspect of the interaction is the ability of T cells to locate the foci of the infection in time to prevent uncontrolled expansion. Here, we develop an agent based spatial model to focus on T cell migration from lymph nodes through the vascular system to sites of infection. We use our model to investigate whether different strains of influenza modulate this process. We calibrate the model using viral and chemokine secretion rates we measure in vitro together with values taken from literature. The spatial nature of the model reveals unique challenges for T cell recruitment that are not apparent in standard differential equation models. In this model comparing three influenza viruses, plaque expansion is governed primarily by the replication rate of the virus strain, and the efficiency of the T cell search-and-kill is limited by the density of infected epithelial cells in each plaque. Thus for each virus there is a different threshold of T cell search time above which recruited T cells are unable to control further expansion. Future models could use this relationship to more accurately predict control of the infection.

A Computational Technique to Estimate Within-Host Productively Infected Cell Lifetimes in Emerging Viral Infections

“…This would enable us to couple within host models of viral kinetics to models of how viruses spread between hosts, enabling modelling of diseases at multiple scales. Our work also suggests a generality and power of dynamical models in capturing rich features of diverse complex systems ranging from immune systems and intra-cellular regulatory networks to global scientific collaboration networks [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34].…”

An Immune System Inspired Theory for Crime and Violence in Cities

“…The dynamics of the immune system is typically modelled using non-linear dynamical models (using ordinary differential equations and agent-based models) that simulate the spread of disease within an organism [3,8,13,[16][17][18].…”

“…Our work can be extended to determine an optimal size of police stations (number of police per police station) and how that should scale with the size of the city. In an immunological theory of cities, these lymph nodes would be police stations and patrolling police would be analogous to circulating T-cells [8,13]. Our theory can be extended to make theoretical predictions of optimal placement and size of police stations for a particular city size.…”

“…Other specialized anatomical structures like iBALTs (inducible bronchus-associated lymphoid tissue) are also created dynamically by the immune system close to previous sites of infection so as to reduce response times during future attacks (which may likely take place close to previous sites of attack) [24]. In previous work we have shown how the immune system optimally trafficks T-cells to infected sites to minimize the time taken to eliminate pathogens [8,13]. Our work may be able to suggest immune system inspired "algorithms" to optimally route police to locations of crime.…”

“…This approach has been also used successfully to derive results for how the immune response against pathogens scales with the size of the complex system (in this case the size of the host organism) [3,4] and how the number of crimes and the response of police against criminals scales with the size of cities [2]. 2) Previous work using the immune system as an inspiration has shown the optimal way to locate structures similar to lymph nodes (anatomical structures used by the biological immune system) that facilitate detection of adverse events and response against them in human-engineered distributed systems like mobile networks, peer-to-peer networks and social networks [3][4][5][6][7][8][9][10][11][12][13][14]. An immune system inspired theory of cities may inform strategies on how to optimally locate police and police stations, and design efficient policing strategies.…”