Identifying how much individual-level immunological detail is required for population-level insight into evolutionary trajectories.
Within-host interactions among parasites, target cells/tissues and immune responses are critical factors shaping disease severity and transmission (e.g., [1-3]). Quantitatively partitioning the relative roles of resource limitation and immune responses in controlling parasite density is fundamental to understanding parasite evolution and optimizing vaccine development.
For some parasites, such as Plasmodium species, the causal agents of malaria, red blood cells (RBCs) are the target cell population that is measurably depleted over the course of infection. The wealth of available data on daily RBC densities in experimentally-infected animals makes the development of resource-based models relatively straightforward: loss of host cells due to exploitation by the parasite can be directly quantified, and replenishment can be measured, or inferred once the RBC dynamics are understood in the absence of infection (e.g., ). Modeling immunity is considerably more challenging. Relevant immune dynamics are often difficult to quantify, especially given the huge complexity of immune responses. Given this challenge, a number of theoretical studies have taken a more phenomenological approach, modeling immune responses as parasite-killing rates that can vary over the course of infections.
Relevant immune dynamics are often difficult to quantify, especially given the huge complexity of immune responses.
The phenomenological approach has proven powerful. For example, the patterns of immune killing that emerged from two fundamentally different frameworks for modeling within-host dynamics of rodent malaria were qualitatively identical [5, 6]. Both models identified the same early window of peak immune killing activity as an important regulator of within-host dynamics and transmission potential. These predictions for the timing and magnitude of parasite mortality can direct the empirical search for immune components with matching patterns of activity.
Still, a phenomenological approach has limitations. It does not supply mechanistic models that can predict the outcome of experimental perturbations. More specifically, when mechanistic details are absent, the reciprocal ecological feedbacks that can promote or constrain evolutionary change  are broken, and predictions become impossible.
Models of within-host dynamics that will allow us to make empirically testable predictions about parasite transmission and, therefore, evolution are important.
With parallel use of phenomenological and mechanistic models and iteration of theory with new empirical studies, our RCN will build and refine realistic yet tractable models of within-host dynamics that will allow us to make empirically testable predictions about parasite transmission and therefore evolution. There is precedent of such work for protozoal infections (e.g., [8, 9]) and viruses such as HIV (e.g., [10, 11]), and experimental evolution can be used to test the predictions that arise (e.g., ). However, for most parasites and pathogens, immunological features of within-host dynamics remain to be effectively modeled and connections to between-host transmission made. The design of the necessary experiments, the suite of immune effectors to measure, and when to measure them will require strong coordination among multiple modelers and empiricists.
- McQueen, P.G. and F.E. McKenzie, Host control of malaria infections: constraints on immune and erythropoeitic response kinetics. PLoS Comput Biol, 2008. 4(8): p. e1000149.
- Pawelek, K.A., et al., Modeling within-host dynamics of influenza virus infection including immune responses. PLoS Comput Biol, 2012. 8(6): p. e1002588.
- Haydon, D.T., et al., Top-down or bottom-up regulation of intra-host blood-stage malaria: do malaria parasites most resemble the dynamics of prey or predator? Proc R Soc London B, 2003. 270: p. 289-298.
- Savill, N.J., W. Chadwick, and S.E. Reece, Quantitative analysis of mechanisms that govern red blood cell age structure and dynamics during anaemia. PLoS Comput Biol, 2009. 5(6): p. e1000416.
- Metcalf, C.J., et al., Partitioning regulatory mechanisms of within-host malaria dynamics using the effective propagation number. Science, 2011. 333(6045): p. 984-8.
- Mideo, N., et al., Bridging Scales in the Evolution of Infectious Disease Life Histories: Application. Evolution, 2011. 65(11): p. 3298-3310.
- Mideo, N., S. Alizon, and T. Day, Linking within- and between-host dynamics in the evolutionary epidemiology of infectious diseases. Trends Ecol Evol, 2008. 23(9): p. 511-7.
- Antia, R., M.A. Nowak, and R.M. Anderson, Antigenic variation and the within-host dynamics of parasites. PNAS U S A, 1996. 93(3): p. 985-9.
- Gjini, E., et al., Critical interplay between parasite differentiation, host immunity, and antigenic variation in trypanosome infections. Am Nat, 2010. 176(4): p. 424-39.
- Fryer, H.R., et al., Modelling the evolution and spread of HIV immune escape mutants. Plos Pathogens, 2010. 6(11): p. e1001196.
- Ganusov, V.V., et al., Fitness costs and diversity of the cytotoxic T lymphocyte (CTL) response determine the rate of CTL escape during acute and chronic phases of HIV infection. J Virol, 2011. 85(20): p. 10518-28.
- Barclay, V.C., et al., The evolutionary consequences of blood-stage vaccination on the rodent malaria Plasmodium chabaudi. PLoS Bio, 2012. 10(7): p. e1001368.