Cytokine network ecology: towards a dynamic understanding of immune responses to co-infection

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Cytokines are important molecules for the organisation of immune responses: they activate cells to divide and to attack infectious agents, and they sometimes act directly against pathogens. Cytokines thus strongly influence how (& how quickly) parasites are killed. Multiple cytokine signals are involved in every immune response -- an intricate network of positive and negative feedback loops that determine how well the host manages to fight infection. These feedback loops, and the time lags inherent in such a signalling system, can make the immune system difficult to study. Data collected at any one time point or analysed only one variable at a time simply cannot reveal the way that different cytokines must interact to generate an observed system-wide immune response. With this project, I propose to treat the host as a closed ecological system in which ecological and evolutionary analyses can identify how cytokines work together to generate effective versus pathological immune responses. Why study cytokines to learn about the whole immune system? The appeal of cytokines is three-fold: - cytokines are immunologically relevant -- e.g., nearly all immunologists measure cytokines to help them infer the function of cells, no matter which host cell type nor which infection or autoimmune condition is under study. - cytokines are analytically tractable -- the same <10 cytokines are implicated in all described infectious and autoimmune diseases. Details of which cells and membrane bound molecules (& less abundant or less well described cytokines) are involved does change across these systems, but the fact that the same cytokines are always important is striking. Better to model these <10 than to model the many cell populations, for example, that vary across context. - and (for evolutionary studies), because cytokines choose parasite-killing mechanisms, they strongly influence host health and survival. The proposed project aims to integrate real data on multiple cytokines of co-infected mice into a network framework, making use of optimality-based and probabilistic mathematical methods. Optimality methods are appropriate because we expect that there has been natural selection on immune systems, particularly to optimise their ability to multi-task / for example, to optimally manage protozoan-helminth co-infection. At the same time, probabilistic methods are also appropriate because cytokines form a probabilistic network, with a lot of variability and chance events. The probabilistic statistical methods that I propose to use have been successfully applied to environmental science (for example, to predict damage to coral reefs given multiple interacting environmental factors). Testing optimality predictions has greatly deepened our understanding of the evolutionary biology of everything from bird song to the development of antibiotic resistance. The combined application of these predictive analytical methods to immunological molecules will also bear fruit: an integrated understanding of immune system functioning that links to the health of hosts.