The interplay between immunity and aging in Drosophila

Here, we provide a brief review of the mechanistic connections between immunity and aging—a fundamental biological relationship that remains poorly understood—by considering two intertwined questions: how does aging affect immunity, and how does immunity affect aging? On the one hand, aging contributes to the deterioration of immune function and predisposes the organism to infections (“immuno-senescence”). On the other hand, excessive activation of the immune system can accelerate degenerative processes, cause inflammation and immunopathology, and thus promote aging (“inflammaging”). Interestingly, several recent lines of evidence support the hypothesis that restrained or curbed immune activity at old age (that is, optimized age-dependent immune homeostasis) might actually improve realized immune function and thereby promote longevity. We focus mainly on insights from Drosophila, a powerful genetic model system in which both immunity and aging have been extensively studied, and conclude by outlining several unresolved questions in the field

A neural network model for the evolution of learning in changing environments

The ability to learn from past experience is an important adaptation, but how natural selection shapes learning is not well understood. Here, we present a novel way of modelling learning using small neural networks and a simple, biology-inspired learning algorithm. Learning affects only part of the network, and it is governed by the difference between expectations and reality. We used this model to study the evolution of learning under various environmental conditions and different scenarios for the trade-off between exploration (learning) and exploitation (foraging). Efficient learning regularly evolved in our individual-based simulations. However, in line with previous studies, the evolution of learning was less likely in relatively constant environments (where genetic adaptation alone can lead to efficient foraging) or in the case of short-lived organisms (that cannot afford to spend much of their lifetime on exploration). Once learning did evolve, the characteristics of the learning strategy (the duration of the learning period and the learning rate) and the average performance after learning were surprisingly little affected by the frequency and/or magnitude of environmental change. In contrast, an organism’s lifespan and the distribution of resources in the environment had a strong effect on the evolved learning strategy. Interestingly, a longer learning period did not always lead to better performance, indicating that the evolved neural networks differ in the effectiveness of learning. Overall, however, we showed that a biologically inspired, yet relatively simple, learning mechanism can evolve to lead to an efficient adaptation in a changing environment.Author Summary The ability to learn from experience is an important adaptation. However, it is still unclear how learning is shaped by natural selection. Here, we present a novel way of modelling the evolution of learning using small neural networks and a simple, biology-inspired learning mechanism. Computer simulations reveal that efficient learning readily evolves in this model. However, the evolution of learning is less likely in relatively constant environments (where evolved inborn preferences can guide animal behaviour) and in short-lived organisms (that cannot afford to spend much of their lifetime on learning). If learning does evolve, the evolved learning strategy is strongly affected by the lifespan and environmental richness but surprisingly little by the rate and degree of environmental change. In summary, we show that a simple and biologically plausible mechanism can help understand the evolution of learning and the structure of the evolved learning strategies.Competing Interest StatementThe authors have declared no competing interest