Periodic Reporting for period 4 - AdaptiveResponse (The evolution of adaptive response mechanisms)
Período documentado: 2023-06-01 hasta 2023-11-30
In an era of rapid climate change, there is a pressing need to understand whether and how organisms are able to adapt to novel environments. Such understanding is hampered by a major divide in the life sciences. Disciplines like systems biology or neurobiology make rapid progress in unravelling the mechanisms underlying the responses of organisms to their environment, but this knowledge is insufficiently integrated with evolutionary theory. Current evolutionary models focus on the response patterns themselves, largely neglecting the structures and mechanisms producing these patterns. The goal of the project was to develop a new, mechanism-oriented framework that views the architecture of adaptation, rather than the resulting responses, as the primary target of natural selection. Such a change in perspective requires a major rethink. Adaptive responses to environmental change are not mediated by single regulatory elements but by whole networks: gene regulatory networks determine the expression of genes; metabolic networks orchestrate a huge number of metabolic reactions; the immune network responds to the attacks of pathogens; and neural networks steer behaviour and allow to learn from experience. The evolution of such networks is governed by different principles than the evolution of single traits. The reason is that the network as a whole is evolving, making it impossible to study its evolution from a gene-centred perspective.
The main objective of the project was to demonstrate that the evolutionary dynamics of mechanistic models (based on regulatory networks) differs profoundly from that of traditional models of evolution, thus providing new insights into (a) plasticity, the ability of individuals to adaptively respond to their local environment, and (b) evolvability, the ability of populations to adapt genetically to environmental change. More specifically, the project aimed at testing three hypotheses: [H1] In contrast to conventional wisdom, plasticity boosts (rather than hampers) evolvability and speeds up evolution by orders of magnitude. [H2] Plasticity and evolvability are evolvable properties that mutually reinforce each other. [H3] The evolution of plasticity tends to create polymorphism (i.e. individual variation within a population), and polymorphic populations have very different (and often faster) evolutionary dynamics than monomorphic populations.
These hypotheses were tested (mainly theoretically, by means of individual-based simulations) in three parallel lines of investigation: [WP1] The relation between evolvability and plasticity; [WP2] Evolution of learning and cultural change; [WP3] Implications for the interplay of ecology and evolution. To this end, 8 PhD students and 19 MSc students investigated a broad diversity of models. These studies provided many specific insights, e.g. into the evolution of antibiotic resistance or the evolution of social distancing after the advent of a novel pathogen. Most importantly, however, virtually all studies provided strong support for hypotheses H1 to H3, which implies that evolutionary theory does indeed need a rethink. In a series of articles, we synthesised the project by explaining why mechanistic modelling profoundly changes our view of evolution. We are currently writing a textbook to make this new way of modelling more easily accessible to the scientific community.
The main objective of the project was to demonstrate that the evolutionary dynamics of mechanistic models (based on regulatory networks) differs profoundly from that of traditional models of evolution, thus providing new insights into (a) plasticity, the ability of individuals to adaptively respond to their local environment, and (b) evolvability, the ability of populations to adapt genetically to environmental change. More specifically, the project aimed at testing three hypotheses: [H1] In contrast to conventional wisdom, plasticity boosts (rather than hampers) evolvability and speeds up evolution by orders of magnitude. [H2] Plasticity and evolvability are evolvable properties that mutually reinforce each other. [H3] The evolution of plasticity tends to create polymorphism (i.e. individual variation within a population), and polymorphic populations have very different (and often faster) evolutionary dynamics than monomorphic populations.
These hypotheses were tested (mainly theoretically, by means of individual-based simulations) in three parallel lines of investigation: [WP1] The relation between evolvability and plasticity; [WP2] Evolution of learning and cultural change; [WP3] Implications for the interplay of ecology and evolution. To this end, 8 PhD students and 19 MSc students investigated a broad diversity of models. These studies provided many specific insights, e.g. into the evolution of antibiotic resistance or the evolution of social distancing after the advent of a novel pathogen. Most importantly, however, virtually all studies provided strong support for hypotheses H1 to H3, which implies that evolutionary theory does indeed need a rethink. In a series of articles, we synthesised the project by explaining why mechanistic modelling profoundly changes our view of evolution. We are currently writing a textbook to make this new way of modelling more easily accessible to the scientific community.
[WP1] Here, we studied the relationship between plasticity and evolvability. On the conceptual side, we presented our views in three ‘opinion’ papers that provide arguments in favour of a mechanistic view of phenotypic plasticity, epigenetic inheritance and evolvability. The ideas developed were later worked out in various modelling studies, which all arrived at the conclusion that the (epi)genetic system can evolve in such a way that plasticity and evolvability are enhanced simultaneously. Here, we only mention our studies on the evolution of the mutation process. It is a kind of dogma in molecular genetics that mutations are random and not related to the ‘evolutionary needs’ of an organism. By means of a surprisingly simple gene-regulatory network model, we could show that this view needs to be modified. Even if the mutations are random at the genetic level, the regulatory network systematically evolves in such a way that the phenotypic effects of these mutations are not random but biased towards higher-fitness outcomes, enhancing the evolvability of the population. Other models on the evolution of the mutation rate also arrived at the conclusion that evolvability is not constant but evolvable. These theoretical predictions were partly tested in the lab. We showed, for example, that the mutation rate towards antibiotic resistance is strongly temperature dependent, a finding of potential medical importance.
[WP2] Learning is one of the most important mechanisms to respond adaptively to novel conditions. . We made a major methodological advance in developing a new framework for the evolution of individual and social learning, which considers the evolution of neural networks that are capable of learning. Although learning is based on a simple (but biologically realistic) mechanism, effective learning rapidly evolved. The extension to social learning is of special importance for the field of cultural evolution, where progress is hampered by the lack of convincing approaches to the spread of cultural information. Our framework can be the basis for a new generation of models for cultural evolution.
[WP3] All our studies show that in mechanistic models evolution is much faster than previously thought, making the timescale of evolution similar to the timescale of ecological processes. This leads to an intriguing interplay of ecology and evolution with very different properties than previously thought. In various studies, we investigated the evolution of animal movement in response to resources, competitors, and predators. Our neural network model has the advantage that its outcome can be compared with movement patterns in the field. Surprisingly, even the simplest networks allow the rapid evolution of highly efficient (but previously unanticipated) movement patterns. Accordingly, environmental change can trigger a rapid change in movement strategies. For example, social distancing evolved within a few generations after the introduction of a novel pathogen. In addition, virtually all our mechanistic models lead to the emergence of systematic variation between individuals. In various studies (e.g. on the evolution of parental care strategies), we worked out in detail how and why such polymorphism changes the eco-evolutionary dynamics.
[WP2] Learning is one of the most important mechanisms to respond adaptively to novel conditions. . We made a major methodological advance in developing a new framework for the evolution of individual and social learning, which considers the evolution of neural networks that are capable of learning. Although learning is based on a simple (but biologically realistic) mechanism, effective learning rapidly evolved. The extension to social learning is of special importance for the field of cultural evolution, where progress is hampered by the lack of convincing approaches to the spread of cultural information. Our framework can be the basis for a new generation of models for cultural evolution.
[WP3] All our studies show that in mechanistic models evolution is much faster than previously thought, making the timescale of evolution similar to the timescale of ecological processes. This leads to an intriguing interplay of ecology and evolution with very different properties than previously thought. In various studies, we investigated the evolution of animal movement in response to resources, competitors, and predators. Our neural network model has the advantage that its outcome can be compared with movement patterns in the field. Surprisingly, even the simplest networks allow the rapid evolution of highly efficient (but previously unanticipated) movement patterns. Accordingly, environmental change can trigger a rapid change in movement strategies. For example, social distancing evolved within a few generations after the introduction of a novel pathogen. In addition, virtually all our mechanistic models lead to the emergence of systematic variation between individuals. In various studies (e.g. on the evolution of parental care strategies), we worked out in detail how and why such polymorphism changes the eco-evolutionary dynamics.
*A new modelling paradigm for the evolution of movement, allowing the study of the interplay of ecology and evolution in a range of modelling studies.
* A new class of neural network models for the evolution of individual and social learning, allowing more realistic approaches to the study of cultural evolution.
* A mechanistic approach to plasticity and evolvability, showing that both concepts are closely intertwined and producing novel insights such as the ability of evolution to generalise over environments and the evolution of mutations with fitness-enhancing phenotypic effects.
* The finding that mechanistic adaptive response models universally lead to the emergence of individual differences, leading to a new theory for the evolution of ‘animal personalities’.
* A new class of neural network models for the evolution of individual and social learning, allowing more realistic approaches to the study of cultural evolution.
* A mechanistic approach to plasticity and evolvability, showing that both concepts are closely intertwined and producing novel insights such as the ability of evolution to generalise over environments and the evolution of mutations with fitness-enhancing phenotypic effects.
* The finding that mechanistic adaptive response models universally lead to the emergence of individual differences, leading to a new theory for the evolution of ‘animal personalities’.