Background Biological evolution exhibits an extraordinary capability to adapt organisms to their environments. The explanation for this often takes for granted that random genetic variation produces at least some beneficial phenotypic variation in which natural selection can act. Such genetic evolvability could itself be a product of evolution, but it is widely acknowledged that the immediate selective gains of evolvability are small on short timescales. So how do biological systems come to exhibit such extraordinary capacity to evolve? One suggestion is that adaptive phenotypic plasticity makes genetic evolution find adaptations faster. However, the need to explain the origin of adaptive plasticity puts genetic evolution back in the driving seat, and genetic evolvability remains unexplained. Results To better understand the interaction between plasticity and genetic evolvability, we simulate the evolution of phenotypes produced by gene-regulation network-based models of development. First, we show that the phenotypic variation resulting from genetic and environmental perturbation are highly concordant. This is because phenotypic variation, regardless of its cause, occurs within the relatively specific space of possibilities allowed by development. Second, we show that selection for genetic evolvability results in the evolution of adaptive plasticity and vice versa. This linkage is essentially symmetric but, unlike genetic evolvability, the selective gains of plasticity are often substantial on short, including within-lifetime, timescales. Accordingly, we show that selection for phenotypic plasticity can be effective in promoting the evolution of high genetic evolvability. Conclusions Without overlooking the fact that adaptive plasticity is itself a product of genetic evolution, we show how past selection for plasticity can exercise a disproportionate effect on genetic evolvability and, in turn, influence the course of adaptive evolution.
Cell differentiation in multicellular organisms requires cells to respond to complex combinations of extracellular cues, such as morphogen concentrations. Some models of phenotypic plasticity conceptualise the response as a relatively simple function of a single environmental cues (e.g. a linear function of one cue), which facilitates rigorous analysis. Conversely, more mechanistic models such those implementing GRNs allows for a more general class of response functions but makes analysis more difficult. Therefore, a general theory describing how cells integrate multi-dimensional signals is lacking. In this work, we propose a theoretical framework for understanding the relationships between environmental cues (inputs) and phenotypic responses (outputs) underlying cell plasticity. We describe the relationship between environment and cell phenotype using logical functions, making the evolution of cell plasticity equivalent to a simple categorisation learning task. This abstraction allows us to apply principles derived from learning theory to understand the evolution of multi-dimensional plasticity. Our results show that natural selection is capable of discovering adaptive forms of cell plasticity associated with complex logical functions. However, developmental dynamics cause simpler functions to evolve more readily than complex ones. By using conceptual tools derived from learning theory we show that this developmental bias can be interpreted as a learning bias in the acquisition of plasticity functions. Because of that bias, the evolution of plasticity enables cells, under some circumstances, to display appropriate plastic responses to environmental conditions that they have not experienced in their evolutionary past. This is possible when the selective environment mirrors the bias of the developmental dynamics favouring the acquisition of simple plasticity functions-an example of the necessary conditions for generalisation in learning systems. These results illustrate the functional parallelisms between learning in neural networks and the action of natural selection on environmentally sensitive gene regulatory networks. This offers a theoretical framework for the evolution of plastic responses that integrate information from multiple cues, a phenomenon that underpins the evolution of multicellularity and developmental robustness.
The concept of combustion under oxy-fuel conditions has the potential to reduce greenhouse gas emissions. For the design of combustion devices operating under these conditions, a good understanding of fuel oxidation behavior in terms of chemical kinetic mechanisms is useful. For the oxidation of the main component of natural gas and coal devolatilization products, i. e. methane, various chemical mechanisms are available in the literature validated mostly with experiments using air, and none of them is developed particularly or has been validated extensively for oxy-methane combustion. An important prerequisite for model assessment is high-quality data typically obtained from resource-and time-consuming measurements. The aim of this study is to identify the best methane mechanism for oxy-fuel combustion from a set of models available in the literature with a minimum number of measurements. Five chemical models, which have been validated for the oxidation of methane/air mixtures, are compared in terms of their performance for extinction strain rates, ignition delay times, and laminar burning velocities of oxy-methane mixtures. A model-based experimental design method, i. e. Akaike Weights Design Criterion, is applied to determine the optimal potential measurements. Ideally, at the conditions of designed experiments, model predictions are nicely separated and thus the best model can be identified by comparison with these measurements. It is shown that the employed experimental design strategy identifies informative experiments for model discrimination efficiently. While measurements of extinction strain rates are proposed to be carried out for flames with small methane mass fractions of the fuel stream and oxygen mass fractions of the oxidizer stream, shock tube experiments are evaluated as equally useful for model discrimination over the investigated range of conditions. Measurements of flame speeds are designed at very small and very large equivalence ratios particularly at relatively high pressures.
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