Models on the Runway: How Do We Make Replicas of the World?

Modeling endosymbioses: Insights and hypotheses from theoretical approaches

Endosymbiotic relationships are pervasive across diverse taxa of life, offering key avenues for eco-evolutionary dynamics. Although a variety of experimental and empirical frameworks have shed light on critical aspects of endosymbiosis, theoretical frameworks (mathematical models) are especially well-suited for certain tasks. Mathematical models can integrate multiple factors to determine the net outcome of endosymbiotic relationships, identify broad patterns that connect endosymbioses with other systems, simplify biological complexity, generate hypotheses for underlying mechanisms, evaluate different hypotheses, identify constraints that limit certain biological interactions, and open new lines of inquiry. This Essay highlights the utility of mathematical models in endosymbiosis research, particularly in generating relevant hypotheses. Despite their limitations, mathematical models can be used to address known unknowns and discover unknown unknowns.

Developing the genotype-to-phenotype relationship in evolutionary theory: A primer of developmental features

For decades, there have been repeated calls for more integration across evolutionary and developmental biology. However, critiques in the literature and recent funding initiatives suggest this integration remains incomplete. We suggest one way forward is to consider how we elaborate the most basic concept of development, the relationship between genotype and phenotype, in traditional models of evolutionary processes. For some questions, when more complex features of development are accounted for, predictions of evolutionary processes shift. We present a primer on concepts of development to clarify confusion in the literature and fuel new questions and approaches. The basic features of development involve expanding a base model of genotype-to-phenotype to include the genome, space, and time. A layer of complexity is added by incorporating developmental systems, including signal-response systems and networks of interactions. The developmental emergence of function, which captures developmental feedbacks and phenotypic performance, offers further model elaborations that explicitly link fitness with developmental systems. Finally, developmental features such as plasticity and developmental niche construction conceptualize the link between a developing phenotype and the external environment, allowing for a fuller inclusion of ecology in evolutionary models. Incorporating aspects of developmental complexity into evolutionary models also accommodates a more pluralistic focus on the causal importance of developmental systems, individual organisms, or agents in generating evolutionary patterns. Thus, by laying out existing concepts of development, and considering how they are used across different fields, we can gain clarity in existing debates around the extended evolutionary synthesis and pursue new directions in evolutionary developmental biology. Finally, we consider how nesting developmental features in traditional models of evolution can highlight areas of evolutionary biology that need more theoretical attention.

EcoEvoApps: Interactive apps for theoretical models in ecology and evolutionary biology

The integration of theory and data drives progress in science, but a persistent barrier to such integration in ecology and evolutionary biology is that theory is often developed and expressed in the form of mathematical models that can feel daunting and inaccessible for students and empiricists with variable quantitative training and attitudes towards math. A promising way to make mathematical models more approachable is to embed them into interactive tools with which one can visually evaluate model structures and directly explore model outcomes through simulation. To promote such interactive learning of quantitative models, we developed EcoEvoApps, a collection of free, open-source, and multilingual R/Shiny apps that include model overviews, interactive model simulations, and code to implement these models directly in R. The package currently focuses on canonical models of population dynamics, species interactions, and landscape ecology. These apps help illustrate fundamental results from theoretical ecology and can serve as valuable teaching tools in classroom settings. We present data from student surveys which show that students rate these apps as useful learning tools, and that using interactive apps leads to substantial gains in students' interest and confidence in working with mathematical models. This points to the potential for interactive activities to make theoretical models more accessible to a wider audience, and thus facilitate the feedback between theory and data across ecology and evolutionary biology.

The landscape of innovation in bacteria, battleships, and beyond

We draw lessons from microbial experimental evolution and naval warfare to improve the understanding of innovation in financial markets. Major financial innovations often arise without explicit societal planning because novel approaches can be favored by markets, in a manner strikingly parallel to natural selection. We utilize the concept of an adaptive landscape to characterize environments that increase the speed and magnitude of innovation. We apply this adaptive landscape framework to innovation in portfolio management. We create a general taxonomy for understanding and nurturing innovation.

Bacterial interspecies interactions modulate pH-mediated antibiotic tolerance

Predicting antibiotic efficacy within microbial communities remains highly challenging. Interspecies interactions can impact antibiotic activity through many mechanisms, including alterations to bacterial physiology. Here, we studied synthetic communities constructed from the core members of the fruit fly gut microbiota. Co-culturing of Lactobacillus plantarum with Acetobacter species altered its tolerance to the transcriptional inhibitor rifampin. By measuring key metabolites and environmental pH, we determined that Acetobacter species counter the acidification driven by L. plantarum production of lactate. Shifts in pH were sufficient to modulate L. plantarum tolerance to rifampin and the translational inhibitor erythromycin. A reduction in lag time exiting stationary phase was linked to L. plantarum tolerance to rifampicin, opposite to a previously identified mode of tolerance to ampicillin in E. coli. This mechanistic understanding of the coupling among interspecies interactions, environmental pH, and antibiotic tolerance enables future predictions of growth and the effects of antibiotics in more complex communities.

Realistic scenarios of missing taxa in phylogenetic comparative methods and their effects on model selection and parameter estimation

Model-based analyses of continuous trait evolution enable rich evolutionary insight. These analyses require a phylogenetic tree and a vector of trait values for the tree's terminal taxa, but rarely do a tree and dataset include all taxa within a clade. Because the probability that a taxon is included in a dataset depends on ecological traits that have phylogenetic signal, missing taxa in real datasets should be expected to be phylogenetically clumped or correlated to the modelled trait. I examined whether those types of missing taxa represent a problem for model selection and parameter estimation. I simulated univariate traits under a suite of Brownian Motion and Ornstein-Uhlenbeck models, and assessed the performance of model selection and parameter estimation under absent, random, clumped or correlated missing taxa. I found that those analyses perform well under almost all scenarios, including situations with very sparsely sampled phylogenies. The only notable biases I detected were in parameter estimation under a very high percentage (90%) of correlated missing taxa. My results offer a degree of reassurance for studies of continuous trait evolution with missing taxa, but the problem of missing taxa in phylogenetic comparative methods still demands much further investigation. The framework I have described here might provide a starting point for future work.

Responses of flavonoid profile and associated gene expression to solar blue and UV radiation in two accessions of Vicia faba L. from contrasting UV environments

Blue light and UV radiation shape a plant's morphology and development, but accession-dependent responses under natural conditions are unclear. Here we tested the hypothesis that two faba bean (Vicia faba L.) accessions adapted to different latitudes and altitudes vary in their responses to solar blue and UV light. We measured growth, physiological traits, phenolic profiles and expression of associated genes in a factorial experiment combining two accessions (Aurora, a Swedish cultivar adapted to high latitude and low altitude; ILB938, from the Andean region of Colombia and Ecuador, adapted to low latitude and high altitude) and four filter treatments created with plastic sheets: 1. transparent as control; 2. attenuated short UV (290-350 nm); 3. attenuated UV (290-400 nm); 4. attenuated blue and UV light. In both accessions, the exclusion of blue and UV light increased plant height and leaf area, and decreased transcript abundance of ELONGATED HYPOCOTYL 5 (HY5) and TYROSINE AMINOTRANSFERASE 3 (TAT3). Blue light and short UV induced the accumulation of epidermal and whole-leaf flavonoids, mainly quercetins, and the responses in the two accessions were through different glycosides. Filter treatments did not affect kaempferol concentration, but there were more tri-glycosides in Aurora and di-glycosides in ILB938. Furthermore, fewer quercetin glycosides were identified in ILB938. The transcript abundance was consistently higher in Aurora than in ILB938 for all seven investigated genes: HY5, TAT3, CHALCONE SYNTHASE (CHS), CHALCONE ISOMERASE (CHI), DON-GLUCOSYLTRANSFERASE 1 (DOGT1), ABA INSENSITIVE 2 (ABI2), AUXIN-INDUCIBLE 2-27 (IAA5). The two largest differences in transcript abundance between the two accessions across treatments were 132-fold in CHS and 30-fold in DOGT1 which may explain the accession-dependent glycosylation patterns. Our findings suggest that agronomic selection for adaptation to high altitude may favour phenotypes with particular adaptations to the light environment, including solar UV and blue light.