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- Title
- Evolution and evolvability in changing environments
- Creator
- Canino-Koning, Rosangela
- Date
- 2017
- Collection
- Electronic Theses & Dissertations
- Description
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"The specific meaning of the term 'evolvability' is heavily debated, but most definitions can be summarized as: the potential of populations and genomes to produce adaptive variation and complex structures in response to mutation and selection. Changing environments are thought to play a significant role in shaping and promoting evolvability through alternating selective pressures. In this dissertation, I will discuss my recent research on the interplay between changing environments,...
Show more"The specific meaning of the term 'evolvability' is heavily debated, but most definitions can be summarized as: the potential of populations and genomes to produce adaptive variation and complex structures in response to mutation and selection. Changing environments are thought to play a significant role in shaping and promoting evolvability through alternating selective pressures. In this dissertation, I will discuss my recent research on the interplay between changing environments, evolvability, genetic architecture, and the evolution of horizontal gene transfer (HGT), an information-rich mutagenic function that is ubiquitous in nature. Before delving into my own research, however, I begin in the first chapter by providing a survey of current literature on each of these topics, with emphases on how they are believed to arise, how they affect subsequent evolution, and how they relate to each other. Genetic architecture and population dynamics clearly have a complex interplay in ongoing evolutionary dynamics. Evolutionary history, population diversity, modularity, and task size all play a role in determining the location and characteristics of populations in genotype space, and alter the genotype to phenotype map that permits neutral genetic variation. All of these features contribute to evolvability. In Chapter 2, I demonstrate how changing environments provided a sufficient selective pressure to produce quasi-modular genetic architectures that allow for rapid adaptation to the meta-environment of environmental change. Horizontal gene transfer is a highly regulated, ubiquitous, and ancient mechanism for exchanging genetic material between unrelated organisms. In the third chapter, I explore conditions which may have led to the evolution of horizontal gene transfer through transformation, and identify mechanisms that might support its continued performance. In Chapter 4, I compare the fitness and phenotypic effects of the HGT process against other types of increasingly less information rich mutational operators. I demonstrate that not only is HGT selected for in harsh changing environments, but that other mutagenic instructions that contain less information, or provide lesser fitness benefits are not similarly selected for. In the fifth chapter, I explore the long-term evolutionary potential of populations evolved in changing environments by evolving two different populations, one evolved in a minimal changing environment, and the other in a rich changing environment, and exposing them to a brand new environment. I demonstrate that while populations adapted to harsh changing environments are indeed able to adapt quickly to previously seen environmental changes, that these populations do not fare as well in brand new environments. Rather, benign changing environments perform best in measures of task discovery and exploration. In the final chapter, I conclude with a synthesis of my results, along with implications for the field, as well as identification of some new directions for pursuing my research into changing environments."--Pages ii-iii.
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- Title
- Balancing convergence and diversity in evolutionary single, multi and many objectives
- Creator
- Seada, Haitham
- Date
- 2017
- Collection
- Electronic Theses & Dissertations
- Description
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"Single objective optimization targets only one solution, that is usually the global optimum. On the other hand, the goal of multiobjective optimization is to represent the whole set of trade-off Pareto-optimal solutions to a problem. For over thirty years, researchers have been developing Evolutionary Multiobjective Optimization (EMO) algorithms for solving multiobjective optimization problems. Unfortunately, each of these algorithms were found to work well on a specific range of objective...
Show more"Single objective optimization targets only one solution, that is usually the global optimum. On the other hand, the goal of multiobjective optimization is to represent the whole set of trade-off Pareto-optimal solutions to a problem. For over thirty years, researchers have been developing Evolutionary Multiobjective Optimization (EMO) algorithms for solving multiobjective optimization problems. Unfortunately, each of these algorithms were found to work well on a specific range of objective dimensionality, i.e. number of objectives. Most researchers overlooked the idea of creating a cross-dimensional algorithm that can adapt its operation from one level of objective dimensionality to the other. One important aspect of creating such algorithm is achieving a careful balance between convergence and diversity. Researchers proposed several techniques aiming at dividing computational resources uniformly between these two goals. However, in many situations, only either of them is difficult to attain. Also for a new problem, it is difficult to tell beforehand if it will be challenging in terms of convergence, diversity or both. In this study, we propose several extensions to a state-of-the-art evolutionary many-objective optimization algorithm - NSGA-III. Our extensions collectively aim at (i) creating a unified optimization algorithm that dynamically adapts itself to single, multi- and many objectives, and (ii) enabling this algorithm to automatically focus on either convergence, diversity or both, according to the problem being considered. Our approach augments the already existing algorithm with a niching-based selection operator. It also utilizes the recently proposed Karush Kuhn Tucker Proximity Measure to identify ill-converged solutions, and finally, uses several combinations of point-to-point single objective local search procedures to remedy these solutions and enhance both convergence and diversity. Our extensions are shown to produce better results than state-of-the-art algorithms over a set of single, multi- and many-objective problems."--Pages ii-iii.
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- Title
- Digital Evolution in Experimental Phylogenetics and Evolution Education
- Creator
- Kohn, Cory
- Date
- 2021
- Collection
- Electronic Theses & Dissertations
- Description
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The creation and evaluation of known evolutionary histories and the implementation of student investigatory experiences on evolution are difficult endeavors that have only recently been feasible. The research presented in this dissertation is related in their shared use of digital evolution with Avidians as a model study system, both to conduct science research in experimental phylogenetics and to conduct education research in curricular intervention to aid student understanding.I first...
Show moreThe creation and evaluation of known evolutionary histories and the implementation of student investigatory experiences on evolution are difficult endeavors that have only recently been feasible. The research presented in this dissertation is related in their shared use of digital evolution with Avidians as a model study system, both to conduct science research in experimental phylogenetics and to conduct education research in curricular intervention to aid student understanding.I first present background discussions on the Avidian digital evolution study system—as implemented in Avida and Avida-ED—and its favorable use in experimental phylogenetics and biology education owing to its greater biological realism than computational simulations, and greater utility and generality than biological systems. Prior work on conducting experimental evolution for use in phylogenetics and work on developing undergraduate lab curricula using experimental evolution are also reviewed. I establish digital evolution as an effective method for phylogenetic inference validation by demonstrating that results from a known Avidian evolutionary history are concordant, under similar conditions, to established biological experimental phylogenetics work. I then further demonstrate the greater utility and generality of digital evolution over biological systems by experimentally testing how phylogenetic accuracy may be reduced by complex evolutionary processes operating singly or in combination, including absolute and relative degrees of evolutionary change between lineages (i.e., inferred branch lengths), recombination, and natural selection. These results include that directional selection aids phylogenetic inference, while stabilizing selection impedes it. By evaluating clade accuracy and clade resolvability across treatments, I evaluate measures of tree support and its presentation in the form of consensus topologies and I offer several general recommendations for systematists. Using a larger and more biologically realistic experimental design, I systematically examine a few of the complex processes that are hypothesized to affect phylogenetic accuracy—natural selection, recombination, and deviations from the model of evolution. By analyzing the substitutions that occurred and calculating selection coefficients for derived alleles throughout their evolutionary trajectories to fixation, I show that molecular evolution in these experiments is complex and proceeding largely as would be expected for biological populations. Using these data to construct empirical substitution models, I demonstrate that phylogenetic inference is incredibly robust to significant molecular evolution model deviations. I show that neutral evolution in the presence of always-occurring population processes, such as clonal or Hill-Robertson interference and lineage sorting, result in reduced clade support, and that selection and especially recombination, including their joint occurrence, restore this otherwise-reduced phylogenetic accuracy. Finally, this work demonstrates that inferred branch lengths are often quite inaccurate despite clade support being accurate. While phylogenetic inference methods performed relatively well in both theoretically facile and challenging molecular evolution scenarios, their accuracy in clade support might be a remarkable case of being right for misguided reasons, since branch length inference were largely inaccurate, and drastically different models of evolution made little difference. This work highlights the need for further research that evaluates phylogenetic methods under experimental conditions and suggests that digital evolution has a role here. Finally, I examine student understanding of the importance of biological variation in the context of a course featuring a digital evolution lab. I first describe the Avida-ED lab curriculum and its fulfillment of calls for reform in education. Then I describe the specific education context and other course features that aim to address student conceptualization of variation. I present a modified published assessment on transformational and variational understanding and findings regarding student understanding of variation within an evolution education progression. Finally, I offer suggestions on incorporating course material to engage student understanding of variation.
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- Title
- Contextual influences on undergraduate biology students' reasoning and representations of evolutionary concepts
- Creator
- de Lima, Joelyn
- Date
- 2021
- Collection
- Electronic Theses & Dissertations
- Description
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Context is the background or the settings of an event or idea. It is only when events or ideas are considered within the context in which they occur that they can be fully understood. In education, the application of knowledge communicated in one context to a different one is a central feature of learning. However, knowledge transfer can be affected by multiple factors including contexts used. Context plays a vital role in both shaping students’ learning and in eliciting their knowledge....
Show moreContext is the background or the settings of an event or idea. It is only when events or ideas are considered within the context in which they occur that they can be fully understood. In education, the application of knowledge communicated in one context to a different one is a central feature of learning. However, knowledge transfer can be affected by multiple factors including contexts used. Context plays a vital role in both shaping students’ learning and in eliciting their knowledge. Therefore, understanding how context can help or hinder learning and how context impacts knowledge assessment is important for improving science learning outcomes.For my dissertation, I studied contextual influences on the ways students reason and represent their knowledge. My studies explored two types of contexts: surface features of prompts provided to students (e.g., organism used) and the mode of response requested (e.g., written narratives vs constructed models). I analysed the effect of prompt surface features on the content of students’ written responses and on the architecture of models they constructed to explain evolution by natural selection. I also analysed the effect of mode on the content and level of scientific plausibility of students’ responses. In addition, I explored the association between instruction and prior achievement and susceptibility to contextual influences.My results indicate that prompt contextual features and mode of response are eliciting differences in the content of students’ representations. Contextual susceptibility decreased with instruction and higher prior academic achievement. This could indicate that they are novice learners and have a fragile understanding of either the subject matter (evolution), the alternative representation that was required (constructing models), or of both the subject matter and the representation. Incorporating multiple contexts and modes of assessment has potential to generate a more holistic view of students’ understanding and may promote greater transfer by requiring students to think and reason across contexts.
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- Title
- MODELING AND PREDICTION OF GENETIC REDUNDANCY IN ARABIDOPSIS THALIANA AND SACCHAROMYCES CEREVISIAE
- Creator
- Cusack, Siobhan Anne
- Date
- 2020
- Collection
- Electronic Theses & Dissertations
- Description
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Genetic redundancy is a phenomenon where more than one gene encodes products that perform the same function. This frequently manifests experimentally as a single gene knockout mutant which does not demonstrate a phenotypic change compared to the wild type due to the presence of a paralogous gene performing the same function; a phenotype is only observed when one or more paralogs are knocked out in combination. This presents a challenge in a fundamental goal of genetics, linking genotypes to...
Show moreGenetic redundancy is a phenomenon where more than one gene encodes products that perform the same function. This frequently manifests experimentally as a single gene knockout mutant which does not demonstrate a phenotypic change compared to the wild type due to the presence of a paralogous gene performing the same function; a phenotype is only observed when one or more paralogs are knocked out in combination. This presents a challenge in a fundamental goal of genetics, linking genotypes to phenotypes, especially because it is difficult to determine a priori which gene pairs are redundant. Furthermore, while some factors that are associated with redundant genes have been identified, little is known about factors contributing to long-term maintenance of genetic redundancy. Here, we applied a machine learning approach to predict redundancy among benchmark redundant and nonredundant gene pairs in the model plant Arabidopsis thaliana. Predictions were validated using well-characterized redundant and nonredundant gene pairs. Additionally, we leveraged the availability of fitness and multi-omics data in the budding yeast Saccharomyces cerevisiae to build machine learning models for predicting genetic redundancy and related phenotypic outcomes (single and double mutant fitness) among paralogs, and to identify features important in generating these predictions. Collectively, our models of genetic redundancy provide quantitative assessments of how well existing data allow predictions of fitness and genetic redundancy, shed light on characteristics that may contribute to long-term maintenance of paralogs that are seemingly functionally redundant, and will ultimately allow for more targeted generation of phenotypically informative mutants, advancing functional genomic studies.
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- Title
- Social Modulation of Individual Decision-Making in Foraging Bumblebees : Mechanisms and Evolution
- Creator
- Incorvaia, Darren
- Date
- 2021
- Collection
- Electronic Theses & Dissertations
- Description
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How and why animals choose to do what they do at any given moment is one of the fundamental questions in animal behavior. For social animals, influences on decision-making can come from both personal and social sources, and in eusocial insects like ants, bees, and wasps, the reliance on social information is taken to the extreme. Foraging bumblebees offer the perfect model in which to examine the social influences on individual decision-making because they are presented with extensive...
Show moreHow and why animals choose to do what they do at any given moment is one of the fundamental questions in animal behavior. For social animals, influences on decision-making can come from both personal and social sources, and in eusocial insects like ants, bees, and wasps, the reliance on social information is taken to the extreme. Foraging bumblebees offer the perfect model in which to examine the social influences on individual decision-making because they are presented with extensive personal and social information, and when foraging they are solely focused on the task at hand. Chapter 1 reviews information use by foraging bumblebees, setting the stage for the subsequent data chapters. Chapter 2 examines how the motivation for bumblebees to feed from a known feeder is modified by the nutritive state of the colony, such that individuals in colonies with full food stores show lower motivation to feed. In addition to this behavioral result, a biochemical analysis reveals that lipid levels may be involved in the mechanism underlying this social effect. Eusocial insects are famous for collective behaviors, such as the swarming behavior of honeybees, the foraging trails of termites, and the bridge-building of ants. While the collective foraging strategy of other eusocial insects has been well-studied, it has not received attention in bumblebees. In Chapter 3 I use a behavioral experiment to reveal that bumblebees use a strategy of informed individual initiative to collectively ensure they are foraging from the best resources in the environment. In this strategy, individual bees adjust their reward expectations based on the quality of nectar stored in the nest. I followed up this experiment with a computational model to reveal that this strategy is adaptive, as it results in higher fitness than does individual search alone. This strategy is markedly different from the spatial communication of the dance language used by honeybees, who are close relatives of bumblebees. This prompted me to extend the computational model to examine the selective pressures that shape foraging strategies in social insects, including the honeybee dance language and bumblebee strategy of informed individual initiative. In Chapter 4, I present the results of simulations of this extended model, demonstrating that, although resource density influences fitness for both the dance language and informed individual initiative, colony size only matters for the dance language. This suggests that the large colony sizes of honeybees may have been important for the dance language to evolve, whereas a similar spatial communication system would not be adaptive in bumblebees, which have smaller colony sizes. Taken all together, the results in this dissertation explore how individual decision-making is shaped by the social environment in bumblebees, and the potential selective pressures that led to these behavioral strategies over evolutionary time. Bumblebees are important pollinators in both agricultural and natural ecosystems, but many species are facing declines; a more thorough understanding of their behavior is imperative to help us conserve them as the planet continues to change due to climate change and other anthropogenic influences.
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