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- Title
- Deconstructing the correlated nature of ancient and emergent traits : an evolutionary investigation of metabolism, morphology, and mortality
- Creator
- Grant, Nkrumah Alions
- Date
- 2020
- Collection
- Electronic Theses & Dissertations
- Description
-
Phenotypic correlations are products of genetic and environmental interactions, yet the nature of these correlations is obscured by the multitude of genes organisms possess. My dissertation work focused on using 12 populations of Escherichia coli from Richard Lenski's long-term evolution experiment (LTEE) to understand how genetic correlations facilitate or impede an organism's evolution. In chapter 1, I describe how ancient correlations between aerobic and anaerobic metabolism have...
Show morePhenotypic correlations are products of genetic and environmental interactions, yet the nature of these correlations is obscured by the multitude of genes organisms possess. My dissertation work focused on using 12 populations of Escherichia coli from Richard Lenski's long-term evolution experiment (LTEE) to understand how genetic correlations facilitate or impede an organism's evolution. In chapter 1, I describe how ancient correlations between aerobic and anaerobic metabolism have maintained - and even improved - the capacity of E. coli to grow in an anoxic environment despite 50,000 generations of relaxed selection for anaerobic growth. I present genomic evidence illustrating substantially more mutations have accumulated in anaerobic-specific genes and show parallel evolution at two genetic loci whose protein products regulate the aerobic-to-anaerobic metabolic switch. My findings reject the "if you don't use it, you lose it" notion underpinning relaxed selection and show modules with deep evolutionary roots can overlap more, hence making them harder to break. In chapter 2, I revisit previous work in the LTEE showing that the fitness increases measured for the 12 populations positively correlated with an increase in cell size. This finding was contrary to theory predicting smaller cells should have evolved. Sixty thousand generations have surpassed since that initial study, and new fitness data collected for the 12 populations show fitness has continued to increase over this period. Here, I asked whether cell size also continued to increase. To this end, I measured the size of cells for each of the 12 populations spanning 50,000 generations of evolution using a particle counter, microscopy, and machine learning. I show cell size has continued to increase and that it remains positively correlated with fitness. I also present several other observations including heterogeneity in cell shape and size, parallel mutations in cell-shape determining genes, and elevated cell death in the single LTEE population that evolved a novel metabolism - namely the ability to grow aerobically on citrate. This last observation formed the basis of my chapter 3 research where my collaborators and I fully examine the cell death finding and the associated genotypic and phenotypic consequences of the citrate metabolic innovation.
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- Title
- Economic gain-aware routing protocols for device-to-device content dissemination
- Creator
- Hajiaghajani Memar, Faezeh
- Date
- 2017
- Collection
- Electronic Theses & Dissertations
- Description
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"The objective of this dissertation is to investigate Device-to-Device content dissemination protocols for maximizing the economic gain of dissemination for given combinations of commercial and network parameters. " -- Abstract.
- Title
- The evolution of neural plasticity in digital organisms
- Creator
- Sheneman, Leigh
- Date
- 2017
- Collection
- Electronic Theses & Dissertations
- Description
-
Learning is a phenomenon that organisms throughout nature demonstrate and that machinelearning aims to replicate. In nature, it is neural plasticity that allows an organismto integrate the outcomes of their past experiences into their selection of future actions.While neurobiology has identified some of the mechanisms used in this integration, how theprocess works is still a relatively unclear and highly researched topic in the cognitive sciencefield. Meanwhile in the field of machine...
Show moreLearning is a phenomenon that organisms throughout nature demonstrate and that machinelearning aims to replicate. In nature, it is neural plasticity that allows an organismto integrate the outcomes of their past experiences into their selection of future actions.While neurobiology has identified some of the mechanisms used in this integration, how theprocess works is still a relatively unclear and highly researched topic in the cognitive sciencefield. Meanwhile in the field of machine learning, researchers aim to create algorithms thatare also able to learn from past experiences; this endeavor is complicated by the lack ofunderstanding how this process takes place within natural organisms.In this dissertation, I extend the Markov Brain framework [1, 2] which consists of evolvablenetworks of probabilistic and deterministic logic gates to include a novel gate type{feedback gates. Feedback gates use internally generated feedback to learn how to navigatea complex task by learning in the same manner a natural organism would. The evolutionarypath the Markov Brains take to develop this ability provides insight into the evolutionof learning. I show that the feedback gates allow Markov Brains to evolve the ability tolearn how to navigate environments by relying solely on their experiences. In fact, the probabilisticlogic tables of these gates adapt to the point where the an input almost alwaysresults in a single output, to the point of almost being deterministic. Further, I show thatthe mechanism the gates use to adapt their probability table is robust enough to allow theagents to successfully complete the task in novel environments. This ability to generalizeto the environment means that the Markov Brains with feedback gates that emerge fromevolution are learning autonomously; that is without external feedback. In the context ofmachine learning, this allows algorithms to be trained based solely on how they interact withthe environment. Once a Markov Brain can generalize, it is able adapt to changing sets of stimuli, i.e. reversal learn. Machines that are able to reversal learn are no longer limited tosolving a single task. Lastly, I show that the neuro-correlate is increased through neuralplasticity using Markov Brains augmented with feedback gates. The measurement of isbased on Information Integration Theory[3, 4] and quanties the agent's ability to integrateinformation.
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- Title
- Evolution of decision-making systems
- Creator
- Schossau, Jorden D.
- Date
- 2017
- Collection
- Electronic Theses & Dissertations
- Description
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Adaptive biological or engineered systems are adaptive because they can make decisions. Some systems such as viruses use their molecular composition – genetic information – to decide when to become lysogenic (dormant) or lytic (active). Others, such as self-driving cars, must use spatiotemporal information about obstacles, speed, and previous signs to determine when to turn or begin braking. Computational models of systems allow us to both engineer better systems, and create better scientific...
Show moreAdaptive biological or engineered systems are adaptive because they can make decisions. Some systems such as viruses use their molecular composition – genetic information – to decide when to become lysogenic (dormant) or lytic (active). Others, such as self-driving cars, must use spatiotemporal information about obstacles, speed, and previous signs to determine when to turn or begin braking. Computational models of systems allow us to both engineer better systems, and create better scientific understanding about the dynamic world. The practice of modeling decision-making started with the study of interactions between rational agents on the spectrum of conflict and cooperation began with Von Neumann and Morgenstern's Theory of Game and Economic Behavior.Scenarios, called "games", are models designed and studied to increase understanding of conflict and cooperation between these agents. The games discussed here are Prisoner's Dilemma and Volunteer's Dilemma. Modern methods of analysis for games involving populations of interacting agents fail to predict the final strategy distribution among all agents. In chapter 2 I develop a new computational agent-based simulation used as an inductive study system to compare the deductive predictive capabilities of an analytical model that is capable of predicting the final distribution under idealized conditions. Lastly, I show a novel finding that the agent-based model suggests probabilistic, or mixed, strategies (such as probabilistic gene expression) are a result of the development and maintenance of cooperation in Volunteer's Dilemma.Game theory fails to provide tractable models for more complex decision-making situations, such as those with complex spatial or temporal dimensions. In these cases an algorithm of conditional logic may be used to simulate decision-making behavior. Yet still there are systems for which the use of an algorithm as a model is inadequate due to incomplete knowledge of the system. Perhaps the model makes too many generalizations, is limited by atomic discretization, or is otherwise incomplete. In these cases it is useful to compensate for deficits by using probabilistic logic. That is, we assume that a stochastic process can roughly describe those subprocesses not fully modeled.Lastly, algorithms as decision strategies can incorporate temporal information in the decision-making process. There are two ways temporal information can be used in an individual's conditional logic: evolutionary, and lifetime. The evolutionary approach has proved much more flexible as a means to discover and tune models of unknown decision-making processes. Neuroevolution is a machine learning method that uses evolutionary algorithms to train artificial neural networks as models of decision-making systems. There is currently a wide diversity of methods for neuroevolution that all share common structures of the types of problems being solved: those generally being cognitive tasks. Toward this end it would be useful if there were some properties common to all cognitive systems that could be incorporated into the optimizing objective function in order to enhance or simplify the evolutionary process. In chapter 3 and 4 I explore new methods of improving model discovery through neuroevolution and discuss the applicability of these methods for probabilistic models.
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- Title
- The evolution of complexity and robustness in small populations
- Creator
- LaBar, Thomas
- Date
- 2018
- Collection
- Electronic Theses & Dissertations
- Description
-
"A central goal of evolutionary biology is to understand a population's evolutionary trajectory from fundamental population-level characteristics. The mathematical framework of population genetics provides the tools to make these predictions. And while population genetics provides a well-studied framework to understand how adaptation and neutral evolution quantitatively alter population fitness, less attention has been paid to using population genetics to predict qualitative evolutionary...
Show more"A central goal of evolutionary biology is to understand a population's evolutionary trajectory from fundamental population-level characteristics. The mathematical framework of population genetics provides the tools to make these predictions. And while population genetics provides a well-studied framework to understand how adaptation and neutral evolution quantitatively alter population fitness, less attention has been paid to using population genetics to predict qualitative evolutionary outcomes. For instance, do different populations evolve alternative genetic mechanisms to encode similar phenotypic traits, and if so, which processes lead to these differences? This dissertation investigates the role of population size in altering the qualitative outcome of evolution. It is difficult to experimentally investigate qualitative evolutionary outcomes, especially in small populations, due to the time required for novel evolutionary features to appear. To get around this constraint, I use digital experimental evolution. While digital evolution experiments lack aspects of biological realism, in some regards they are the only methodology that can approach the complexity of biological systems while maintaining the ease of analysis present in mathematical models. Digital evolution experiments can never prove that certain evolutionary trajectories occur in biological populations, but they can suggest hypotheses to test in more realistic model systems. First, I explore the role of population size in determining the evolution of both genomic and phenotypic complexity. Previous hypotheses have argued that small population size may lead to increases in complexity and I test aspects of those hypotheses here. Second, I introduce the novel concept of 'drift robustness' and argue that drift robustness is a strong factor in the evolution of small populations. Finally, I end with a project on the role of genome size in enhancing the extinction risk of small populations. I conclude with a broader discussion of the consequences of this research, some limitations of the results, and some ideas for future research."--Page ii.
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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
-
"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
-
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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