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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
- The Evolution of Fundamental Neural Circuits for Cognition in Silico
- Creator
- Tehrani-Saleh, Ali
- Date
- 2021
- Collection
- Electronic Theses & Dissertations
- Description
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Despite decades of research on intelligence and fundamental components of cognition, we still know very little about the structure and functionality of nervous systems. Questions in cognition and intelligent behavior are addressed by scientists in the fields of behavioral biology, neuroscience, psychology, and computer science. Yet it is difficult to reverse engineer observed sophisticated intelligent behaviors in animals and even more difficult to understand their underlying mechanisms.In...
Show moreDespite decades of research on intelligence and fundamental components of cognition, we still know very little about the structure and functionality of nervous systems. Questions in cognition and intelligent behavior are addressed by scientists in the fields of behavioral biology, neuroscience, psychology, and computer science. Yet it is difficult to reverse engineer observed sophisticated intelligent behaviors in animals and even more difficult to understand their underlying mechanisms.In this dissertation, I use a recently-developed neuroevolution platform -called Markov brain networks- in which Darwinian selection is used to evolve both structure and functionality of digital brains. I use this platform to study some of the most fundamental cognitive neural circuits: 1) visual motion detection, 2) collision-avoidance based on visual motion cues, 3) sound localization, and 4) time perception. In particular, I investigate both the selective pressures and environmental conditions in the evolution of these cognitive components, as well as the circuitry and computations behind them. This dissertation lays the groundwork for an evolutionary agent-based method to study the neural circuits for cognition in silico.
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- Title
- Replaying Life's Virtual Tape : Examining the Role of History in Experiments with Digital Organisms
- Creator
- Bundy, Jason Nyerere
- Date
- 2021
- Collection
- Electronic Theses & Dissertations
- Description
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Evolution is a complex process with a simple recipe. Evolutionary change involves three essential “ingredients” interacting over many generations: adaptation (selection), chance (random variation), and history (inheritance). In 1989’s Wonderful Life, the late paleontologist Stephen Jay Gould advocated for the importance of historical contingency—the way unique events throughout history influence future possibilities—using a clever thought experiment of “replaying life’s tape”. But not...
Show moreEvolution is a complex process with a simple recipe. Evolutionary change involves three essential “ingredients” interacting over many generations: adaptation (selection), chance (random variation), and history (inheritance). In 1989’s Wonderful Life, the late paleontologist Stephen Jay Gould advocated for the importance of historical contingency—the way unique events throughout history influence future possibilities—using a clever thought experiment of “replaying life’s tape”. But not everyone was convinced. Some believed that chance was the primary driver of evolutionary change, while others insisted that natural selection was the most powerful influence. Since then, “replaying life’s tape” has become a core method in experimental evolution for measuring the relative contributions of adaptation, chance, and history. In this dissertation, I focus on the effects associated with history in evolving populations of digital organisms—computer programs that self-replicate, mutate, compete, and evolve in virtual environments. In Chapter 1, I discuss the philosophical significance of Gould’s thought experiment and its influence on experimental methods. I argue that his thought experiment was a challenge to anthropocentric reasoning about natural history that is still popular, particularly outside of the scientific community. In this regard, it was his way of advocating for a “radical” view of evolution. In Chapter 2—Richard Lenski, Charles Ofria, and I describe a two-phase, virtual, “long-term” evolution experiment with digital organisms using the Avida software. In Phase I, we evolved 10 replicate populations, in parallel, from a single genotype for around 65,000 generations. This part of the experiment is similar to the design of Lenski’s E. coli Long-term Evolution Experiment (LTEE). We isolated the dominant genotype from each population around 3,000 generations (shallow history) into Phase I and then again at the end of Phase I (deep history). In Phase II, we evolved 10 populations from each of the genotypes we isolated from Phase I in two new environments, one similar and one dissimilar to the old environment used for Phase I. Following Phase II, we estimated the contributions of adaptation, chance, and history to the evolution of fitness and genome length in each new environment. This unique experimental design allowed us to see how the contributions of adaptation, chance, and history changed as we extended the depth of history from Phase I. We were also able to determine whether the results depended on the extent of environmental change (similar or dissimilar new environment). In Chapter 3, we report an extended analysis of the experiment from the previous chapter to further examine how extensive adaptation to the Phase I environment shaped the evolution of replicates during Phase II. We show how the form of pleiotropy (antagonistic or synergistic) between the old (Phase I) and new (Phase II) habitats was influenced by the depth of history from Phase I (shallow or deep) and the extent of environmental change (similar or dissimilar new environment). In the final chapter Zachary Blount, Richard Lenski, and I describe an exercise we developed using the educational version of Avida (Avida-ED). The exercise features a two-phase, “replaying life’s tape” activity. Students are able to explore how the unique history of founders that we pre-evolved during Phase I influences the acquisition of new functions by descendent populations during Phase II, which the students perform during the activity.
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- Title
- EMERGENT COORDINATION : ADAPTATION, OPEN-ENDEDNESS, AND COLLECTIVE INTELLIGENCE
- Creator
- Bao, Honglin
- Date
- 2022
- Collection
- Electronic Theses & Dissertations
- Description
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Agent-based modeling is a widely used computational method for studying the micro-macro bridge issue by simulating the microscopic interactions and observing the macroscopic emergence. This thesis begins with the fundamental methodology of agent-based models: how agents are represented, how agents interact, and how the agent population is structured. Two vital topics, the evolution of cooperation and opinion dynamics are used to illustrate methodological innovation. For the first topic, we...
Show moreAgent-based modeling is a widely used computational method for studying the micro-macro bridge issue by simulating the microscopic interactions and observing the macroscopic emergence. This thesis begins with the fundamental methodology of agent-based models: how agents are represented, how agents interact, and how the agent population is structured. Two vital topics, the evolution of cooperation and opinion dynamics are used to illustrate methodological innovation. For the first topic, we study the equilibrium selection in a coordination game in multi-agent systems. In particular, we focus on the characteristics of agents (supervisors and subordinates versus representative agents), the interactions of agents (reinforcement learning in the games with fixed versus adaptive learning rates according to the supervision and time-varying versus supervision-guided exploration rates), the network of agents (single-layer versus multi-layer networks), and their impact on the emergent behaviors. Regarding the second topic, we examine how opinions evolve and spread in a cognitively heterogeneous agent population with sparse interactions and how the opinion dynamics co-evolve with the open-ended society's structural change. We then discuss the rich insights into collective intelligence in the two proposed models viewed from the interaction-based adaptation and open-ended network structure. We finally link collective emergent intelligence to diverse applications in the realm of computing and other scientific fields in a cross-multidisciplinary manner.
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- Title
- EXTRACTING STRUCTURE AND FUNCTION FROM COMPLEX SYSTEMS USING INFORMATION-THEORETIC TOOLS
- Creator
- C G, Nitash
- Date
- 2022
- Collection
- Electronic Theses & Dissertations
- Description
-
One of the primary areas of scientific research is understanding how complex systemswork, both structurally and functionally. In the natural world, complex systems are very high dimensional, with many interacting parts, making studying them difficult and in some cases nearly impossible. Due to the complexity of these systems, a lot of modern research focuses on studying these systems from a computational viewpoint. While this necessarily abstracts away from the true system, we attempt to...
Show moreOne of the primary areas of scientific research is understanding how complex systemswork, both structurally and functionally. In the natural world, complex systems are very high dimensional, with many interacting parts, making studying them difficult and in some cases nearly impossible. Due to the complexity of these systems, a lot of modern research focuses on studying these systems from a computational viewpoint. While this necessarily abstracts away from the true system, we attempt to represent the salient aspects of the system in order to better understand it. Results from such computational studies can yield insight into the natural system, and actively constrain the research space by suggesting hypotheses that can be tested.In this work, I investigate the structure and function of two seemingly disparate complexdigital systems. I begin with an investigation of the structure and function of an evolved cognitive architecture, and look at how this structure is affected by environmental changes by developing some new metrics to classify cognitive systems. I then look at the structure of the primordial fitness landscape in a different digital system, and use techniques inspired by information theory to understand the structure of this landscape. I first look at the role of historical contingency in the evolution of life by studying how the structure of this fitness landscape affects the evolutionary trajectories of life. I then investigate how information is encoded in the primordial fitness landscape. I then extend this analysis by developing a general approach for calculating the information content of individual sequences, and use them to analyze the primordial landscape. Finally, I validate this information-theoretic technique by predicting the effects of mutations on the function of a specific protein, and show that this technique can outperform the current state of the art approaches.
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