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Title
The evolution of a key innovation in an experimental population of Escherichia coli : a tale of opportunity, contingency, and co-option
Creator
Blount, Zachary David
Date
2011
Collection
Electronic Theses & Dissertations
Description
The importance of historical contingency in evolution has been extensively debated over the last few decades, but direct empirical tests have been rare. Twelve initially identical populations of E. coli were founded in 1988 to investigate this issue. They have since evolved for more than 50,000 generations in a glucose-limited medium that also contains a citrate. However, the inability to use citrate as a carbon source under oxic conditions is a species-defining trait of ...
Show moreThe importance of historical contingency in evolution has been extensively debated over the last few decades, but direct empirical tests have been rare. Twelve initially identical populations of E. coli were founded in 1988 to investigate this issue. They have since evolved for more than 50,000 generations in a glucose-limited medium that also contains a citrate. However, the inability to use citrate as a carbon source under oxic conditions is a species-defining trait of E. coli. A weakly Cit+ variant capable of aerobic citrate utilization finally evolved in one population just prior to 31,500 generations. Shortly after 33,000 generations, the population experienced a several-fold expansion as strongly Cit+ variants rose to numerical dominance (but not fixation). The Cit+ trait was therefore a key innovation that increased both population size and diversity by opening a previously unexploited ecological opportunity.The long-delayed and unique evolution of the Cit+ innovation might be explained by two possible hypotheses. First, evolution of the Cit+ function may have required an extremely rare mutation. Alternately, the evolution of Cit+ may have been contingent upon one or more earlier mutations that had accrued over the population's history. I tested these hypotheses in a series of experiments in which I "replayed" evolution from different points in the population's history. I observed no Cit+ mutants among 8.4 x 1012 ancestral cells, nor among 9 x 1012 cells from 60 clones sampled in the first 15,000 generations. However, I observed a significantly greater tendency to evolve Cit+ among later clones. These results indicate that one or more earlier mutations potentiated the evolution of Cit+ by increasing the rate of mutation to Cit+ to an accessible, though still very low, level. The evolution of the Cit+ function was therefore contingent on the particular history of the population in which it occurred.I investigated the Cit+ innovation's history and genetic basis by sequencing the genomes of 29 clones isolated from the population at various time points. Analysis of these genomes revealed that at least 3 distinct clades coexisted for more than 10,000 generations prior to the innovation's evolution. The Cit+ trait originated in one clade by a tandem duplication that produced a new regulatory module in which a silent citrate transporter was placed under the control of an aerobically-expressed promoter. Subsequent increases in the copy number of this new regulatory module refined the initially weak Cit+ phenotype, leading to the population expansion. The 3 clades varied in their propensity to evolve the novel Cit+ function, though genotypes able to do so existed in all 3, implying that potentiation involved multiple mutations.My findings demonstrate that historical contingency can significantly impact evolution, even under the strictest of conditions. Moreover, they suggest that contingency plays an especially important role in the evolution of novel innovations that, like Cit+, require prior construction of a potentiating genetic background, and are thus not easily evolved by gradual, cumulative selection. Contingency may therefore have profoundly shaped life's evolution given the importance of evolutionary novelties in the history of life. Finally, the genetic basis of the Cit+ function illustrates the importance of promoter capture and altered gene regulation in mediation the exaptation events that often underlie evolutionary innovations.
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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
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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