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School of Biological Sciences

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https://hdl.handle.net/1853/70750

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College of Sciences

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Center for the Study of Systems Biology

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https://finding-aids.library.gatech.edu/agents/corporate_entities/1131

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Publication Search Results

Now showing 1 - 10 of 736

Using Polymorphic Microsatellites to Determine the Population Genetics of Vespula maculifrons

(Georgia Institute of Technology, 2019-12) Thompson, Veronica

Social insects have an interesting genetic history and are studied in order to discern how their social behaviors affects their genetic makeup. The eastern yellowjacket Vespula maculifrons is one such species whose altruistic behaviors and caste system should negatively affect their genetic diversity but instead has fluorished for many years as a dominant species in their ecosystem. We investigated whether V. maculifrons follows the pattern of other social insects in having a small genetic diversity and therefore, a small effective population size. We sequenced seventeen polymorphic microsatellites of V. maculifrons of three different years that were chosen in accordance to the temporal method. We performed a Fixation Index test on the data with the three years as subpopulations in order to determine the differences in allele frequency amongst the groups over time. This was done in order to support our theory that V. maculifrons has a low amount of genetic diversity, which correlates to low amounts of allele fixation, and therefore a low effective population size. We found that the fixation index was significantly low, which supported this idea that not many alleles have gone to fixation. This would indicate that the effective population size is low because the population is still affected by genetic drift. In the future, a concrete calculation of the effective population size will be performed with combinations of multiple equations that can account for the many unique social traits of Vespula maculifrons. This will then help in order to add more information to the gap of knowledge on the fascinating genetic makeup of these unique social organisms.
Testing how methods impact the results of interspecific competition research

(Georgia Institute of Technology, 2019-12) Woo, Esther

There are currently two methods that can be used to measure interspecific competition, pairwise and mutual invasion. Both can be used to generate niche difference (ND) and relative fitness difference (RFD) values, as well as determine if two species are able to coexist. Although the newer method, mutual invasion, has been in use for eight years, there has not yet been a study that compares the two. In order to determine if the method impacts the results found in a study, two simple experiments were conducted concurrently. The five-week long experiment involved determining whether Colpidium striatum and Tetrahymena pyriformis are able to coexist. Upon completion, both methods concluded that they could coexist. Despite reaching the same conclusion, it is still unknown if other species pairings or more complex experiments would alter these results.
Development of a Wireless, Commercial Electromyography System for Use in Athletics and Physical Therapy

(Georgia Institute of Technology, 2019-12) Brooksher, Riley

Electromyography is a muscle activity recording technique that is not often used in a clinical setting due to difficulties in reproducibility. In this paper I aim to create a wireless, wearable system for electromyography. This system is built into a pair of compression shorts, and sends both electromyography and positional data from inertial measurement units to users’ mobile devices. This system is primarily useful in physical therapy and athletic fields, as quantitative information on user gait can improve in the healing and training processes.
Ecological Community Assembly in the Face of Anthropogenic Environmental Changes

(Georgia Institute of Technology, 2019-11-11) Yang, Xian

Anthropogenic environmental changes, such as increased nitrogen (N) deposition, changes in precipitation regimes, and habitat loss and fragmentation, are known to affect Earth’s ecosystems. Understanding mechanisms regulating the assembly of ecological communities in the face of anthropogenic environmental changes is one of the primary goals of contemporary ecology. In this dissertation, I present four studies addressing questions on community assembly under anthropogenic environmental changes. First, I conducted an experiment in a semi-arid grassland to examine how anthropogenic environmental changes, in the form of resource addition, influence phylogenetic alpha- and beta-diversity of the communities. I found N and water addition influenced different aspects of grassland community structure. N addition altered plant community phylogenetic structure, driving communities towards phylogenetic overdispersion; water addition promoted phylogenetic convergence, driving communities to converge towards a more similar phylogenetic structure over time. Next, I used bacterivorous ciliated protists as model organisms to explore how the loss of a keystone local community affects metacommunity biodiversity and ecosystem functions. I found that local communities with distinct environmental conditions supported endemic species, and had greater impact on regional-scale diversity than other local communities, therefore qualifying them as keystone communities. These keystone communities also had significant impacts on ecosystem functions, including biomass production and particulate organic matter decomposition. Finally, I investigated the drivers of variation in the phyllosphere microbial community composition in a fragmented subtropical forest on the islands of the Thousand-Island Lake, China. I found that stochastic processes, rather than deterministic processes, played a prominent role in shaping phyllosphere bacterial and fungal communities in the context of habitat fragmentation. Taken together, these findings further our understanding of community assembly processes in the face of anthropogenic environmental changes.
Human genetic ancestry, health, and adaptation in Latin America

(Georgia Institute of Technology, 2019-11-05) Norris, Emily Taylor

Genetic admixture is the process that occurs when populations that were previously reproductively isolated, and consequently genetically diverged, come back together and exchange genes. Recent studies of modern and ancient genomes have underscored the frequency with which admixture has occurred during human evolution. Indeed, human evolution has been characterized by numerous iterations of physical isolation and genetic divergence followed by population convergence and admixture. Genetic admixture has profound implications for human evolution as it results in the creation of evolutionarily novel genomes that contain combinations of genetic variants (haplotypes) never seen before on the same genomic background. This dissertation explores the implications of large-scale genetic admixture in Latin America for human health, evolution (natural selection), and population structure (assortative mating). Latin America provides an ideal setting to explore the implications of admixture given the formation of modern populations via admixture among distinct African, European, and Native American population groups. Human health and evolution are explored through the lens of admixture, with an emphasis on the demographic processes that serve to combine distinct ancestry components within genomes. Population structure is considered with respect to assortative mating, which serves to limit the extent of genetic admixture within populations, thereby maintaining genetic diversity among distinct population groups even when they are co-located. In order to understand the implications of admixture for the formation of the New World, comparative genomic analyses were used to characterize patterns of genetic ancestry and admixture for individuals from four modern Latin American populations: Colombia, Mexico, Peru, and Puerto Rico. Comparative genomic analyses with ancestral source populations allowed for the characterization of genetic ancestry and admixture profiles for these four Latin American populations at both genome-wide (global) and variant/gene (local) levels. These data on genetic ancestry were integrated with a variety of functional genomic data sources in an effort to more fully understand the biological implications of admixture. Global patterns of ancestry for each population were used to parameterize the expected values of local ancestry, for both specific genetic variants and at the level of individual genes, and comparisons of observed versus expected ancestry levels were used to look for anomalous deviations of local ancestry, i.e. ancestry enrichment. Ancestry-enriched genetic variants were implicated in a number of health-related phenotypes, including immune system and disease response pathways, and a number of these variants were shown to exert their phenotypic effects via ancestry-specific gene regulation. Ancestry enrichment at the gene level was used to provide evidence for rapid adaptation to local environments via admixture-enabled selection, which occurs when admixture introduces novel genetic variants (haplotypes) to newly formed populations at intermediate frequencies. Admixture-enabled selection was observed for the major histocompatibility complex (MHC) locus of the adaptive immune system across multiple Latin American populations, and both the adaptive and innate immune systems were shown to evolve via polygenic admixture-enabled selection. Patterns of gene level ancestry were also used to search for evidence of population structure caused by assortative mating, whereby mate choice is influenced by phenotypic similarity. This analysis allowed us to characterize the genetic basis of phenotypic cues that influence patterns of assortative mating, including a number of anthropometric and neurological traits as well as the MHC locus. Considered together, these results underscore the outsized role that admixture has played in shaping the biology of modern Latin American populations. Global patterns of genetic ancestry and admixture are distinct to each population, and local ancestry can differ widely even for closely related individuals within a population. Local ancestry impacts a wide variety of health-related traits, provides the raw material for rapid, adaptive evolution, and informs the phenotypic cues that are used for mate choice and help to maintain population structure.
Exploring the ecological and evolutionary consequences of clonal and aggregative development during the transition to multicellularity

(Georgia Institute of Technology, 2019-09-03) Pentz, Jennifer T.

Multicellular organisms form groups in one of two basic ways: cells can ‘stay together’ due to incomplete separation following cellular division (clonal development), or cells can ‘come together’ via aggregation (aggregative development). Multicellularity has evolved multiple times via both routes, but all ‘complex multicellularity’ (e.g., plants, animals, fungi) has only evolved in lineages that develop clonally. Evolutionary theory predicts that clonal development may be superior to aggregation because groups formed this way have little among-cell genetic conflict, thereby aligning the fitness interests of lower-level units (cells), increasing the potential for groups to undergo an ‘evolutionary transition in individuality’ (ETI). ETIs are characterized by a hierarchical shift in the level at which heritable variation in fitness is expressed (e.g., from cells to the multicellular group). In this dissertation, I compare clonal and aggregative development in a simple yeast (Saccharomyces cerevisiae) model system. First, I performed a selection experiment using wild-isolated aggregative yeast (termed flocs) with daily selection for rapid sedimentation in liquid medium. Clonally-developing yeast (termed ‘snowflake yeast’) arose and displaced flocs, and invading snowflake yeast showed higher fitness than their floc counterparts. Next, I engineered snowflake and floc yeast from a common unicellular ancestor, so these two strains only differ in their mode of cluster development. In monoculture, floc yeast were superior to snowflake yeast, growing faster and forming larger clusters that settling more rapidly. Yet, in direct competition, snowflake yeast exploit flocs, becoming disproportionately represented within fast-settling groups. Modeling suggests that ‘choosy’ flocs that exclude snowflake yeast would have the highest fitness, but such a strain would not be able to invade from rare. Finally, I performed a long-term evolution experiment to compare the dynamics of multicellular adaptation in floc and snowflake yeast by selecting for increasingly large cluster size, a multicellular trait. Our environment introduces two important life history traits that affect fitness, growth (cell level) and settling (cluster level), and evolved floc and snowflake yeast exhibited fitness gains in these two opposing traits, respectively. Furthermore, snowflake yeast were enriched with mutations that decrease fitness at the single-cell level, but may be beneficial at the cluster-level. Over evolutionary time, this could result in cells becoming interdependent parts of a new multicellular individual. Taken together, these results show that non-clonal cellular binding may be beneficial in environments favoring rapid multicellular group formation, but this paves the way for persistent evolutionary conflict. Conversely, simple clonal multicellular life cycles increase the efficacy of cluster-level adaptation relative to cell-level, which can potentiate an ETI and establish the emergent multicellular cluster as the new level of biological organization. These results highlight the critical role early multicellular life cycles play in driving – or constraining – this major evolutionary transition.
Genetic epidemiology algorithms for tracking drug resistance variants and genomic clustering of plasmodium species

(Georgia Institute of Technology, 2019-09-03) Ravishankar, Shashidhar

The goal of this thesis is to develop algorithms for the analysis of P. falciparum, P. brasilianum, and P. malariae. Malaria is endemic in many parts of the world, including regions of central Africa, South America, and South East Asia. There are five known species that cause malaria in humans: P. falciparum, P. vivax, P. malariae, P. ovale, and P. knowlesi. P. knowlesi is a zoonotic parasite restricted to mostly South East Asia.According to a World Health Organization (WHO) report from 2018, these five species were responsible for nearly 219 million infections, resulting in an estimated 435,000 deaths related to malaria in 2017. In this work, we highlight algorithms that can identify the similarity between Plasmodium species and detect drug-resistant P. falciparum parasites. The two specific aims in this work, describe two novel algorithms for genomic clustering and molecular surveillance from Next-generation Sequencing (NGS) data. First, we describe a consensus-based variant identification framework molecular surveillance of drug resistance in infectious disease. We highlight its utility by identifying mutations associated with drug resistance in malaria isolates. The scalability of the framework is highlighted by analyzing 8351 M. tuberculosis isolates for the genotypic prediction of drug resistance. In the second aim, we describe a k-mer based alignment-free algorithm for the estimation of similarity between isolates from raw NGS data. Using a weighted Jaccard distance, we describe an exact method for estimation of the distance between isolates from k-mer count data. The memory efficiency, scalability, and accuracy of the algorithm was demonstrated using in-silico datasets generated from genomes of 12 Plasmodium species, as well as real-world isolates from an outbreak of C. auris in Colombia. The improved accuracy and scalability offered by the methods described in this work can facilitate the use of NGS in public health.
Global dysregulation of gene expression and tumorigenesis: Data science for cancer

(Georgia Institute of Technology, 2019-09-03) Clayton, Evan

Dysregulation of gene expression is a hallmark of cancer. Broadly speaking, my research is focused on the changes in gene expression that characterize the transition from normal to cancerous states, i.e. tumorigenesis. To study such changes, I performed integrated analysis of next generation sequencing data for matched normal and primary tumor samples from hundreds of patients across numerous different cancer types. By analyzing this sequencing data, I have been able to explore the global landscape of transcriptional reprogramming in cancer and discover how changes in the regulation of gene expression may be implicated in tumorigenesis. My thesis is focused on four specific areas of transcriptional reprogramming in cancer: (1) changes in the expression and activity of transposable elements (TEs), (2) changes in alternative splicing induced by TEs, (3) allele-specific expression of tumor suppressor genes (TSGs), and (4) gene expression changes that are implicated in cancer drug response. TEs are known to be uniformly overexpressed in cancer, suggesting a possible role for their activity in tumorigenesis. I discovered a class of long interspersed nuclear elements (the LINE-1 family) with elevated levels of expression and activity in three different cancer types, and I showed examples where cancer-specific LINE-1 insertions disrupt enhancers, leading to the down-regulation of TSGs. TEs are also implicated in the creation of novel splicing isoforms, and aberrant alternative splicing has been associated with tumorigenesis for a number of different cancers. Integrated analysis of genome sequence and transcriptome data revealed thousands of TE-generated alternative splice events genome-wide, including close to 5,000 events distributed among cancer associated genes. I explored the functional implications of specific cases of isoform switching, whereby TE-induced isoforms of cancer associated genes show elevated levels of relative expression in tumor samples. A closer look at TSG expression in matched normal and tumor samples indicated that functionally important changes in patterns of allele-specific expression in individuals heterozygous for loss-of-function TSG alleles is a significant factor in cancer onset/progression. These results identified a variety of molecular mechanisms that contribute to the observed changes in allele-specific expression patterns in cancer with allele-specific alternative splicing mediated by anti-sense RNA emerging as a predominant factor. Furthermore, analysis of the genomic variation for world-wide human populations demonstrates that loss-of-function TSG alleles are segregating at remarkedly high frequencies implying that a significant fraction of otherwise healthy individuals may be pre-disposed to developing cancer. For the final study of my thesis research, I applied the gene expression data from primary tumor samples to build predictive models of cancer drug response for two common chemotherapeutics: 5-Fluorouracil and Gemcitabine. My gene expression based models predict whether patients will respond to individual therapies with up to 86% accuracy. The genes that I found to be most informative for predicting drug response were enriched in well-known cancer signaling pathways highlighting their potential significance in prognosis of chemotherapy.
Building a systematic analytic pipeline – big data innovation in healthcare

(Georgia Institute of Technology, 2019-08-27) Wang, Yuanbo

Electronic Health Records (EHR) containing large amount of patient data present both opportunities and challenges to industry, policy makers, and researchers. Data-driven healthcare utilizing big data in EHR has the potential to revolutionize care delivery while reducing costs. However, for researchers, policymakers, and practitioners to take full advantage of the benefits that electronic records can provide, several challenges must be addressed: 1) Extraction and coding methods for EHR data must be strategically designed to address issues of data quantity, quality, and patient confidentiality; 2) Standardization of clinical terminologies is essential in facilitating interoperability among EHR systems and allows for multi-site comparative effectiveness studies; 3) Effective methods for mining longitudinal health data common in the EHR are critical for revealing disease progression, treatment patterns, and patient similarities, all of which play important role in clinical decision support and treatment improvement; 4) Advanced machine learning techniques are necessary for early detection and prognosis of disease and identifying critical factors that impact patient outcome and; 5) Practical intervention strategies must be developed to address healthcare disparity in rural and remote areas with lack of resources and access. This thesis focuses on these five issues by developing a systematic analytic pipeline for big data in healthcare. Specifically, innovative strategies are developed for information extraction, clinical terminology mapping, time-series mining and clustering, feature selection and discriminatory modeling. Finally, practical implementation methods for telehealth services are designed to reduce healthcare disparity in underserved rural Appalachian counties in Georgia.
Public health informatics - Strategy and decision modeling

(Georgia Institute of Technology, 2019-08-21) Tian, Haozheng

My research is composed of three studies focused on providing decision modeling and analytical tools with the objective of protecting public health. The first study introduces an agent-based simulation platform that serves as a decision support system for crowd management in public venues. I propose a new implementation of agent-based simulation with improvement on four aspects: path planning, collision avoidance, emotion modeling and optimization with simulation. The deliverables of this study also include a complete simulation platform for researcher’s use. The second study applies a data-driven informatics and machine learning approach to quantify the outcome of practice variance of medical care providers. The study investigates the safety and efficacy of a large-dose, needle-based epidural anesthesia technique for parturient women. Machine learning model is proposed as the classifier to predict the occurrence of hypotension. Further, machine learning approach is applied to predict the outcome of epidural anesthesia, uncovering the important factors of a successful practice. Quantification of the effect of practice variance and medicine usage is provided. The findings from this investigation facilitate delivery improvement and establish an improved clinical practice guideline for training and for dissemination of safe practice. The third study proposes the application of convolutional neural network (CNN) in the prediction of antigenicity of influenza viruses (A/H3N2) and vaccine recommendation. The study systematically explores the ways of representation of hemagglutinin (HA) besides using binary digit or character as widely applied in other researches. Heuristic optimization is applied to optimize the selection of AAindex as well as the structure of CNN. Contrasting to other state-of-the-art approaches, the model offers better coverage in vaccine recommendation and has superior performance in accurate prediction of antigenicity.