Constructing a graphical model of the Drosophila melanogaster metabolome

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dc.contributor Staudhammer, Christina
dc.contributor Kocot, Kevin M.
dc.contributor Pienaar, Jason
dc.contributor Chen, Yuhui
dc.contributor.advisor Reed, Laura K.
dc.contributor.author Oza, Vishal Himanshu
dc.date.accessioned 2020-09-30T17:24:50Z
dc.date.available 2020-09-30T17:24:50Z
dc.date.issued 2020
dc.identifier.other u0015_0000001_0003613
dc.identifier.other Oza_alatus_0004D_14072
dc.identifier.uri http://ir.ua.edu/handle/123456789/7012
dc.description Electronic Thesis or Dissertation
dc.description.abstract Metabolomics, a relatively late entrant in the ’omics’ pyramid, aims to capture a complete snapshot of the metabolome of an organism at any given point in time. Recent advances in mass spectrometry techniques have allowed for the simultaneous detection of hundreds of metabolites in a given sample. However, metabolomics data suffers from high dimen- sionality, high correlations, and the presence of unknown metabolites. In my Ph.D. disser- tation, I have employed machine learning techniques and graphical models to analyze and deconstruct some of the complexities in metabolomics data in Drosophila melanogaster. In chapter 1, I introduce the challenges in metabolomics data analysis and outline my dissertation. In chapter 2, I employed the Random Forest algorithm, to identify essential metabolites that best differentiate between the high-fat diet and normal diet. I found that flies on a high-fat diet had an upregulated omega fatty acid oxidation pathway. Further- more, I analyzed the network structure differences between the high-fat diet and normal diet-fed flies using Gaussian Graphical Models. The edge symmetric difference between the two networks was 0.786, indicating very different topology. Chapter 3 shows the use of Bayesian networks to predict metabolic networks from the untargeted metabolomics data. The networks obtained were then compared to known metabolic networks in various organisms present in KEGG. I found that the generated Bayesian networks showed a similar degree distribution, had similar secondary motif com- position, and similar short path length distribution as the known KEGG metabolic net- works. Thus, I demonstrate that Bayesian network analysis can be successfully utilized for untargeted metabolomics data to generate data-driven network models that have similar underlying characteristics as known metabolic networks. In chapter 4, we present FlyNet, a multilayer network database conceptualized and con- structed for storing and visualizing complex network data. FlyNet integrates the metabolome with the genome and the proteome to facilitate integrative studies in Drosophila melanogaster. As an example, I show how the betweenness of gene and protein nodes changes in a mul- tilayer setting compared to a single layer analysis. Furthermore, I show how using FlyNet, one can query a possible relationship between genes and metabolites across different bio- logical layers.
dc.format.extent 415 p.
dc.format.medium electronic
dc.format.mimetype application/pdf
dc.language English
dc.language.iso en_US
dc.publisher University of Alabama Libraries
dc.relation.ispartof The University of Alabama Electronic Theses and Dissertations
dc.relation.ispartof The University of Alabama Libraries Digital Collections
dc.relation.hasversion born digital
dc.rights All rights reserved by the author unless otherwise indicated.
dc.subject.other Biology
dc.subject.other Bioinformatics
dc.title Constructing a graphical model of the Drosophila melanogaster metabolome
dc.type thesis
dc.type text
etdms.degree.department University of Alabama. Department of Biological Sciences
etdms.degree.discipline Biological Sciences
etdms.degree.grantor The University of Alabama
etdms.degree.level doctoral
etdms.degree.name Ph.D.


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