Graph-based modeling and evolutionary analysis of microbial metabolism

dc.contributor.advisorMa, Jianpeng
dc.contributor.advisorNakhleh, Luay K.
dc.contributor.committeeMemberIgoshin, Oleg A.
dc.contributor.committeeMemberBennett, George N.
dc.creatorZhou, Wanding
dc.date.accessioned2013-09-16T19:15:44Z
dc.date.accessioned2013-09-16T19:15:51Z
dc.date.available2013-09-16T19:15:44Z
dc.date.available2013-09-16T19:15:51Z
dc.date.created2013-05
dc.date.issued2013-09-16
dc.date.submittedMay 2013
dc.date.updated2013-09-16T19:15:51Z
dc.description.abstractMicrobial organisms are responsible for most of the metabolic innovations on Earth. Understanding microbial metabolism helps shed the light on questions that are central to biology, biomedicine, energy and the environment. Graph-based modeling is a powerful tool that has been used extensively for elucidating the organising principles of microbial metabolism and the underlying evolutionary forces that act upon it. Nevertheless, various graph-theoretic representations and techniques have been applied to metabolic networks, rendering the modeling aspect ad hoc and highlighting the conflicting conclusions based on the different representations. The contribution of this dissertation is two-fold. In the first half, I revisit the modeling aspect of metabolic networks, and present novel techniques for their representation and analysis. In particular, I explore the limitations of standard graphs representations, and the utility of the more appropriate model---hypergraphs---for capturing metabolic network properties. Further, I address the task of metabolic pathway inference and the necessity to account for chemical symmetries and alternative tracings in this crucial task. In the second part of the dissertation, I focus on two evolutionary questions. First, I investigate the evolutionary underpinnings of the formation of communities in metabolic networks---a phenomenon that has been reported in the literature and implicated in an organism's adaptation to its environment. I find that the metabolome size better explains the observed community structures. Second, I correlate evolution at the genome level with emergent properties at the metabolic network level. In particular, I quantify the various evolutionary events (e.g., gene duplication, loss, transfer, fusion, and fission) in a group of proteobacteria, and analyze their role in shaping the metabolic networks and determining the organismal fitness. As metabolism gains an increasingly prominent role in biomedical, energy, and environmental research, understanding how to model this process and how it came about during evolution become more crucial. My dissertation provides important insights in both directions.
dc.format.mimetypeapplication/pdf
dc.identifier.citationZhou, Wanding. "Graph-based modeling and evolutionary analysis of microbial metabolism." (2013) Diss., Rice University. <a href="https://hdl.handle.net/1911/72072">https://hdl.handle.net/1911/72072</a>.
dc.identifier.slug123456789/ETD-2013-05-442
dc.identifier.urihttps://hdl.handle.net/1911/72072
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectBioinformatics
dc.subjectSystematic biology
dc.subjectMetabolic network
dc.subjectGraph
dc.subjectModeling
dc.subjectEvolution
dc.subjectMicrobiology
dc.titleGraph-based modeling and evolutionary analysis of microbial metabolism
dc.typeThesis
dc.type.materialText
thesis.degree.departmentBioengineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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