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Graph-based Modeling and Evolutionar...

Zhou, Wanding.

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Graph-based Modeling and Evolutionary Analysis of Microbial Metabolism.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Graph-based Modeling and Evolutionary Analysis of Microbial Metabolism./
作者:	Zhou, Wanding.
面頁冊數:	251 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.
Contained By:	Dissertation Abstracts International75-03B(E).
標題:	Engineering, Biomedical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3577600
ISBN:	9781303621239

Graph-based Modeling and Evolutionary Analysis of Microbial Metabolism.
Zhou, Wanding.

Graph-based Modeling and Evolutionary Analysis of Microbial Metabolism. - 251 p.

Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.

Thesis (Ph.D.)--Rice University, 2013.

Microbial 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 graph 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 of accounting 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 metabolic networks and determining 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 becomes more crucial. My dissertation provides important insights on both of the two issues.

ISBN: 9781303621239Subjects--Topical Terms:

1017684
Engineering, Biomedical.

Graph-based Modeling and Evolutionary Analysis of Microbial Metabolism.
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