東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Graph-based Modeling and Evolutionar...

Zhou, Wanding.

Linked to FindBook

Google Book

Amazon

博客來

Graph-based Modeling and Evolutionary Analysis of Microbial Metabolism.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Graph-based Modeling and Evolutionary Analysis of Microbial Metabolism./
Author:	Zhou, Wanding.
Description:	251 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.
Contained By:	Dissertation Abstracts International75-03B(E).
Subject:	Engineering, Biomedical. -
Online resource:	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.
LDR:03088nam a2200301 4500 001 1964252
005 20141015113820.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303621239
035 $a (MiAaPQ)AAI3577600
035 $a AAI3577600
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhou, Wanding. $3 2100667
245 1 0 $a Graph-based Modeling and Evolutionary Analysis of Microbial Metabolism.
300 $a 251 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.
500 $a Adviser: Luay Nakhleh.
502 $a Thesis (Ph.D.)--Rice University, 2013.
520 $a 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.
590 $a School code: 0187.
650 4 $a Engineering, Biomedical. $3 1017684
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Biology, Microbiology. $3 1017734
650 4 $a Computer Science. $3 626642
690 $a 0541
690 $a 0715
690 $a 0410
690 $a 0984
710 2 $a Rice University. $3 960124
773 0 $t Dissertation Abstracts International $g 75-03B(E).
790 $a 0187
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3577600