東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Computational approaches to the infe...

Petti, Allegra Adele.

FindBook

Google Book

Amazon

博客來

Computational approaches to the inference of biological network connectivity.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational approaches to the inference of biological network connectivity./
作者:	Petti, Allegra Adele.
面頁冊數:	197 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2397.
Contained By:	Dissertation Abstracts International66-05B.
標題:	Biology, Genetics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3174011
ISBN:	9780542115578

Computational approaches to the inference of biological network connectivity.
Petti, Allegra Adele.

Computational approaches to the inference of biological network connectivity. - 197 p.

Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2397.

Thesis (Ph.D.)--Harvard University, 2005.

The phenotype of a unicellular organism is determined by an integrated network of genes, proteins, and metabolites that participate in reciprocal regulatory relationships. Creating a quantitative description of this network is essential to understanding, predicting and manipulating cellular behavior. A first step toward this goal is deciphering the connectivity of the network, i.e., the pattern of interactions among its components, and its general organizational features. Given the complexity of this task, it is convenient to view the integrated network as a group of superimposed subnetworks, including the gene regulatory, protein, and metabolic networks. This thesis addresses the inference of connectivity in the gene and protein networks of Saccharomyces cerevisiae and Pseudomonas aeruginosa.

ISBN: 9780542115578Subjects--Topical Terms:

1017730
Biology, Genetics.

Computational approaches to the inference of biological network connectivity.
LDR:03251nmm 2200325 4500 001 1824766
005 20061206120144.5
008 130610s2005 eng d
020 $a 9780542115578
035 $a (UnM)AAI3174011
035 $a AAI3174011
040 $a UnM $c UnM
100 1 $a Petti, Allegra Adele. $3 1913815
245 1 0 $a Computational approaches to the inference of biological network connectivity.
300 $a 197 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2397.
500 $a Adviser: George M. Church.
502 $a Thesis (Ph.D.)--Harvard University, 2005.
520 $a The phenotype of a unicellular organism is determined by an integrated network of genes, proteins, and metabolites that participate in reciprocal regulatory relationships. Creating a quantitative description of this network is essential to understanding, predicting and manipulating cellular behavior. A first step toward this goal is deciphering the connectivity of the network, i.e., the pattern of interactions among its components, and its general organizational features. Given the complexity of this task, it is convenient to view the integrated network as a group of superimposed subnetworks, including the gene regulatory, protein, and metabolic networks. This thesis addresses the inference of connectivity in the gene and protein networks of Saccharomyces cerevisiae and Pseudomonas aeruginosa.
520 $a It is widely believed that biological networks are organized into functional modules, defined here as groups of genes, proteins, and other small molecules that participate in common subcellular processes. Chapter 2 presents a computational method to identify transcriptional coordination among predefined functional modules of genes in S. cerevisiae. Two modules are said to be coordinated by a particular transcription factor if it confers a distinct expression profile on its target genes in each module. The method was applied to a variety of module pairs to reveal a global network of functional coordination in yeast.
520 $a Chapter 3 describes an algorithm that searches the S. cerevisiae protein-protein interaction network for signal transduction pathways whose components are encoded by coexpressed genes. This enables the discovery of known and novel linear signal transduction pathways. When linear pathways with common endpoints are combined, the resulting network represents the complex interactions underlying signal transduction more fully than a traditional linear representation.
520 $a Chapter 4 describes the identification of putative transcription factor binding motifs that shape the topology of the quorum sensing gene regulatory network in the pathogenic bacterium P. aeruginosa. The approach identified novel, putative motifs, and variants of a motif known to function in quorum sensing. These motifs may imbue quorum-sensing regulation with signal and/or temporal specificity.
590 $a School code: 0084.
650 4 $a Biology, Genetics. $3 1017730
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biophysics, General. $3 1019105
690 $a 0369
690 $a 0308
690 $a 0786
710 2 0 $a Harvard University. $3 528741
773 0 $t Dissertation Abstracts International $g 66-05B.
790 1 0 $a Church, George M., $e advisor
790 $a 0084
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3174011