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

Novel optimization-based methods of ...

Princeton University.

FindBook

Google Book

Amazon

博客來

Novel optimization-based methods of studying cellular signaling pathways.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Novel optimization-based methods of studying cellular signaling pathways./
作者:	Tan, Meng Piao.
面頁冊數:	391 p.
附註:	Advisers: Christodoulos A. Floudas; James R. Broach.
Contained By:	Dissertation Abstracts International70-01B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3341309
ISBN:	9780549969655

Novel optimization-based methods of studying cellular signaling pathways.
Tan, Meng Piao.

Novel optimization-based methods of studying cellular signaling pathways. - 391 p.

Advisers: Christodoulos A. Floudas; James R. Broach.

Thesis (Ph.D.)--Princeton University, 2009.

The advent of microarray technology has made it possible to simultaneously monitor and study expression behavior across entire genomes and is an efficient way of gathering information on genetic functions and pathways. However, the typically large number of genes and the complexity of the underlying biological networks make this a formidable task. A common first step to interpret DNA microarray data is the use of clustering techniques. Classifying genes into clusters can lead to interesting biological insights. Since genes with similar functions cluster together, grouping genes of known functions with poorly characterized ones may provide insights into the functions of the latter. Patterns seen in genome-wide expression data can then give indications about the status of cellular processes and information about unknown biological pathways and gene regulatory networks. It is with a rigorously derived set of clusters that we can then use to uncover insights on the control mechanisms these genes are responsible for in a cell and the ensuing biochemical networks involved. The complexity and size of these interacting components cannot be understood by experiments alone. Instead, the development of computational models and the integration of these models with actual experimental design can then provide valuable insight into these systems-level behaviors. In this dissertation, we present a robust clustering algorithm that iteratively identifies the most biologically coherent data groupings from gene expression data. We then develop optimization-based models to predict the most feasible interactions between these gene clusters and their regulatory transcription factors, as well as the optimal linkages between the upstream cellular metabolites. We use as case studies actual gene expression data and glucose sensing signaling pathways in yeast.

ISBN: 9780549969655Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Novel optimization-based methods of studying cellular signaling pathways.
LDR:02818nmm 2200289 a 45 001 890666
005 20101104
008 101104s2009 ||||||||||||||||| ||eng d
020 $a 9780549969655
035 $a (UMI)AAI3341309
035 $a AAI3341309
040 $a UMI $c UMI
100 1 $a Tan, Meng Piao. $3 1064628
245 1 0 $a Novel optimization-based methods of studying cellular signaling pathways.
300 $a 391 p.
500 $a Advisers: Christodoulos A. Floudas; James R. Broach.
500 $a Source: Dissertation Abstracts International, Volume: 70-01, Section: B, page: 0495.
502 $a Thesis (Ph.D.)--Princeton University, 2009.
520 $a The advent of microarray technology has made it possible to simultaneously monitor and study expression behavior across entire genomes and is an efficient way of gathering information on genetic functions and pathways. However, the typically large number of genes and the complexity of the underlying biological networks make this a formidable task. A common first step to interpret DNA microarray data is the use of clustering techniques. Classifying genes into clusters can lead to interesting biological insights. Since genes with similar functions cluster together, grouping genes of known functions with poorly characterized ones may provide insights into the functions of the latter. Patterns seen in genome-wide expression data can then give indications about the status of cellular processes and information about unknown biological pathways and gene regulatory networks. It is with a rigorously derived set of clusters that we can then use to uncover insights on the control mechanisms these genes are responsible for in a cell and the ensuing biochemical networks involved. The complexity and size of these interacting components cannot be understood by experiments alone. Instead, the development of computational models and the integration of these models with actual experimental design can then provide valuable insight into these systems-level behaviors. In this dissertation, we present a robust clustering algorithm that iteratively identifies the most biologically coherent data groupings from gene expression data. We then develop optimization-based models to predict the most feasible interactions between these gene clusters and their regulatory transcription factors, as well as the optimal linkages between the upstream cellular metabolites. We use as case studies actual gene expression data and glucose sensing signaling pathways in yeast.
590 $a School code: 0181.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Engineering, Chemical. $3 1018531
690 $a 0542
690 $a 0715
710 2 $a Princeton University. $3 645579
773 0 $t Dissertation Abstracts International $g 70-01B.
790 $a 0181
790 1 0 $a Broach, James R., $e advisor
790 1 0 $a Floudas, Christodoulos A., $e advisor
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3341309