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

Modeling multi-factor binding of the...

Wasson, Todd Steven.

FindBook

Google Book

Amazon

博客來

Modeling multi-factor binding of the genome.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Modeling multi-factor binding of the genome./
作者:	Wasson, Todd Steven.
面頁冊數:	138 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-12, Section: B, page: 7175.
Contained By:	Dissertation Abstracts International71-12B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3427654
ISBN:	9781124322162

Modeling multi-factor binding of the genome.
Wasson, Todd Steven.

Modeling multi-factor binding of the genome. - 138 p.

Source: Dissertation Abstracts International, Volume: 71-12, Section: B, page: 7175.

Thesis (Ph.D.)--Duke University, 2010.

Hundreds of different factors adorn the eukaryotic genome, binding to it in large number. These DNA binding factors (DBFs) include nucleosomes, transcription factors (TFs), and other proteins and protein complexes, such as the origin recognition complex (ORC). DBFs compete with one another for binding along the genome, yet many current models of genome binding do not consider different types of DBFs together simultaneously. Additionally, binding is a stochastic process that results in a continuum of binding probabilities at any position along the genome, but many current models tend to consider positions as being either binding sites or not.

ISBN: 9781124322162Subjects--Topical Terms:

517247
Statistics.

Modeling multi-factor binding of the genome.
LDR:02949nam 2200373 4500 001 1404611
005 20111130124058.5
008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124322162
035 $a (UMI)AAI3427654
035 $a AAI3427654
040 $a UMI $c UMI
100 1 $a Wasson, Todd Steven. $3 1683944
245 1 0 $a Modeling multi-factor binding of the genome.
300 $a 138 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-12, Section: B, page: 7175.
500 $a Adviser: Alexander J. Hartemink.
502 $a Thesis (Ph.D.)--Duke University, 2010.
520 $a Hundreds of different factors adorn the eukaryotic genome, binding to it in large number. These DNA binding factors (DBFs) include nucleosomes, transcription factors (TFs), and other proteins and protein complexes, such as the origin recognition complex (ORC). DBFs compete with one another for binding along the genome, yet many current models of genome binding do not consider different types of DBFs together simultaneously. Additionally, binding is a stochastic process that results in a continuum of binding probabilities at any position along the genome, but many current models tend to consider positions as being either binding sites or not.
520 $a Here, we present a model that allows a multitude of DBFs, each at different concentrations, to compete with one another for binding sites along the genome. The result is an 'occupancy profile', a probabilistic description of the DNA occupancy of each factor at each position. We implement our model efficiently as the software package COMPETE. We demonstrate genome-wide and at specific loci how modeling nucleosome binding alters TF binding, and vice versa, and illustrate how factor concentration influences binding occupancy. Binding cooperativity between nearby TFs arises implicitly via mutual competition with nucleosomes. Our method applies not only to TFs, but also recapitulates known occupancy profiles of a well-studied replication origin with and without ORC binding.
520 $a We then develop a statistical framework for tuning our model concentrations to further improve its predictions. Importantly, this tuning optimizes with respect to actual biological data. We take steps to ensure that our tuned parameters are biologically plausible.
520 $a Finally, we discuss novel extensions and applications of our model, suggesting next steps in its development and deployment.
590 $a School code: 0066.
650 4 $a Statistics. $3 517247
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0463
690 $a 0715
690 $a 0984
710 2 $a Duke University. $b Computational Biology and Bioinformatics. $3 1279998
773 0 $t Dissertation Abstracts International $g 71-12B.
790 1 0 $a Hartemink, Alexander J., $e advisor
790 1 0 $a West, Mike $e committee member
790 1 0 $a Mukherjee, Sayan $e committee member
790 1 0 $a Lew, Daniel J. $e committee member
790 1 0 $a McCarthy, Jeanette $e committee member
790 $a 0066
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3427654