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

Systems theory for pharmaceutical dr...

Aswani, Anil Jayanti.

FindBook

Google Book

Amazon

博客來

Systems theory for pharmaceutical drug discovery.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Systems theory for pharmaceutical drug discovery./
作者:	Aswani, Anil Jayanti.
面頁冊數:	152 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-09, Section: B, page: .
Contained By:	Dissertation Abstracts International71-09B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3413307
ISBN:	9781124140025

Systems theory for pharmaceutical drug discovery.
Aswani, Anil Jayanti.

Systems theory for pharmaceutical drug discovery. - 152 p.

Source: Dissertation Abstracts International, Volume: 71-09, Section: B, page: .

Thesis (Ph.D.)--University of California, Berkeley, 2010.

Biological networks are comprised of thousands of interacting components, and these networks have complicated patterns of feedback and feed-forward motifs. It is practically impossible to use intuition to determine whether simultaneously modifying multiple pharmaceutical targets has a good therapeutic response. Even when a drug is discovered which is safe in humans and highly-effective against its target, the medical effect on the disease may be underwhelming. This provides a strong impetus for developing a systems theory for pharmaceutical drug discovery. This thesis discusses system theoretic tools which are useful for doing drug discovery. The first class of tools discussed is system identification tools, and case studies of parametric modeling are given. A new statistical system identification procedure which exploits the geometric and hierarchical structure of many biological (and engineering) systems is presented, and this new procedure is applied to engineering and biological systems. The second class of tools discussed is a new set of target selection tools. Given mathematical models of biological networks, these tools select a set of targets for pharmaceutical drugs. The targets are selected to achieve good medical outcomes for patients by reducing the effect of diseases on pathways and ensuring that the targets do not too adversely affect healthy cells. The ultimate goal of the work presented in this thesis is to create a framework which can be used to rationally select new drug targets and also be able to create personalized medicine treatments which are tailored to the particular phenotypic behavior of an individual's disease.

ISBN: 9781124140025Subjects--Topical Terms:

517247
Statistics.

Systems theory for pharmaceutical drug discovery.
LDR:02653nam 2200313 4500 001 1392598
005 20110218114623.5
008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124140025
035 $a (UMI)AAI3413307
035 $a AAI3413307
040 $a UMI $c UMI
100 1 $a Aswani, Anil Jayanti. $3 1671056
245 1 0 $a Systems theory for pharmaceutical drug discovery.
300 $a 152 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-09, Section: B, page: .
500 $a Adviser: Claire Tomlin.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2010.
520 $a Biological networks are comprised of thousands of interacting components, and these networks have complicated patterns of feedback and feed-forward motifs. It is practically impossible to use intuition to determine whether simultaneously modifying multiple pharmaceutical targets has a good therapeutic response. Even when a drug is discovered which is safe in humans and highly-effective against its target, the medical effect on the disease may be underwhelming. This provides a strong impetus for developing a systems theory for pharmaceutical drug discovery. This thesis discusses system theoretic tools which are useful for doing drug discovery. The first class of tools discussed is system identification tools, and case studies of parametric modeling are given. A new statistical system identification procedure which exploits the geometric and hierarchical structure of many biological (and engineering) systems is presented, and this new procedure is applied to engineering and biological systems. The second class of tools discussed is a new set of target selection tools. Given mathematical models of biological networks, these tools select a set of targets for pharmaceutical drugs. The targets are selected to achieve good medical outcomes for patients by reducing the effect of diseases on pathways and ensuring that the targets do not too adversely affect healthy cells. The ultimate goal of the work presented in this thesis is to create a framework which can be used to rationally select new drug targets and also be able to create personalized medicine treatments which are tailored to the particular phenotypic behavior of an individual's disease.
590 $a School code: 0028.
650 4 $a Statistics. $3 517247
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Biology, Bioinformatics. $3 1018415
690 $a 0463
690 $a 0544
690 $a 0715
710 2 $a University of California, Berkeley. $b Electrical Engineering & Computer Sciences. $3 1671057
773 0 $t Dissertation Abstracts International $g 71-09B.
790 1 0 $a Tomlin, Claire, $e advisor
790 1 0 $a El Ghaoui, Laurent $e committee member
790 1 0 $a Bickel, Peter $e committee member
790 $a 0028
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3413307