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

Causation in Biology: A Biomolecular...

Lewis, Nathan Enoch.

Linked to FindBook

Google Book

Amazon

博客來

Causation in Biology: A Biomolecular Systems View.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Causation in Biology: A Biomolecular Systems View./
Author:	Lewis, Nathan Enoch.
Description:	179 p.
Notes:	Source: Dissertation Abstracts International, Volume: 73-07(E), Section: B.
Contained By:	Dissertation Abstracts International73-07B(E).
Subject:	Engineering, Biomedical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3502539
ISBN:	9781267256010

Causation in Biology: A Biomolecular Systems View.
Lewis, Nathan Enoch.

Causation in Biology: A Biomolecular Systems View. - 179 p.

Source: Dissertation Abstracts International, Volume: 73-07(E), Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2012.

Fundamental physical phenomena are studied with a "cause and effect" approach. This enables understanding and prediction by employing mathematically formulated physical laws. Such approaches are less successful in biological systems, since they are subject to dual causation. That is, both physicochemical laws and evolving genetic constraints govern organisms. Biological systems respond immediately to stimuli (proximal causation) against a constant genetic background; however, these responses depend upon evolving genetic programs. Alterations in genetic programs are manifestations of distal causation, representing changes induced by genetic drift and natural selection. Constraint-based reconstruction and analysis is an emerging modeling approach that can account for both physicochemical constraints in biological systems and some evolutionary selective pressures. Here, constraint-based modeling is deployed to integrate disparate data types with genome-scale metabolic models to gain insight into mechanisms in proximal and distal causation, and conceptual advances are presented with respect to how these data are interpreted using constraint-based models. Specifically, these advances are used to suggest mechanisms determining proximal responses with respect to disease progression in human brain metabolism and the regulation of prokaryotic metabolism in dynamic environments. In addition, methods are presented that use genome-scale models of metabolism to analyze various data types to identify determinants of distal causation. Specifically, these methods are deployed to show that the evolution of enzyme specificity is guided by network context and the need to produce biomass. Moreover, these pressures further tune expression levels of metabolic pathways in laboratory evolved bacteria. Thus, through network reconstruction and data integration, vast amounts of data can be queried and provide detailed insight into proximal and distal causation in complex biological networks.

ISBN: 9781267256010Subjects--Topical Terms:

1017684
Engineering, Biomedical.

Causation in Biology: A Biomolecular Systems View.
LDR:02917nam a2200301 4500 001 1965483
005 20141030134113.5
008 150210s2012 ||||||||||||||||| ||eng d
020 $a 9781267256010
035 $a (MiAaPQ)AAI3502539
035 $a AAI3502539
040 $a MiAaPQ $c MiAaPQ
100 1 $a Lewis, Nathan Enoch. $3 2102150
245 1 0 $a Causation in Biology: A Biomolecular Systems View.
300 $a 179 p.
500 $a Source: Dissertation Abstracts International, Volume: 73-07(E), Section: B.
500 $a Advisers: Bernhard O. Palsson; Jeff Hasty.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2012.
520 $a Fundamental physical phenomena are studied with a "cause and effect" approach. This enables understanding and prediction by employing mathematically formulated physical laws. Such approaches are less successful in biological systems, since they are subject to dual causation. That is, both physicochemical laws and evolving genetic constraints govern organisms. Biological systems respond immediately to stimuli (proximal causation) against a constant genetic background; however, these responses depend upon evolving genetic programs. Alterations in genetic programs are manifestations of distal causation, representing changes induced by genetic drift and natural selection. Constraint-based reconstruction and analysis is an emerging modeling approach that can account for both physicochemical constraints in biological systems and some evolutionary selective pressures. Here, constraint-based modeling is deployed to integrate disparate data types with genome-scale metabolic models to gain insight into mechanisms in proximal and distal causation, and conceptual advances are presented with respect to how these data are interpreted using constraint-based models. Specifically, these advances are used to suggest mechanisms determining proximal responses with respect to disease progression in human brain metabolism and the regulation of prokaryotic metabolism in dynamic environments. In addition, methods are presented that use genome-scale models of metabolism to analyze various data types to identify determinants of distal causation. Specifically, these methods are deployed to show that the evolution of enzyme specificity is guided by network context and the need to produce biomass. Moreover, these pressures further tune expression levels of metabolic pathways in laboratory evolved bacteria. Thus, through network reconstruction and data integration, vast amounts of data can be queried and provide detailed insight into proximal and distal causation in complex biological networks.
590 $a School code: 0033.
650 4 $a Engineering, Biomedical. $3 1017684
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Chemistry, Biochemistry. $3 1017722
650 4 $a Biology, Systematic. $3 1676856
690 $a 0541
690 $a 0715
690 $a 0487
690 $a 0423
710 2 $a University of California, San Diego. $b Bioengineering. $3 1017915
773 0 $t Dissertation Abstracts International $g 73-07B(E).
790 $a 0033
791 $a Ph.D.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3502539