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From topological network analyses an...

Milenkovic, Tijana.

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From topological network analyses and alignments to biological function, disease, and evolution.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	From topological network analyses and alignments to biological function, disease, and evolution./
Author:	Milenkovic, Tijana.
Description:	238 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-04, Section: B, page: 2499.
Contained By:	Dissertation Abstracts International71-04B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3397055
ISBN:	9781109672145

From topological network analyses and alignments to biological function, disease, and evolution.
Milenkovic, Tijana.

From topological network analyses and alignments to biological function, disease, and evolution. - 238 p.

Source: Dissertation Abstracts International, Volume: 71-04, Section: B, page: 2499.

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

We present computational methods for aligning, analyzing, and modeling biological networks that give insights into biological function, disease, and evolution.

ISBN: 9781109672145Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

From topological network analyses and alignments to biological function, disease, and evolution.
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008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781109672145
035 $a (UMI)AAI3397055
035 $a AAI3397055
040 $a UMI $c UMI
100 1 $a Milenkovic, Tijana. $3 1678820
245 1 0 $a From topological network analyses and alignments to biological function, disease, and evolution.
300 $a 238 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-04, Section: B, page: 2499.
500 $a Adviser: Natasa Przulj.
502 $a Thesis (Ph.D.)--University of California, Irvine, 2010.
520 $a We present computational methods for aligning, analyzing, and modeling biological networks that give insights into biological function, disease, and evolution.
520 $a Comparing networks of different species is an important problem in evolutionary and systems biology. Existing methods incorporate some a priori information about nodes such as sequence similarities of proteins in protein-protein interaction (PPI) networks. We introduce novel network alignment algorithms that are based solely on network topology. As such, our methods can align any type of networks, not just biological ones. We demonstrate that both biological function of proteins and species phylogeny can be extracted from our topological alignments of PPI and metabolic networks.
520 $a Similar to network comparisons across species that can help in functional annotation of proteins and in identification of conserved functional modules or protein complexes, biological function of uncharacterized proteins in a PPI network of a single species can be determined from the function of other, well described proteins from the same network. Since proteins interact to perform a function rather than act in isolation, analyzing patterns of their interactions in PPI networks is expected to provide insights into their biological function. Indeed, we identify a close relationship between network topology and various biological properties. We find that PPI network topology successfully captures proteins' homology information, and thus, it could potentially be used as a complementary method to sequence-based approaches for homology detection. We demonstrate that topological characteristics of proteins in a human PPI network imply their involvement in cancer. We identify from network topology novel pigment regulators that are components of melanogenesis-related pathways and our predictions are phenotypically validated. Finally, we acquire the most comprehensive assessment to date of the PPI network of the yeast protasome responsible for protein degradation.
520 $a Finding an adequate model for biological networks is an important step towards understanding them. We investigate topological properties of protein structure networks to find a well-fitting network model for them; our results are of biological importance since understanding these networks might provide insights into protein folding, stability, and function. Additionally, we present an approach that applies probabilistic classifiers to network topology to predict the best-fitting network model for RIGs and PPI networks. The hope is that a good network model could provide insights into understanding of biological function, disease, and evolution.
520 $a The significance of our results is that we extract biologically significant meaning from a new source of information, pure network topology, independently of sequence or any other commonly used biological information. We believe that we have just barely scratched the surface of the information that can be extracted from network topology.
590 $a School code: 0030.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0984
710 2 $a University of California, Irvine. $b Computer Science - Ph.D. $3 1674690
773 0 $t Dissertation Abstracts International $g 71-04B.
790 1 0 $a Przulj, Natasa, $e advisor
790 1 0 $a Hayes, Wayne $e committee member
790 1 0 $a Mjolsness, Eric $e committee member
790 1 0 $a Xie, Xiaohui $e committee member
790 $a 0030
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3397055