Using graph theory to analyze biological networks

被引：439
|
作者：
Pavlopoulos, Georgios A. [1 ,2 ]
Secrier, Maria [3 ]
Moschopoulos, Charalampos N. [4 ,5 ]
Soldatos, Theodoros G. [6 ]
Kossida, Sophia [5 ]
Aerts, Jan [2 ]
Schneider, Reinhard [7 ]
Bagos, Pantelis G. [1 ]
机构：
[1] Univ Cent Greece, Dept Comp Sci & Biomed Informat, Lamia 35100, Greece
[2] Katholieke Univ Leuven, ESAT SCD, Fac Engn, B-3001 Louvain, Belgium
[3] EMBL, Struct & Computat Biol Unit, D-69117 Heidelberg, Germany
[4] Univ Patras, Dept Comp Engn & Informat, Patras 6500, Greece
[5] Acad Athens, Biomed Res Fdn, Bioinformat & Med Informat Team, Athens 11527, Greece
[6] Life Biosyst GmbH, D-69117 Heidelberg, Germany
[7] Univ Luxembourg, Luxembourg Ctr Syst Biomed, L-1511 Luxembourg, Luxembourg
来源：
BIODATA MINING | 2011年 / 4卷
关键词：
biological network clustering analysis; graph theory; node ranking; EVOLUTIONARY GENETICS ANALYSIS; PROTEIN-PROTEIN INTERACTIONS; MARKUP LANGUAGE; TRANSCRIPTIONAL REGULATION; CLUSTERING ALGORITHMS; BIOCHEMICAL NETWORKS; REGULATORY NETWORKS; SIGNAL-TRANSDUCTION; METABOLIC PATHWAYS; INTERACTION MAP;
D O I：
10.1186/1756-0381-4-10
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system.
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收藏
页数：27
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