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Using graph theory to analyze biological networks.

Pavlopoulos GA, Secrier M, Moschopoulos CN, Soldatos TG, Kossida S, Aerts J, Schneider R, Bagos PG - BioData Min (2011)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia, 35100, Greece. pavlopou@embl.de.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Network Motifs. Some common network motifs. A) Feed-forward loop. Type of networks: protein, neuron, electronic. B) Three chain. Type of network: food webs. C) Four node feedback. Type of network: gene regulatory, electronic. D) Three node feedback. Type of network: gene regulatory, electronic. E) Bi-parallel. Type of network: gene regulatory, biochemical. F) Bi-Fan. Type of networks: protein, neuron, electronic [74].
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Figure 6: Network Motifs. Some common network motifs. A) Feed-forward loop. Type of networks: protein, neuron, electronic. B) Three chain. Type of network: food webs. C) Four node feedback. Type of network: gene regulatory, electronic. D) Three node feedback. Type of network: gene regulatory, electronic. E) Bi-parallel. Type of network: gene regulatory, biochemical. F) Bi-Fan. Type of networks: protein, neuron, electronic [74].

Mentions: Network Motifs represent patterns in complex networks occurring significantly more often than in randomized networks [74]. They consist of subgraphs of local interconnections between network elements. A motif is a small connected graph G'. A match G' of a motif in graph G is a graph G'' which is isomorphic to G' and a subgraph of G. Signal transduction and gene regulatory networks tend to be described by various motifs [72,75]. Although motif determination gives lots of information concerning the properties and the characteristics of a network, it does not necessarily reveal evidence about its function and the function of its components [76]. However, some motifs have been found to be associated with optimized biological functions, like in the case of positive and negative feedback loops, oscillators or bifans [73]. Figure 6 shows the most common motifs that are found in various networks.


Using graph theory to analyze biological networks.

Pavlopoulos GA, Secrier M, Moschopoulos CN, Soldatos TG, Kossida S, Aerts J, Schneider R, Bagos PG - BioData Min (2011)

Network Motifs. Some common network motifs. A) Feed-forward loop. Type of networks: protein, neuron, electronic. B) Three chain. Type of network: food webs. C) Four node feedback. Type of network: gene regulatory, electronic. D) Three node feedback. Type of network: gene regulatory, electronic. E) Bi-parallel. Type of network: gene regulatory, biochemical. F) Bi-Fan. Type of networks: protein, neuron, electronic [74].
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3101653&req=5

Figure 6: Network Motifs. Some common network motifs. A) Feed-forward loop. Type of networks: protein, neuron, electronic. B) Three chain. Type of network: food webs. C) Four node feedback. Type of network: gene regulatory, electronic. D) Three node feedback. Type of network: gene regulatory, electronic. E) Bi-parallel. Type of network: gene regulatory, biochemical. F) Bi-Fan. Type of networks: protein, neuron, electronic [74].
Mentions: Network Motifs represent patterns in complex networks occurring significantly more often than in randomized networks [74]. They consist of subgraphs of local interconnections between network elements. A motif is a small connected graph G'. A match G' of a motif in graph G is a graph G'' which is isomorphic to G' and a subgraph of G. Signal transduction and gene regulatory networks tend to be described by various motifs [72,75]. Although motif determination gives lots of information concerning the properties and the characteristics of a network, it does not necessarily reveal evidence about its function and the function of its components [76]. However, some motifs have been found to be associated with optimized biological functions, like in the case of positive and negative feedback loops, oscillators or bifans [73]. Figure 6 shows the most common motifs that are found in various networks.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia, 35100, Greece. pavlopou@embl.de.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus