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.
Highlights
The theory of complex networks plays an important role in a wide variety of disciplines, ranging from computer science, sociology, engineering and physics, to molecular and population biology
Protein-protein interaction (PPI) networks [1] are very diverse and it is difficult to come to general conclusions about their properties, mainly because data are generated from different sources both computationally and experimentally as described in a previous section
The mathematical discipline which underpins the study of complex networks in biological and other applications is graph theory
Summary
The theory of complex networks plays an important role in a wide variety of disciplines, ranging from computer science, sociology, engineering and physics, to molecular and population biology. A recent review article shows which file formats, visualization techniques and algorithms can be used for tree analysis [129] Another category of clustering algorithms tries to cluster data in separate groups by identifying common properties that the nodes of a network share. Like for example trying to find dense areas in a graph or areas where message exchange between nodes is easier or to identify strongly connected components or clique-like areas etc Many of such algorithms have been used in different case studies like for example to identify protein families [130], to detect protein complexes in PPI networks [131,132], or for finding patterns and motifs in a sequence [133]. Many visualization tools [144] such as Medusa [148], Cytoscape [149], Pajek [98] and many others [144] visualize networks in both 2D and 3D, but very few of them like Arena3D [150] try to bridge the gap between clustering analysis and visualization
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