This Title All WIREs
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 7.250

Tutorial on biological networks

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Abstract Understanding how the functioning of a biological system emerges from the interactions among its components is a long‐standing goal of network science. Fomented by developments in high‐throughput technologies to characterize biomolecules and their interactions, network science has emerged as one of the fastest growing areas in computational and systems biology research. Although the number of research and review articles on different aspects of network science is increasing, updated resources that provide a broad, yet concise, review of this area in the context of systems biology are few. The objective of this article is to provide an overview of the research on biological networks to a general audience, who have some knowledge of biology and statistics, but are not necessarily familiar with this research field. Based on the different aspects of network science research, the article is broadly divided into four sections: (1) network construction, (2) topological analysis, (3) network and data integration, and (4) visualization tools. We specifically focused on the most widely studied types of biological networks, which are, metabolic, gene regulatory, protein–protein interaction, genetic interaction, and signaling networks. In future, with further developments on experimental and computational methods, we expect that the analysis of biological networks will assume a leading role in basic and translational research. © 2012 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Biological Data Mining Application Areas > Data Mining Software Tools Application Areas > Science and Technology

An example of different types of metabolic networks created from three coupled biochemical reactions: compound network, enzyme network, reaction network, and bipartite compound‐reaction network. E1–E4 are the enzymes catalyzing the reactions, whereas A, B, C, D, F, G, and H are metabolites. Note that the reaction and enzyme networks are not the same as R1 is catalyzed by two enzymes E1 and E2. In the bipartite metabolic network, compounds are connected to each other through reactions.

[ Normal View | Magnified View ]

An example of integration of a gene regulatory network (GRN) of Mycobacterium tuberculosis with gene expression data, used to identify the regions of the network activated by hypoxia: dosR transcriptionally activates a gene module of the GRN under hypoxic stress.8 (Reprinted with permission from Ref 8. Copyright 2010 RSC Publishing.)

[ Normal View | Magnified View ]

An example of three graphs G1, G2, and G3. Graph G1 is a subnetwork of graphs G2 and G3, but it is not an induced subgraph of these graphs, because there exist an edge in G2 that does not exist in G1 (between nodes C and D), and there exist edges in G3 that do not exist in G1 (between nodes A and C, C and D, and A and D). We can also notice that node B from G1 is topologically distinct (indicated by different colors) from the rest of nodes in G1, that nodes A and B from G2 are topologically distinct from each other and the rest of nodes from G2, but that all nodes in G3 have topologically the same position compared to other nodes in G3.

[ Normal View | Magnified View ]

An example of two graphs of the same size G and H that have exactly the same degree distribution [both networks have four nodes with degree two (dark gray nodes) and eight nodes with degree three (light gray nodes)], but with very different network structure: graph G consists of one, whereas graph H consists of four connected components, respectively; graph G is a bipartite graph (nodes circled with a solid line belong to one partition and nodes circled with a dashed line belong to the other partition), whereas the graph H is not. The average clustering coefficient of graph G is 0.00 because it does not contain any triangles, whereas the clustering coefficient of graph H is 0.67.

[ Normal View | Magnified View ]

Browse by Topic

Application Areas > Science and Technology
Application Areas > Data Mining Software Tools
Algorithmic Development > Biological Data Mining

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts