Analysis of Biological Networks

• Published in: Distributed and Sequential Algorithms for Bioinformatics Vol. 23; pp. 213 - 240
• Main Author: Erciyes, Kayhan
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2015; Springer International Publishing
• Series: Computational Biology
• Subjects: Algorithms & data structures; Betweenness Centrality; Biological Network; Cluster Coefficient; Degree Distribution; Life sciences: general issues; Maths for computer scientists; Metabolic Network
• ISBN: 9783319249643; 3319249649
• ISSN: 1568-2684
• DOI: 10.1007/978-3-319-24966-7_10

Biological processes such as the interaction between proteins or metabolic reactions can be represented by networks which can be modeled by graphs. Biological networks are present in the cell and outside the cell. Our aim in this chapter is to first introduce the networks in the cell and analyze them as graphs. Centrality analysis provides information about the important nodes and edges in biological networks and we describe algorithms to find various centrality measures. The main problems to investigate in the graph structure of a biological network are the module detection, discovery of recurrent subgraphs called network motifs and aligning two or more networks as we discuss. We will see these networks have interesting features such as small-world, scale-free properties which are not found in random networks. All of these problems are discussed in detail in the rest of this part of the book.