Tutorial on biological networks

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.

Integrative Functional Transcriptomic Analyses Implicate Shared Molecular Circuits in Sensorineural Hearing Loss

“…We next conducted the protein-protein interaction (PPI) analysis for the co-up-regulated genes within M4 and M13 to provide an independent line of interrogation for the centric profiles. We calculated the degree and the closeness centrality for each node, which are commonly used as centrality measures ( Vital-Lopez et al, 2012 ), and examined their relationships for all nodes to identify hub genes in the network. The top 15 genes in the PPI network were highlighted, which directly interacted with 298 (41.5%) co-up-regulated genes and involved in 840 (22.9%) PPIs ( Figure 3B ).…”

Scale invariance in natural and artificial collective systems: a review

“…proteins, genes, etc.) as the nodes and their corresponding interactions as the links, which could be directed or undirected [50]. Although the data available for most molecular networks are incomplete, Rzhetsky & Gomez [51] have shown that the majority can be identified as SF networks.…”

Shortest path counting in probabilistic biological networks

“…Edge betweenness centrality and network modularity are two closely related network characteristics to shortest path. Edge betweenness centrality, a measure of the number of shortest paths that go through each edge, describes the essentiality of the underlying gene-to-gene interactions, and helps us to discover the bottlenecks in the biological systems [ 15 ]. Finding the centrality scores of edges is thus crucial to understand how the biological networks operate, and how they could be fixed or manipulated.…”