A Guide to Conquer the Biological Network Era Using Graph Theory

Abstract

Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer-and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.

A new approach to the anthropocentric design of human–robot collaborative environments

“…It provides an abstraction from the real model preserving all dependencies and relations between the elements involved. Graph theory is characterised by a set of nodes and edges (G={N,E}) [26], and it uses an array or matrix for data organisation. The most significant matrix is the adjacency matrix that highlights the connection between the nodes; the elements of 1 or 0 mean the row element is either related to the column element or not.…”

The Network Analysis Profiler (NAP v2.0): A web tool for visual topological comparison between multiple networks

“…Betweenness centrality quantifies the number of times a node acts as a bridge along the shortest path between two other nodes. Centralization closeness : It measures the speed with which randomly walking messages reach a vertex from elsewhere in the graph. Centralization degree : It is defined as the number of links incident upon a node. Graph mincut: Calculates the minimum st -cut between two vertices in a graph. The minimum st -cut between source and target is the minimum total weight of edges needed to remove to eliminate all paths from source to target. Motifs-3: Searches a graph for motifs of size 3 (6). Motifs-4 : Searches a graph for motifs of size 4 (6).…”

“…Networks are key representations which can capture the associations and interactions between any kind of bioentity such as genes, proteins, diseases, drugs, small molecules and others (1)(2)(3)(4)(5)(6). Gene co-expression networks, gene regulatory networks, protein-protein interaction networks (PPIs), signal transduction networks, metabolic networks, gene-disease networks, sequence similarity networks, phylogenetic networks, ecological networks, epidemiological networks, drug-disease networks, disease-symptom networks, literature co-occurrence networks, food webs, semantic and knowledge networks are the most widely known network types in the biomedical and biomedicalrelated fields (6). However, not all networks are the same in terms of structure and come with certain topological features.…”

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