The human brain is a complex system, whose organization has been represented as a structural connectome of interconnected areas and a functional connectome of interregional neural activity. The human brain connectome can be mapped using MRI data, and further characterized through strategies based on graph theory. The modeling and analysis of connectome is a big challenge in neurosciences and network research. This chapter highlights main techniques for acquiring the process of brain networks, graph theoretic modeling, and analysis of connectome networks.

In a cell, the outcomes of any cellular activities are not the influence of any particular constituent. Instead, it is a rhythmic and collective role of multiple chemical components inside the cell. It is important to model them mathematically and computationally to derive a sketch on interdependency relationship among the cellular components to elucidate such unknown rhythmic activities within a cell. Biological networks are the graphical and mathematical modeling of such interactions or relationships. Graph theory models mathematically and computationally the pairwise relationship among different objects or entities. For this obvious reason, graph theory is considered as the best alternative for the formalism of biological network modeling and analysis. Many networks can be considered for describing various biological systems. Protein network, genetic network, DNA-protein network, signaling network, metabolic network, food web networks, neuronal networks, species interaction network, etc., are few biological networks commonly used for the study. We introduce the biological network, how to capture and model them for analysis, in this chapter.

This book presented an overview on concepts and research related to biological networks, focusing on three major application fields: gene expression networks, protein interaction networks, and brain connectome networks. We tried to make the book interesting for the early and middle stage researchers who started researches in analyzing and inferring any three of the networks.

To understand various biological networks, it is essential to appreciate the underlying topological structures or models of the network. In this chapter, we first discuss various complex network properties, followed by an introduction to different network models. We also highlight use of R packages for network analysis.

To understand biological networks, it is important to have a basic knowledge of graph theory. In this chapter, we explore the basic concepts and definitions of graphs, types, and representation of graphs, including various operations applicable to graph in general.

The influences of one gene in the activity of another gene is important, yet to identify experimentally in a wet lab environment. Hence, computational tools and methods are playing pivotal role in inferring near optimal network structures, which can further be confirmed using wet lab experimentation. The modeling (reconstruction) of a Gene Regulatory Network (GRN) based on experimental data is also called reverse engineering or network inference. Reverse-engineering, gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. We introduce here the gene-gene interaction networks and various inferencing methods for reverse engineering of such networks. We also discuss few tools or softwares available for inference and visualization of gene networks.

One of the challenges in computational biology is the analysis of the so-called interactome, i.e., the whole network composed of proteins and their interactions. Proteins are elementary building blocks of complex cellular processes; therefore the identification of all the possible protein interactions, the elucidation of protein functions, and the role of a set of related interactions (e.g., protein complexes and pathways), do appear as essential steps in describing cellular biology on a molecular basis. In this chapter, we try to highlight different methods involved in the process of protein interaction generation, aligning various protein networks for finding evolutionarily conserved protein functionality, and small subnetwork findings.

Graph analysis refers to the set of algorithms for analyzing properties of a graph. The main algorithms for graph analysis and traversal are introduced in this chapter. We start by discussing graph traversal, i.e., the problem of visiting the nodes of a graph. Many algorithms of graph analysis are based on a extension or modification of graph traversal algorithms. We then discuss the ways to find a shortest path among nodes in a graph. We also consider the compression of graphs through power graph analysis and the discovery of communities (i.e., a subgraph having some given properties). Node centrality measures, used for finding vital nodes are also discussed. We demonstrate the use of various graph traversal and analysis methods using R libraries.

Data about biological networks are the essential ingredient for data mining tasks and comparison of systems. Therefore the development of adequate storage and querying systems is a crucial challenge for bioinformatics.