Network models in bioinformatics: modeling and analysis for complex diseases.

Abstract

Networks are present in different aspects of our life: communication networks, World Wide Web, Social Networks, and can be used to conveniently describe biological and clinical data, such as the interactions of proteins in an organism or the connections of neurons in the brain. Therefore, network science, focusing on the network representations of physical, biological and social phenomena and leading to predictive models of these phenomena, currently represents a vast field of application and research for many scientific and social disciplines. The mathematical background for the study and analysis of networks has its roots in the theory of graphs that allows studying real phenomena in a quantitative way. According to the formalism coming from graph theory, nodes of the graph represent entities, whereas edges represent the associations among them. Currently, in bioinformatics and systems biology, there is a growing interest in analyzing associations among biological molecules at a network level. Since the study of associations in a system-level scale has shown great potential, the use of networks has become the de facto standard for representing such associations, and its application fields span from molecular biology to brain connectome analysis [1]. Molecules of different types, e.g. genes, proteins, ribonucleic acids and metabolites, have fundamental roles in the mechanisms of the cellular processes. The study of their structure and interactions is crucial for different reasons, comprising the development of new drugs and the discovery of disease pathways. Thus, the modeling of the complete set of interactions and associations among biological molecules as a graph is convenient for a variety of reasons. Networks provide a simple and intuitive representation of heterogeneous and complex biological processes. Moreover, they facilitate modeling and understanding of complicated molecular mechanisms combining graph theory, machine learning and deep learning techniques. While proteomics and genomics data, represented as data streams or data tables, are mainly used to screen large populations in case–control studies (e.g. for early detection of diseases), interactomics data are represented as graphs and they add a new dimension of analysis, allowing, for instance, the graph-based comparison of organism’s properties. In general, complex biological systems represented as networks provide an integrated way to look into the dynamic behavior of the cellular system through the interactions of components. For instance, biological networks also referred to as Protein–Protein Interaction Networks, model biochemical interactions among proteins. Nodes represent the proteins from a given organism, and the edges represent the protein–protein interactions [2]. Also, gene regulatory network (GRN) is a collection of genes in a cell, which interact each other and with other substances in the cell, such as proteins or metabolites, thereby governing the rates at which genes in the network are transcribed into mRNA. Similarly, the graph-based modeling of the whole system of the brain elements and their relations, so-called brain connectome, is based on the representation of the regions of interest as nodes, and the representation of functional or anatomical connections as edges [3]. Furthermore, recent discoveries in biology have elucidated that the interplay of molecules of different types (e.g. genes, proteins and ribonucleic acids) is a constitutive block of mechanisms inside cells. Consequently, models describing the interplay should be able to consider the presence of multiple different agents and associations, i.e. multiple different types of nodes and edges, that yield to the so-called heterogeneous networks [4].