CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks

An attempt to understand glioma stem cell biology through centrality analysis of a protein interaction network

Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis.

Centrality Measures and Their Applications in Network Analysis: Unveiling Important Elements and Their Impact

Coronavirus disease 2019 (COVID-19) still provides global public health issues although several vaccines and antiviral agents have been developed. Some patients experience severe conditions needed medical intensive care, and some are dead due to the failure of treatments. Therefore, identifying the key genes and underlying molecular mechanisms is necessary to discover precisely targeted drugs. Analysis of protein-protein interaction (PPI) networks provides invaluable information to find disease mechanisms and effective alternative drugs. Hence, PPI network analysis based on leukocyte transcriptomic profiles of severe COVID-19 collected from Gene Expression Omnibus (GEO) DataSets was proposed for this study. A network diffusion method called Laplacian heat diffusion (LHD) algorithm was performed to construct an immune-related PPI network (IPIN). Furthermore, several network centrality measurements can identify 23 key genes from the IPIN. Subsequently, drug-gene interaction networks were constructed using database searching based on the key genes. There were 5 candidate drugs having the potential effect of interacting with the key genes. To find additional key genes and candidate drugs, two different leukocyte transcriptomic datasets were combined for the common PPI network construction. Centrality measurement and survival analysis were used to find and validate the further key genes. The analysis revealed 4 common key genes. The drug-gene interaction and molecular docking technique provided 2 further candidate drugs that interacted with the key genes. Additionally, miRNA-mRNA regulatory networks were built based on the PPI network to recognize 5 novel biomarkers for severe COVID-19 prediction. In conclusion, PPI network analysis can discover candidate biomarkers and drugs to predict�and treat severe COVID-19 patients.�

Ranking of network elements based on functional substructures

BackgroundIn the post-genomic era, a central and overarching question in the analysis of protein-protein interaction networks continues to be whether biological characteristics and functions of proteins such as lethality, physiological malfunctions and malignancy are intimately linked to the topological role proteins play in the network as a mathematical structure. One of the key features that have implicitly been presumed is the existence of hubs, highly connected proteins considered to play a crucial role in biological networks. We explore the structure of protein interaction networks of a number of organisms as metric spaces and show that hubs are non randomly positioned and, from a distance point of view, centrally located.ResultsBy analysing how the human functional protein interaction network, the human signalling network, Saccharomyces cerevisiae, Arabidopsis thaliana and Escherichia coli protein-protein interaction networks from various databases are distributed as metric spaces, we found that proteins interact radially through a central node, high degree proteins coagulate in the centre of the network, and those far away from the centre have low degree. We further found that the distribution of proteins from the centre is in some hierarchy of importance and has biological significance.ConclusionsWe conclude that structurally, protein interaction networks are mathematical entities that share properties between organisms but not necessarily with other networks that follow power-law. We therefore conclude that (i) if there are hubs defined by degree, they are not distributed randomly; (ii) zones closest to the centre of the network are enriched for critically important proteins and are also functionally very specialised for specific 'house keeping’ functions; (iii) proteins closest to the network centre are functionally less dispensable and may present good targets for therapy development; and (iv) network biology requires its own network theory modelled on actual biological evidence and that simply adopting theories from the social sciences may be misleading.

Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools

Proteins are structural and functional components of cells. They interact with each other to drive specific cellularfunctions. The physical and functional protein interactions are an important feature of cellular organization and regulation. Protein interactions are represented as a network or a graph in which proteins are nodes, and interactions between them are edges. Perturbations in the network affecting essential or central proteins can have pathological consequences. Network or graph theory is a branch of mathematics that provides a conceptual framework to decipher topologicallyimportant proteins in the network. These concepts are known as centrality measures. This chapter introduces various centrality metrics and provides a stepwise protocol to quantify protein's strategic positions in the network using an R programming language.

Many complex systems such as biological and social systems can be modeled using graph structures called biological networks and social networks. Instead of studying separately each of the elements composing such complex systems, it is easier to study the networks representing the interactions between the elements of these systems. A commonly known fact in biological and social networks’ analysis is that in most networks some important or influential elements (e.g. essential proteins in PPI networks) are placed in some particular positions in a network. These positions (i.e. vertices) have some particular structural properties. Centrality measures quantify such facts from different points of view. Based on centrality measures the graph elements such as vertices and edges can be ranked from different points of view. Top ranked elements in the graph are supposed to play an important role in the network. This paper presents a comprehensive review of existing different centrality measures and their applications in some biological networks such as Protein-Protein interaction network, residue interaction and gene–gene interaction networks.

CentiLib is a library and plug-in for the comprehensive analysis and exploration of network centralities. It provides 17 different node centrality and four graph centrality measures in a user-friendly interface and supports the exploration of analysis results within the networks. Its architecture allows for easy adaption to Java-based network analysis, simulation and visualization tools, which is demonstrated by providing the plug-in for two popular network analysis tools-Cytoscape and Vanted. With the ability to quantitatively analyze biological networks in an interactive and visual manner, CentiLib supports a better understanding of complex biological networks and processes. Software with manual and tutorials is freely available at http://centilib.ipk-gatersleben.de/.

The analysis of protein-protein interaction networks can transfer the knowledge of well-studied biological functions to functions that are not yet adequately investigated by constructing networks and extracting similar network structures in different species. Multiple network alignment can be used to find similar regions among multiple networks. In this paper, we introduce Accurate Combined Clustering Multiple Network Alignment (ACCMNA), which is a new and accurate multiple network alignment algorithm. It uses both topology and sequence similarity information. First, the importance of all the nodes is calculated according to the network structures. Second, the seed-and-extend framework is used to conduct an iterative search. In each iteration, a clustering method is combined to generate the alignment. Extensive experimental results show that ACCMNA outperformed the state-of-the-art algorithms in producing functionally consistent and topological conservation alignments within an acceptable running time.

The theory of complex networks has a wide range of applications in a variety of disciplines such as communications and power system engineering, the internet and worldwide web (www), food webs, human social networks, molecular biology, population biology and biological networks. The focus of this paper is on biological applications of the theory of graphs and networks. Graph theory and several graph theoretic properties serve as an ideal mathematical tool in the analysis of complex networks. We present the basic concepts and notations from graph theory which is widely used in the study of biological networks. Various biological networks such as Protein interaction networks, Metabolome based reaction network, Gene regulatory network, Gene coexpression network, Protein structure network, Structural brain network, Phylogenetic networks, Ecological networks and Food web networks are described. We also deal with various centrality measures which provide deep insight in the study of biological networks. Applications of biological network analysis in several areas are also discussed.

Construction and analysis of protein-protein interaction network of non-alcoholic fatty liver disease

After pubertal, cohort of small antral follicles enters to gonadotrophin-sensitive development, called recruited follicles. This study was aimed to identify candidate genes in follicular cyclic recruitment via analysis of protein-protein interaction (PPI) network. Differentially expressed genes (DEGs) in ovine granulosa cells of small antral follicles between follicular and luteal phases were accumulated among gene/protein symbols of the Ensembl annotation. Following directed graphs, PTPN6 and FYN have the highest indegree and outdegree, respectively. Since, these hubs being up-regulated in ovine granulosa cells of small antral follicles during the follicular phase, it represents an accumulation of blood immune cells in follicular phase in comparison with luteal phase. By contrast, the up-regulated hubs in the luteal phase including CDK1, INSRR and TOP2A which stimulated DNA replication and proliferation of granulosa cells, they known as candidate genes of the cyclic recruitment.

BackgroundComparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison.ResultsIn this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow – or the long-term relative frequency of the transitions that the random walker makes – between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment.ConclusionsThrough evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID.