Essential protein plays a crucial role in the process of cell life. The identification of essential proteins can not only promote the development of drug target technology, but also contribute to the mechanism of biological evolution. There are plenty of scholars who pay attention to discovering essential proteins according to the topological structure of protein network and biological information. The accuracy of protein recognition still demands to be improved. In this paper, we propose a method which integrate the clustering coefficient in protein complexes and topological properties to determine the essentiality of proteins. First, we give the definition of In-clustering coefficient (IC) to describe the properties of protein complexes. Then we propose a new method, complex edge and node clustering coefficient (CENC) to identify essential proteins. Different Protein-Protein Interaction (PPI) networks of Saccharomyces cerevisiae, MIPS and DIP are used as experimental materials. Through some experiments of logistic regression model, the results show that the method of CENC can promote the ability of recognizing essential proteins, by comparing with the existing methods DC, BC, EC, SC, LAC, NC and the recent method UC.
Topological analysis of large scale protein-protein interaction networks (PINs) is important for understanding the organisational and functional principles of individual proteins. The number of interactions that a protein has in a PIN has been observed to be correlated with its indispensability. Essential proteins generally have more interactions than the non-essential ones. We show here that the lethality associated with removal of a protein from the yeast proteome correlates with different centrality measures of the nodes in the PIN, such as the closeness of a protein to many other proteins,…
In this work, we propose a novel centrality metric, referred to as star centrality, which incorporates information from the closed neighborhood of a node, rather than solely from the node itself, when calculating its topological importance. More specifically, we focus on degree centrality and show that in the complex protein-protein interaction networks it is a naive metric that can lead to misclassifying protein importance. For our extension of degree centrality when considering stars, we derive its computational complexity, provide a mathematical formulation, and propose two approximation al…
The recognition of essential proteins not only can help to understand the mechanism of cell operation, but also help to study the mechanism of biological evolution. At present, many scholars have been discovering essential proteins according to the topological structure of protein network and complexes. While some proteins still can not be recognized. In this paper, we proposed two new methods complex degree centrality (CDC) and complex in-degree and betweenness definition (CIBD) which integrate the local character of protein complexes and topological properties to determine the essentiality o…
Cooperation played a significant role in the self-organization and evolution of living organisms. Both network topology and the initial position of cooperators heavily affect the cooperation of social dilemma games. We developed a novel simulation program package, called 'NetworGame', which is able to simulate any type of social dilemma games on any model, or real world networks with any assignment of initial cooperation or defection strategies to network nodes. The ability of initially defecting single nodes to break overall cooperation was called as 'game centrality'. The efficiency of this …
In this paper, we compared cancerous and normal cell according to their protein-protein interaction network. Cancer is one of the complicated diseases and experimental investigations have been showed that protein interactions have an important role in the growth of cancer. We calculated some graph related parameters such as Number of Vertices, Number of Edges, Closeness, Graph Diameter, Graph Radius, Index of Aggregation, Connectivity, Number of Edges divided by the Number of Vertices, Degree, Cluster Coefficient, Subgraph Centrality, and Betweenness. Furthermore, the number of motifs and hubs…
Cellular networks undergo rearrangements during stress and diseases. In un-stressed state the yeast protein-protein interaction network (interactome) is highly compact, and the centrally organized modules have a large overlap. During stress several original modules became more separated, and a number of novel modules also appear. A few basic functions, such as the proteasome preserve their central position. However, several functions with high energy demand, such the cell-cycle regulation loose their original centrality during stress. A number of key stress-dependent protein complexes, such as…
We discuss in this survey several network modeling methods and their applicability to precision medicine. We review several network centrality methods (degree centrality, closeness centrality, eccentricity centrality, betweenness centrality, and eigenvector-based prestige) and two systems controllability methods (minimum dominating sets and network structural controllability). We demonstrate their applicability to precision medicine on three multiple myeloma patient disease networks. Each network consists of protein-protein interactions built around a specific patient's mutated genes, around t…
Protein complexes conserved across species indicate processes that are core to cellular machinery (e.g. cell-cycle or DNA damage-repair complexes conserved across human and yeast). While numerous computational methods have been devised to identify complexes from the protein interaction (PPI) networks of individual species, these are severely limited by noise and errors (false positives) in currently available datasets. Our analysis using human and yeast PPI networks revealed that these methods missed several important complexes including those conserved between the two species (e.g. the MLH1-M…
Analysis of network dynamics became a focal point to understand and predict changes of complex systems. Here we introduce Turbine, a generic framework enabling fast simulation of any algorithmically definable dynamics on very large networks. Using a perturbation transmission model inspired by communicating vessels, we define a novel centrality measure: perturbation centrality. Hubs and inter-modular nodes proved to be highly efficient in perturbation propagation. High perturbation centrality nodes of the Met-tRNA synthetase protein structure network were identified as amino acids involved in i…
Most amino acids are encoded by multiple synonymous codons. For an amino acid, some of its synonymous codons are used much more rarely than others. Analyses of positions of such rare codons in protein sequences revealed that rare codons can impact co-translational protein folding and that positions of some rare codons are evolutionary conserved. Analyses of positions of rare codons in proteins' 3-dimensional structures, which are richer in biochemical information than sequences alone, might further explain the role of rare codons in protein folding. We analyze a protein set recently annotated …
Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human prot…
Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges fac…
Self-avoiding random walks were performed on protein residue networks. Compared with protein residue networks with randomized links, the probability of a walk being successful is lower and the length of successful walks shorter in (non-randomized) protein residue networks. Fewer successful walks and shorter successful walks point to higher communication specificity between protein residues, a conceivably favourable attribute for proteins to have. The use of random walks instead of shortest paths also produced lower node centrality, lower edge betweeness and lower edge load for (non-randomized)…
We construct a gene network based on expression data from DNA microarray experiments, by establishing a link between two genes whenever the Pearson's correlation coefficient between their expression profiles is higher than a certain cutoff. The resulting connectivity distribution is compatible with a power-law decay with exponent ~1, corrected by an exponential cutoff at large connectivity. The biological relevance of such network is demonstrated by showing that there is a strong statistical correlation between high connectivity number and lethality: in close analogy to what happens for protei…
Native shortcut networks (SCN0) are sub-graphs of native protein residue networks (PRN0). In this paper, we propose the Network Dynamics (ND) model, which reconstructs a PRN0 by adding back its edges according to some recipe, while its nodes are fixed at native (PDB) locations. A PRN0 reconstruction will eventually reconstruct its SCN0, but only after exploring several non-native shortcut network (SCN) configurations. It is these other SCN configurations that are of interest to us, as they produce the statistics to evaluate the different recipes. The recipes vary from each other slightly to in…
The architecture of the network of protein-protein physical interactions in Saccharomyces cerevisiae is exposed through the combination of two complementary theoretical network measures, betweenness centrality and `Q-modularity'. The yeast interactome is characterized by well-defined topological modules connected via a small number of inter-module protein interactions. Should such topological inter-module connections turn out to constitute a form of functional coordination between the modules, we speculate that this coordination is occurring typically in a pair-wise fashion, rather than by way…
Inference of new biological knowledge, e.g., prediction of protein function, from protein-protein interaction (PPI) networks has received attention in the post-genomic era. A popular strategy has been to cluster the network into functionally coherent groups of proteins and predict protein function from the clusters. Traditionally, network research has focused on clustering of nodes. However, why favor nodes over edges, when clustering of edges may be preferred? For example, nodes belong to multiple functional groups, but clustering of nodes typically cannot capture the group overlap, while clu…
Summary: The ModuLand plug-in provides Cytoscape users an algorithm for determining extensively overlapping network modules. Moreover, it identifies several hierarchical layers of modules, where meta-nodes of the higher hierarchical layer represent modules of the lower layer. The tool assigns module cores, which predict the function of the whole module, and determines key nodes bridging two or multiple modules. The plug-in has a detailed JAVA-based graphical interface with various colouring options. The ModuLand tool can run on Windows, Linux, or Mac OS. We demonstrate its use on protein struc…
By integrating heterogeneous functional genomic datasets, we have developed a new framework for detecting combinatorial control of gene expression, which includes estimating transcription factor activities using a singular value decomposition method and reducing high-dimensional input gene space by considering genomic properties of gene clusters. The prediction of cooperative gene regulation is accomplished by either Gaussian Graphical Models or Pairwise Mixed Graphical Models. The proposed framework was tested on yeast cell cycle datasets: (1) 54 known yeast cell cycle genes with 9 cell cycle…
The idea of 'date' and 'party' hubs has been influential in the study of protein-protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub…
Identifying Protein Co-regulatory Network Logic by Solving B-SAT Problems through Gate-based Quantum Computing
Vaccine and Infectious Disease Organization (VIDO) University of Saskatchewan（萨斯喀彻温大学疫苗与传染病组织）；Engineering Science, Faculty of Applied Science University of Toronto（多伦多大学应用科学学院工程学）；Centre for Quantum Topology and Its Applications (quanTA) Department of Mathematics and Statistics University of Saskatchewan（萨斯喀彻温大学数学与统计学院量子拓扑及其应用中心）
Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximise its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network.…
