단백질 상호작용 네트워크의 삼각형 기반 변 점수 산정법

학위논문 (석사)-- 서울대학교 대학원 사범대학 수학교육과, 2017. 8. 김서령.Motivation: Uncovering the mystery of evolutionary mechanism of protein interaction networks has been actively conducted in order to understand interactions of proteins that induce biological processes in organisms. There have been many attempts to solve the mystery by proposing evolutionary models of protein interaction networks. Topological properties of protein interaction networks are mentioned several times and given an important role in these attempts since a validation of suggested models is made through topological properties of known protein interaction networks. While one group of researchers have made efforts to generate current protein interaction networks from some hypothetical infant state of protein interaction networks through suggested evolutionary models, another group of researchers have made efforts to estimate the phylogenetic age of proteins from evolutionary relationships. Recently, these efforts gave rise to the database of phylogenetic age of proteins and this allows many researchers to estimate ages of proteins in their interest easily. Recent studies on Mendelian diseases and cancer suggested that proteins associated with specific diseases populate certain category of the phylogenetic age of proteins. The fact that the topological properties of the protein interaction network have played important roles in the evolution of protein interaction networks tells us that topological properties of protein interaction network and properties of proteins, which is related to the evolution of the protein interaction network, is closely related in some level. As one can see from closeness in terms, the evolutionary model of protein interaction networks and phylogenetic age of proteins are closely related and thus topological properties of protein interactions, which is important in studies of the evolutionary models, can be used to estimate the phylogenetic age of proteins. Besides, the research results on the relationship between diseases and phylogenetic age of proteins motivate us to predict proteins associated to diseases by utilizing topological properties of protein interaction networks. Results: We construct a weighted human protein interaction network from a human protein interaction network which is provided via BioGRID database. The weight of an edge is defined as the number of triangles which contains this edge in the protein interaction network and thus we call this weight as the triangle score. We make comparison between the edge scores of a human protein interaction network given by STRING database and the triangle score. In an attempt to find relationship between the triangle score and properties of proteins that is related to the evolution of protein interaction networks, we make comparison between the triangle score and bit score, which is a measurement of protein sequence similarity. Moreover, we attempt to sieve out self-interacting proteins from the whole human proteins based on the triangle score. In an effort to predict the phylogenetic age of proteins based on the triangle score, firstly, we extract proteins that are incident on an edge that has a high triangle score from the weighted protein interaction network which we constructed with the triangle score. After the extraction, we make inquiries to the ProteinHistorian database to get phylogenetic ages of extracted proteins. Finally, we show that there is a relationship between triangle score and phylogenetic age by comparing the ratio of proteins with each phylogenetic age to whole human proteins and the ratio of extracted proteins with each phylogenetic age to whole extracted proteins. Based on the triangle score, we also attempt to predict disease associated proteins for several diseases. The fact that the topological properties of the protein interaction network have played important roles in the evolution of protein interaction networks tells us that topological properties of protein interaction network and properties of proteins, which is related to the evolution of the protein interaction network, is closely related in some level. As one can see from closeness in terms, the evolutionary model of protein interaction networks and phylogenetic age of proteins are closely related and topological properties of protein interactions, which is important in studies of the evolutionary models, can be used to estimate the phylogenetic age of proteins. Besides, the research results on the relationship between diseases and phylogenetic age of proteins motivate us to predict proteins associated to diseases by utilizing topological properties of protein interaction networks. Results: We construct a weighted human protein interaction network from a human protein interaction network which is provided via BioGrid database. The weight of an edge is defined as the number of triangles which contains this edge in the protein interaction network and thus we call this weight as the triangle score. We make comparison between the edge scores of a human protein interaction network given by STRING database and the triangle score. In an attempt to find relationship between the triangle score and properties of proteins that is related to the evolution of protein interaction networks, we make comparison between the triangle score and bit score, which is a measurement of protein sequence similarity. Moreover, we attempt to sieve out self-interacting proteins from the whole human proteins based on the triangle score. In an effort to predict the phylogenetic age of proteins based on the triangle score, firstly, we extract proteins that are incident on an edge that has a high triangle score from the weighted protein interaction network which we constructed with the triangle score. After the extraction, we make inquiries to the ProteinHistorian database to get phylogenetic ages of extracted proteins. Finally, we show that there is a relationship between triangle score and phylogenetic age by comparing the ratio of proteins with each phylogenetic age to whole human proteins and the ratio of extracted proteins with each phylogenetic age to whole extracted proteins. Based on the triangle score, we also attempt to predict disease associated proteins for several diseases.제 1 장 Introduction 1 제 2 장 Materials and Methods 11 제 3 장 Results 40 제 4 장 Conclusions 55 Bibliography 59 국문초록 63Maste

Detecting rich-club ordering in complex networks

Uncovering the hidden regularities and organizational principles of networks arising in physical systems ranging from the molecular level to the scale of large communication infrastructures is the key issue for the understanding of their fabric and dynamical properties [1-5]. The ``rich-club'' phenomenon refers to the tendency of nodes with high centrality, the dominant elements of the system, to form tightly interconnected communities and it is one of the crucial properties accounting for the formation of dominant communities in both computer and social sciences [4-8]. Here we provide the analytical expression and the correct null models which allow for a quantitative discussion of the rich-club phenomenon. The presented analysis enables the measurement of the rich-club ordering and its relation with the function and dynamics of networks in examples drawn from the biological, social and technological domains.Comment: 1 table, 3 figure

Hierarchical characterization of complex networks

While the majority of approaches to the characterization of complex networks has relied on measurements considering only the immediate neighborhood of each network node, valuable information about the network topological properties can be obtained by considering further neighborhoods. The current work discusses on how the concepts of hierarchical node degree and hierarchical clustering coefficient (introduced in cond-mat/0408076), complemented by new hierarchical measurements, can be used in order to obtain a powerful set of topological features of complex networks. The interpretation of such measurements is discussed, including an analytical study of the hierarchical node degree for random networks, and the potential of the suggested measurements for the characterization of complex networks is illustrated with respect to simulations of random, scale-free and regular network models as well as real data (airports, proteins and word associations). The enhanced characterization of the connectivity provided by the set of hierarchical measurements also allows the use of agglomerative clustering methods in order to obtain taxonomies of relationships between nodes in a network, a possibility which is also illustrated in the current article.Comment: 19 pages, 23 figure

Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology

Abstract Background In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN). Results The RNN topology is a weighted directed graph derived from PPI network, and it is a natural representation of the topological dependences between proteins within the PPI network. Similar to the original PPI network, we have observed that essential proteins tend to be hub proteins in RNN topology. Additionally, essential genes are enriched in clusters containing many hub proteins in RNN topology (RNN protein clusters). Based on these two properties of essential genes in RNN topology, we have proposed a new measure; the RNN cluster centrality. Results from a variety of PPI networks demonstrate that RNN cluster centrality outperforms other centrality measures with regard to the proportion of selected proteins that are essential proteins. We also investigated the biological importance of RNN clusters. Conclusions This study reveals that RNN cluster centrality provides the best correlation of protein essentiality and placement of proteins in PPI network. Additionally, merged RNN clusters were found to be topologically important in that essential proteins are significantly enriched in RNN clusters, and biologically important because they play an important role in many Gene Ontology (GO) processes.http://deepblue.lib.umich.edu/bitstream/2027.42/78257/1/1471-2105-11-505.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/2/1471-2105-11-505-S1.DOChttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/3/1471-2105-11-505.pdfPeer Reviewe

Characterization of complex networks: A survey of measurements

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements.Comment: A working manuscript with 78 pages, 32 figures. Suggestions of measurements for inclusion are welcomed by the author