Functional organization and its implication in evolution of the human protein-protein interaction network

<p>Abstract</p> <p>Background</p> <p>Based on the distinguishing properties of protein-protein interaction networks such as power-law degree distribution and modularity structure, several stochastic models for the evolution of these networks have been purposed, motivated by the idea that a validated model should reproduce similar topological properties of the empirical network. However, being able to capture topological properties does not necessarily mean it correctly reproduces how networks emerge and evolve. More importantly, there is already evidence suggesting functional organization and significance of these networks. The current stochastic models of evolution, however, grow the network without consideration for biological function and natural selection.</p> <p>Results</p> <p>To test whether protein interaction networks are functionally organized and their impacts on the evolution of these networks, we analyzed their evolution at both the topological and functional level. We find that the human network is shown to be functionally organized, and its function evolves with the topological properties of the network. Our analysis suggests that function most likely affects local modularity of the network. Consistently, we further found that the topological unit is also the functional unit of the network.</p> <p>Conclusion</p> <p>We have demonstrated functional organization of a protein interaction network. Given our observations, we suggest that its significance should not be overlooked when studying network evolution.</p

The evolutionary dynamics of protein-protein interaction networks inferred from the reconstruction of ancient networks

Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today's PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI network structure and dynamics

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

Improving evolutionary models of protein interaction networks

Motivation: Theoretical models of biological networks are valuable tools in evolutionary inference. Theoretical models based on gene duplication and divergence provide biologically plausible evolutionary mechanics. Similarities found between empirical networks and their theoretically generated counterpart are considered evidence of the role modeled mechanics play in biological evolution. However, the method by which these models are parameterized can lead to questions about the validity of the inferences. Selecting parameter values in order to produce a particular topological value obfuscates the possibility that the model may produce a similar topology for a large range of parameter values. Alternately, a model may produce a large range of topologies, allowing (incorrect) parameter values to produce a valid topology from an otherwise flawed model. In order to lend biological credence to the modeled evolutionary mechanics, parameter values should be derived from the empirical data. Furthermore, recent work indicates that the timing and fate of gene duplications are critical to proper derivation of these parameters