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相关论文: Structure and evolution of protein interaction net…

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Two processes can influence the evolution of protein interaction networks: addition and elimination of interactions between proteins, and gene duplications increasing the number of proteins and interactions. The rates of these processes can…

统计力学 · 物理学 2007-05-23 A. Wagner

Protein interaction networks aim to summarize the complex interplay of proteins in an organism. Early studies suggested that the position of a protein in the network determines its evolutionary rate but there has been considerable…

分子网络 · 定量生物学 2007-05-23 Ino Agrafioti , Jonathan Swire , James Abbott , Derek Huntley , Sarah Butcher , Michael P. H. Stumpf

The Saccharomyces cerevisiae protein-protein interaction map, as well as many natural and man-made networks, shares the scale-free topology. The preferential attachment model was suggested as a generic network evolution model that yields…

统计力学 · 物理学 2007-05-23 Eli Eisenberg , Erez Y. Levanon

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…

分子网络 · 定量生物学 2013-03-27 Yuliang Jin , Dmitrij Turaev , Thomas Weinmaier , Thomas Rattei , Hernan A. Makse

We introduce a graph generating model aimed at representing the evolution of protein interaction networks. The model is based on the hypotesis of evolution by duplications and divergence of the genes which produce proteins. The obtained…

统计力学 · 物理学 2007-05-23 A. Vazquez , A. Flammini , A. Maritan , A. Vespignani

We show that the protein-protein interaction networks can be surprisingly well described by a very simple evolution model of duplication and divergence. The model exhibits a remarkably rich behavior depending on a single parameter, the…

分子网络 · 定量生物学 2009-11-10 I. Ispolatov , P. L. Krapivsky , A. Yuryev

We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate…

分子网络 · 定量生物学 2015-01-07 Jack Peterson , Steve Presse , Kristin S. Peterson , Ken A. Dill

The evolution processes of complex systems carry key information in the systems' functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be…

物理与社会 · 物理学 2024-03-25 Junya Wang , Yi-Jiao Zhang , Cong Xu , Jiaze Li , Jiachen Sun , Jiarong Xie , Ling Feng , Tianshou Zhou , Yanqing Hu

Genomic duplication-divergence events, which are the primary source of new protein functions, occur stochastically at a wide range of genomic scales, from single gene to whole genome duplications. Clearly, this fundamental evolutionary…

分子网络 · 定量生物学 2007-05-23 Kirill Evlampiev , Herve Isambert

Successive whole genome duplications have recently been firmly established in all major eukaryote kingdoms. It is not clear, however, how such dramatic evolutionary process has contributed to shape the large scale topology of…

分子网络 · 定量生物学 2007-05-23 K. Evlampiev , H. Isambert

A fundamental question for evolutionary biology is why rates of evolution vary dramatically between proteins. Perhaps surprisingly, it is controversial how much a protein's functional importance affects its rate of evolution. In most…

种群与进化 · 定量生物学 2009-09-20 Ryan N. Gutenkunst

The study of human interactions is of central importance for understanding the behavior of individuals, groups and societies. Here, we observe the formation and evolution of networks by monitoring the addition of all new links and we…

物理与社会 · 物理学 2013-02-01 Lazaros K. Gallos , Diego Rybski , Fredrik Liljeros , Shlomo Havlin , Hernan A. Makse

Proteins in organisms, rather than act alone, usually form protein complexes to perform cellular functions. We analyze the topological network structure of protein complexes and their component proteins in the budding yeast in terms of the…

定量方法 · 定量生物学 2011-08-16 Sang Hoon Lee , Pan-Jun Kim , Hawoong Jeong

In this paper, we consider the statistical analysis of a protein interaction network. We propose a Bayesian model that uses a hierarchy of probabilistic assumptions about the way proteins interact with one another in order to: (i) identify…

分子网络 · 定量生物学 2007-11-15 Edoardo M Airoldi , David M Blei , Stephen E Fienberg , Eric P Xing

It is well-known that population structure is a catalyst for the evolution of cooperation since individuals can reciprocate with their neighbors through local interactions defined by network structures. Previous research typically relies on…

物理与社会 · 物理学 2021-12-16 Anzhi Sheng , Aming Li , Long Wang

The primary structure of proteins, that is their sequence, represents one of the most abundant set of experimental data concerning biomolecules. The study of correlations in families of co--evolving proteins by means of an inverse…

生物大分子 · 定量生物学 2015-06-16 Sara Lui , Guido Tiana

Evolving biomolecular networks have to combine the stability against perturbations with flexibility allowing their constituents to assume new roles in the cell. Gene duplication followed by functional divergence of associated proteins is a…

分子网络 · 定量生物学 2007-05-23 Sergei Maslov , Kim Sneppen , Kasper Astrup Eriksen

Recently described stochastic models of protein evolution have demonstrated that the inclusion of structural information in addition to amino acid sequences leads to a more reliable estimation of evolutionary parameters. We present a…

In this thesis, we have studied the large scale structure and system level dynamics of certain biological networks using tools from graph theory, computational biology and dynamical systems. We study the structure and dynamics of large…

分子网络 · 定量生物学 2008-12-31 Areejit Samal

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions and structural biology has provided detailed functional insight for select 3D protein…

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