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Topological network motifs represent functional relationships within and between regulatory and protein-protein interaction networks. Enriched motifs often aggregate into self-contained units forming functional modules. Theoretical models…

分子网络 · 定量生物学 2011-09-12 Tom Michoel , Anagha Joshi , Bruno Nachtergaele , Yves Van de Peer

We present a method that compares the protein interaction networks of two species to detect functionally similar (conserved) protein modules between them. The method is based on an algorithm we developed to identify matching subgraphs…

分子网络 · 定量生物学 2007-05-23 Manikandan Narayanan , Richard M. Karp

Motivation: Protein interactions are fundamental building blocks of biochemical reaction systems underlying cellular functions. The complexity and functionality of such systems emerge not from the protein interactions themselves but from…

分子网络 · 定量生物学 2011-06-15 Johannes Köster , Eli Zamir , Sven Rahmann

A major issue in biology is the understanding of the interactions between proteins. These interactions can be described by a network, where the proteins are modeled by nodes and the interactions by edges. The origin of these protein…

生物物理 · 物理学 2011-08-01 Christian M. Schneider , Lucilla de Arcangelis , Hans J. Herrmann

Understanding of how protein interaction networks (PIN) of living organisms have evolved or are organized can be the first stepping stone in unveiling how life works on a fundamental ground. Here we introduce a hybrid network model composed…

分子网络 · 定量生物学 2007-05-23 K. -I. Goh , B. Kahng , D. Kim

The structure and dynamics of a typical biological system are complex due to strong and inhomogeneous interactions between its constituents. The investigation of such systems with classical mathematical tools, such as differential equations…

分子网络 · 定量生物学 2008-02-15 Murat Tuğrul

How do living cells achieve sufficient abundances of functional protein complexes while minimizing promiscuous non-functional interactions? Here we study this problem using a first-principle model of the cell whose phenotypic traits are…

生物大分子 · 定量生物学 2011-01-04 Muyoung Heo , Sergei Maslov , Eugene I. Shakhnovich

Genetic interactions have been widely used to define functional relationships between proteins and pathways. In this study, we demonstrated that yeast synthetic lethal genetic interactions can be explained by the genetic interactions…

分子网络 · 定量生物学 2011-04-25 Bo Li , Weiguo Cao , Jizhong Zhou , Feng Luo

In this paper, we study the large-scale protein interaction network of yeast uti lizing a stochastic method based upon percolation of random graphs. In order to find the global features of connectivities in the network, we introduce numeric…

统计力学 · 物理学 2007-05-23 Chen-Shan Chin , Manoj Pratim Samanta

Recent developments in complex networks have paved the way to a series of important biological insights, such as the fact that many of the essential proteins of S. cerevisae corresponds to the so-called hubs of the respective…

分子网络 · 定量生物学 2007-05-23 Luciano da Fontoura Costa

We explore the interplay between the protein-protein interactions network and the expression of the interacting proteins. It is shown that interacting proteins are expressed in significantly more similar cellular concentrations. This is…

分子网络 · 定量生物学 2007-05-23 Shai Carmi , Erez Y. Levanon , Shlomo Havlin , Eli Eisenberg

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…

分子网络 · 定量生物学 2008-02-23 Robin Palotai , Mate S. Szalay , Peter Csermely

We propose a general method to predict functions of vertices where: 1. The wiring of the network is somehow related to the vertex functionality. 2. A fraction of the vertices are functionally classified. The method is influenced by…

分子网络 · 定量生物学 2007-05-23 Petter Holme , Mikael Huss

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

Protein interaction networks (PIN) are popular means to visualize the proteome. However, PIN datasets are known to be noisy, incomplete and biased by the experimental protocols used to detect protein interactions. This paper aims at…

无序系统与神经网络 · 物理学 2015-01-06 Alessia Annibale , Anthony C. C. Coolen , Nuria Planell-Morell

We present a simple model for the underlying structure of protein-protein pairwise interaction graphs that is based on the way in which proteins attach to each other in experiments such as yeast two-hybrid assays. We show that data on the…

分子网络 · 定量生物学 2007-05-23 Alun Thomas , Rob Cannings , Nicholas A. M. Monk , Chris Cannings

Gene regulatory networks constitute the first layer of the cellular computation for cell adaptation and surveillance. In these webs, a set of causal relations is built up from thousands of interactions between transcription factors and…

分子网络 · 定量生物学 2009-11-30 Carlos Rodriguez-Caso , Bernat Corominas-Murtra , Ricard V. Solé

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 structure of molecular networks derives from dynamical processes on evolutionary time scales. For protein interaction networks, global statistical features of their structure can now be inferred consistently from several…

统计力学 · 物理学 2007-05-23 Johannes Berg , Michael Lässig , Andreas Wagner

Background:Typically, proteins perform key biological functions by interacting with each other. As a consequence, predicting which protein pairs interact is a fundamental problem. Experimental methods are slow, expensive, and may be error…

生物大分子 · 定量生物学 2022-02-08 Leonardo Martini , Adriano Fazzone , Luca Becchetti