Modeling of Protein Interaction Networks

By A. Vázquez, Alessandro Flammini, Amos Maritan, Alessandro Vespignani. ComPlexUs, 2001

430 citations recorded by the source corpus.

We introduce a graph-generating model aimed at representing the evolution of protein interaction networks. The model is based on the hypothesis of evolution by duplication and divergence of the genes which produce proteins. The obtained graphs have multifractal properties recovering the absence of a characteristic connectivity as found in real data of protein interaction networks. The error tolerance of the model to random or targeted damage is in very good agreement with the behavior obtained in real protein network analyses. The proposed model is a first step in the identification of the evolutionary dynamics leading to the development of protein functions and interactions.

Subfields: Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3cc88ae75a4233f42f45277c3348354219deab1a

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In Statistical and Nonlinear Physics

Citations in the corpus

High-Betweenness Proteins in the Yeast Protein Interaction Network (2005, methodology): “A biologically motivated model put forward by Sole et al [19] and Vazquez et al [20] incorporated “gene duplication” as the driving mechanism for genome growth.”
Scale-free networks in cell biology (2005, neutral): “(Kim et al., 2002; Pastor-Satorras et al., 2003; Vazquez et al., 2003 ).”
Human disease classification in the postgenomic era: A complex systems approach to human pathobiology (2007, background): “…et al, 2001), and is also affected by alternate splicing and posttranslational modification in protein networks (Qian et al, 2001; Bhan et al, 2002; Pastor-Satorras et al, 2003; Vazquez et al, 2003), as well as the variable chemical versatility of the metabolic intermediates in metabolic networks.”
The Structure and Function of Complex Networks (2003, background): “Models of protein networks that make use of copying mechanisms have been proposed by a number of authors [49, 232, 376, 398].”
Generative models of the human connectome (2015, neutral): “One example among many (Watts and Strogatz, 1998; Kumar et al., 2000; Solé et al., 2002; Vázquez et al., 2003; Dall and Christensen, 2002; Middendorf et al., 2005) is the preferential attachment model (Barabási and Albert, 1999), which generates synthetic networks with heavy-tailed degree…”
Bipartite Graphs as Models of Complex Networks (2003, neutral): “More recent models [9, 59, 60, 61, 65, 66, 69] have been introduced.”
Discriminative topological features reveal biological network mechanisms (2004, methodology): “[17] Klemm Growing graph using sets of active and inactive nodes to model citation networks.”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “[369] proposed a model in which each node in the network represents a protein that is expressed by a gene, and the network evolves following two steps: (i) duplication: a randomly selected node i is duplicated as i which is connected to i according to a probability p and has all connections of i, (i”
Graph theory and networks in Biology. (2006, background): “The idea behind using degree centrality as a measure of importance in network is the following:”
Learning the Structural Vocabulary of a Network (2017, methodology): “Both of these features are expected to emerge from the duplication process, where two paralogous genes often share common interaction partners (Vazquez et al., 2003).”