A Model of Large-Scale proteome Evolution

By R. V. Solé, Romualdo Pastor-Satorras, Eric Smith, Thomas B. Kepler. Advances in Complex Systems, 2002

317 citations recorded by the source corpus.

The next step in the understanding of the genome organization, after the determination of complete sequences, involves proteomics. The proteome includes the whole set of protein-protein interactions, and two recent independent studies have shown that its topology displays a number of surprising features shared by other complex networks, both natural and artificial. In order to understand the origins of this topology and its evolutionary implications, we present a simple model of proteome evolution that is able to reproduce many of the observed statistical regularities reported from the analysis of the yeast proteome. Our results suggest that the observed patterns can be explained by a process of gene duplication and diversification that would evolve proteome networks under a selection pressure, favoring robustness against failure of its individual components.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c3512907e4814d10be800d30d57c07bab44efe3a

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Citations in the corpus

Generative models of the human connectome (2015, unknown): “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…”
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.”
Selection, Tinkering, and Emergence in Complex Networks - Crossing the Land of Tinkering (2002, methodology): “This type of random model has been used in diﬁerent contexts, including ecological (May, 1976; Cohen 1978; Sol¶e et al. , 2002), genetic and metabolic (Kauﬁman, 1962; Kauﬁman, 1993) and neural (Hertz et al. , 1991; Sompolinsky et al. , 1988) networks.”
Boolean dynamics of networks with scale-free topology (2003, background): “5shows the histogram of 30 different scale-free exponents reported in [1,20,25]for a wide collection of scale-free networks (not too many more networks have been analyzed so far).”
Graph theory and networks in Biology. (2006, background): “For instance, point (i) above has been addressed in [93], while a model that allows for edge additions and removals at a much faster rate than gene duplications has been described and analysed in [94].”
Does Selection Mold Molecular Networks? (2003, background): “Second, for protein interaction networks, turnover of individual protein interactions on evolutionary time scales, without natural selection shaping the network’s global structure, is sufficient to explain the network’s structure (9, 10 ).”
Information Theory of Complex Networks: On Evolution and Architectural Constraints (2004, background): “Scale-free nets have been shown to be obtainable through a number of mechanisms, including preferential attachment [2,3,7,8], optimization [9,10], duplication and divergence [11,12] or fitness-dependent, rich-gets-richer mechanisms [13] or the “copying” model [14].”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, methodology): “proposed a similar model with the same concepts [370], which is given in detail in [371].”
Discriminative topological features reveal biological network mechanisms (2004, methodology): “Among the six growth models two are based on preferential attachment [16][7], three on a duplication-mutation mechanism [17][18], and one on purely random growth [19].”