Specificity and Stability in Topology of Protein Networks

By S. Maslov, K. Sneppen. Science, 2002

2,910 citations recorded by the source corpus.

Molecular networks guide the biochemistry of a living cell on multiple levels: Its metabolic and signaling pathways are shaped by the network of interacting proteins, whose production, in turn, is controlled by the genetic regulatory network. To address topological properties of these two networks, we quantified correlations between connectivities of interacting nodes and compared them to a null model of a network, in which all links were randomly rewired. We found that for both interaction and regulatory networks, links between highly connected proteins are systematically suppressed, whereas those between a highly connected and low-connected pairs of proteins are favored. This effect decreases the likelihood of cross talk between different functional modules of the cell and increases the overall robustness of a network by localizing effects of deleterious perturbations.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1b87d889c143f23132ea748559f124b52fc06cb2

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Citations in the corpus

Scaling Laws in the Functional Content of Genomes (2003, background): “This holds both when one defines ’interaction’ between genes on the level of the proteins that they encode [17, 9, 14] or if one defines it at the level of co-regulation of the expression of the genes [16].”
Identifying influential spreaders in complex networks based on gravity formula (2015, background): “In view of this, many centrality indices have been proposed to address this problem, including degree centrality [12], betweenness centrality [13], neighborhood centrality [14] and closeness 1 Corresponding author: haifengzhang1978@gmail.”
Mapping Change in Large Networks (2008, background): “Because there is no way to look at multiple samples, the most eective approach to identify prominent and nonrandom features ( 11 , 12), or to predict missing data (13, 14), is to compare the single networks to proper null models.”
Structural Insights into Aberrant Topological Patterns of Large-Scale Cortical Networks in Alzheimer's Disease (2008, background): “…studies have also demonstrated disrupted coordination of the brain morphology in various neuropsychiatric disorders, such as schizophrenia (Bullmore et al., 1998; Wright et al., 1999; Mitelman et al., 2005), autism (McAlonan et al., 2005), and obsessive-compulsive disorder (Pujol et al., 2004).”
The Structure of Plant-Animal Mutualistic Networks (2006, methodology): “For example, protein networks show a negative connectivity correlation so that highly connected proteins tend to interact with weakly connected proteins (Maslov and Sneppen 2002).”
Origins of fractality in the growth of complex networks (2005, background): “Section III reveals that the statistical properties, in particular, the degree distribution keeps invariant under renormalization for real-world networks.”
The small world of the cerebral cortex (2007, methodology): “Special kinds of random and lattice matrices that preserve the in-degree and out-degree for each vertex are generated from the original anatomical connection matrices by a Markov-chain algorithm (Maslov and Sneppen, 2002; Milo et al., 2002).”
Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex (2012, methodology): “Specifically, 1000 matched random networks were generated at each sparsity using the random rewiring procedure described by Maslov and Sneppen (2002) that preserves the degree distribution.”
Structure and dynamics of core/periphery networks (2013, methodology): “[4] introduced the first null model to detect rich-clubs using the randomization procedure of Maslov & Sneppen [69], which preserved the degree sequence of the original graph.”
Network-Level Structure-Function Relationships in Human Neocortex (2016, methodology): “This procedure was simultaneously performed for a null distribution of 1000 randomized networks with preserved degree sequences (Maslov and Sneppen 2002).”
Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits (2005, background): “In addition, probability of connection between nodes depends on how many connections they have [25].”
The Ubiquity of Small-World Networks (2011, methodology): “Random and lattice network construction To calculate σ for a given network, an equivalent random graph was created by assigning an edge to a node pair with uniform probability while maintaining the degree distribution of the original graph(Maslov and Sneppen 2002).”