Modeling interactome: scale-free or geometric?

By Natasa Przulj, Derek Gordon Corneil, Igor Jurisica. 2004

711 citations recorded by the source corpus.

MOTIVATION Networks have been used to model many real-world phenomena to better understand the phenomena and to guide experiments in order to predict their behavior. Since incorrect models lead to incorrect predictions, it is vital to have as accurate a model as possible. As a result, new techniques and models for analyzing and modeling real-world networks have recently been introduced. RESULTS One example of large and complex networks involves protein-protein interaction (PPI) networks. We analyze PPI networks of yeast Saccharomyces cerevisiae and fruitfly Drosophila melanogaster using a newly introduced measure of local network structure as well as the standardly used measures of global network structure. We examine the fit of four different network models, including Erdos-Renyi, scale-free and geometric random network models, to these PPI networks with respect to the measures of local and global network structure. We demonstrate that the currently accepted scale-free model of PPI networks fails to fit the data in several respects and show that a random geometric model provides a much more accurate model of the PPI data. We hypothesize that only the noise in these networks is scale-free. CONCLUSIONS We systematically evaluate how well-different network models fit the PPI networks. We show that the structure of PPI networks is better modeled by a geometric random graph than by a scale-free model. SUPPLEMENTARY INFORMATION Supplementary information is available at http://www.cs.utoronto.ca/~juris/data/data/ppiGRG04/

Subfields: Experimental and Cognitive Psychology, Molecular Biology, Statistical and Nonlinear Physics

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Citations in the corpus

Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “[332], on the other hand, suggested that the protein evolution is governed by a geometrical model and the scale-f ree degree distribution may be caused by the high percentage of false negatives.”
Protein-to-protein interactions: Technologies, databases, and algorithms (2010, methodology): “The extraction of global properties aimed to represent PPI networks by using models used in yet other .elds, such as random graphs or scale-free networks [Albert 2005; Erdos and Renyi 1960; Przulj et al. 2004].”
Graph theory and networks in Biology. (2006, background): “clusions about the structure of biological and other real world networks. Before moving on to discuss a number of more biologically motivated models for interaction networks, we note the recent paper [148] in which geometric random graphs [145] weresuggested asan alternative model for protein intera”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, background): “Towards this goal, for each snapshot they compute features for each pair of nodes based on the graphlet transitions (see [185, 186]).”
Biological network comparison using graphlet degree distribution (2007, neutral): “Statistical and theoretical properties of these networks have been extensively studied (15; 20; 30; 22; 36; 41; 27; 34) due to their important biological implications (14; 17).”
Effect of sampling on topology predictions of protein-protein interaction networks (2005, background): “Recent reports have already noted discrepancies in matching existing interactome networks to a scale-free topolog”
Uncovering Biological Network Function via Graphlet Degree Signatures (2008, background): “…the degree of a node, which counts the number of edges that the node touches, into the vector of graphlet degrees , counting the number of graphlets that the node touches; graphlets are small connected non-isomorphic induced subgraphs of a large network (Prˇzulj et al., 2004) (see Figure 1).”
Analysis of Biological Networks (2008, background): “Protein interaction networks are generated out of different types of large-scale experimental and computational approaches [38].”
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting (2020, methodology): “The closely related concept of graphlets (Pržulj et al., 2004; Pržulj, 2007; Milenković & Pržulj, 2008; Sarajlić et al., 2016), different from motifs in being induced subgraphs, has been successfully used as a topological signature for network similarity.”
A combinatorial approach to graphlet counting (2014, background): “Pržulj et al. (2004) described an approach focused on small induced subgraphs called graphlets.”