{"id":2265,"date":"2014-12-09T18:09:32","date_gmt":"2014-12-09T18:09:32","guid":{"rendered":"http:\/\/www.blopig.com\/blog\/?p=2265"},"modified":"2014-12-09T18:09:56","modified_gmt":"2014-12-09T18:09:56","slug":"looking-for-a-null-model-of-ppi-ego-networks","status":"publish","type":"post","link":"https:\/\/www.blopig.com\/blog\/2014\/12\/looking-for-a-null-model-of-ppi-ego-networks\/","title":{"rendered":"Looking for a null model of PPI ego-networks"},"content":{"rendered":"
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Protein-protein interaction (PPI) networks describe how proteins are connected to one another in terms of physical interactions. They can be used to aid our understanding of the individual roles of proteins (Sarajli \u0301c et al., 2013), the co-functioning properties of sets of proteins (West et al., 2013) and even the operation of the complete system (Janowski et al., 2014).<\/p>\n

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Different approaches have been proposed to analyse, describe and predict these PPI networks, such as network summary statistics, clustering methods, random graph models and machine learning methods. However, despite the large biological, computational and statistical interest in PPI net- works, current models insufficiently describe PPI networks (Winterbach et al., 2013; Ali et al., 2014; Rito et al., 2010). It is commonly accepted that proteins perform functions usually in conjunction with other proteins, forming a functional module (Lewis et al., 2010). Hence local structure is found to be important in protein-protein interaction networks (Planas-Iglesias et al., 2013).<\/p>\n

Here we address the modelling problem locally by modelling the ego-networks of PPI networks by means of random graph models.<\/p>\n

Random graph models<\/strong><\/p>\n

Loosely speaking, a random graph model is a set of rules that define an edge generation process among a set of nodes. Usually this\u00a0set of rules relate to particular characteristics that are embedded in the network generation process. Here are three examples of such characteristics:<\/p>\n