Network Querying Techniques for PPI Network Comparison

2009 ◽  
pp. 312-334
Author(s):  
Valeria Fionda ◽  
Luigi Palopoli
Keyword(s):  
Knowledge Discovery ◽  
Functional Module ◽  
Research Area ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Knowledge ◽  
Network Module ◽  
Interaction Data ◽  
Biological Interaction ◽  
Research Issues

The aim of this chapter is that of analyzing and comparing network querying techniques as applied to protein interaction networks. In the last few years, several automatic tools supporting knowledge discovery from available biological interaction data have been developed. In particular, network querying tools search a whole biological network to identify conserved occurrences of a query network module. The goal of such techniques is that of transferring biological knowledge. Indeed, the query subnetwork generally encodes a well-characterized functional module, and its occurrences in the queried network probably denote that this function is featured by the associated organism. The proposed analysis is intended to be useful to understand problems and research issues, state of the art and opportunities for researchers working in this research area.

scholarly journals XlinkCyNET: a Cytoscape application for visualization of protein interaction networks based on cross-linking mass-spectrometry identifications

2020 ◽  
Author(s):  
Diogo Borges Lima ◽  
Ying Zhu ◽  
Fan Liu
Keyword(s):  
Mass Spectrometry ◽  
Protein Interaction ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Cross Linking ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Link Type

ABSTRACTSoftware tools that allow visualization and analysis of protein interaction networks are essential for studies in systems biology. One of the most popular network visualization tools in biology is Cytoscape, which offers a large selection of plugins for interpretation of protein interaction data. Chemical cross-linking coupled to mass spectrometry (XL-MS) is an increasingly important source for such interaction data, but there are currently no Cytoscape tools to analyze XL-MS results. In light of the suitability of Cytoscape platform but also to expand its toolbox, here we introduce XlinkCyNET, an open-source Cytoscape Java plugin for exploring large-scale XL-MS-based protein interaction networks. XlinkCyNET offers rapid and easy visualization of intra and intermolecular cross-links and the locations of protein domains in a rectangular bar style, allowing subdomain-level interrogation of the interaction network. XlinkCyNET is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/xlinkcynet and at https://www.theliulab.com/software/xlinkcynet.

Discovering Interaction Motifs from Protein Interaction Networks

2009 ◽  
pp. 99-116 ◽  
Author(s):  
Hugo Willy
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Patterns ◽  
Protein Protein Interaction ◽  
Family Based ◽  
High Throughput Experiments

Recent breakthroughs in high throughput experiments to determine protein-protein interaction have generated a vast amount of protein interaction data. However, most of the experiments could only answer the question of whether two proteins interact but not the question on the mechanisms by which proteins interact. Such understanding is crucial for understanding the protein interaction of an organism as a whole (the interactome) and even predicting novel protein interactions. Protein interaction usually occurs at some specific sites on the proteins and, given their importance, they are usually well conserved throughout the evolution of the proteins of the same family. Based on this observation, a number of works on finding protein patterns/motifs conserved in interacting proteins have emerged in the last few years. Such motifs are collectively termed as the interaction motifs. This chapter provides a review on the different approaches on finding interaction motifs with a discussion on their implications, potentials and possible areas of improvements in the future.

scholarly journals From MIN model to ordinary differential equations

2007 ◽  
Vol 4 (3) ◽  
pp. 15-26
Author(s):  
A. Yartseva ◽  
R. Devillers ◽  
H. Klaudel ◽  
F. Képès
Keyword(s):  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Biological System ◽  
Interaction Network ◽  
Interaction Networks ◽  
Biological Knowledge ◽  
Biological Interaction ◽  
Genetic Switch ◽  
Modeling Level ◽  
Translation Procedure

Summary Biological interaction networks can be modeled using the Modular Interaction Network (MIN) formalism, which provides an intermediary modeling level between the biological and mathematical ones. MIN focuses on a simple but structured and versatile representation of biological knowledge, without targeting a particular analysis or simulation technique. In this paper, we propose a translation procedure which, starting from a MIN specification of a biological system, generates its representation in ordinary differential equations (ODEs) allowing to study the dynamics of the system. The translation is illustrated on a classical benchmark: the λ phage genetic switch.

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