Interaction network

About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.

Complexity management in visualizing protein interaction networks.

Abstract: Motivation: Protein-protein interaction networks often consist of thousands of nodes or more. This severely limits the utility of many graph drawing tools because they become too slow for an interactive analysis of the networks and because they produce cluttered drawings with many edge crossings. Results: An ew layout algorithm with complexity management operations in visualizing a large-scale protein interaction network was developed and implemented in a program called InterViewer3. InterViewer3 simplifies a complex network by collapsing a group of nodes with the same interacting partners into a composite node and by replacing a clique with a star-shaped subgraph. The experimental results demonstrated that InterViewer3 is one order of magnitude faster than the other drawing programs and that its complexity management is successful.

ProtNet: a tool for stochastic simulations of protein interaction networks dynamics

Abstract: Protein interactions support cell organization and mediate its response to any specific stimulus. Recent technological advances have produced large data-sets that aim at describing the cell interactome. These data are usually presented as graphs where proteins (nodes) are linked by edges to their experimentally determined partners. This representation reveals that protein-protein interaction (PPI) networks, like other kinds of complex networks, are not randomly organized and display properties that are typical of "hierarchical" networks, combining modularity and local clustering to scale free topology. However informative, this representation is static and provides no clue about the dynamic nature of protein interactions inside the cell. To fill this methodological gap, we designed and implemented a computer model that captures the discrete and stochastic nature of protein interactions. In ProtNet, our simplified model, the intracellular space is mapped onto either a two-dimensional or a three-dimensional lattice with each lattice site having a linear size (5 nm) comparable to the diameter of an average globular protein. The protein filled lattice has an occupancy (e.g. 20%) compatible with the estimated crowding of proteins in the cell cytoplasm. Proteins or protein complexes are free to translate and rotate on the lattice that represents a sort of naive unstructured cell (devoid of compartments). At each time step, molecular entities (proteins or complexes) that happen to be in neighboring cells may interact and form larger complexes or dissociate depending on the interaction rules defined in an experimental protein interaction network. This whole procedure can be seen as a sort of "discrete molecular dynamics" applied to interacting proteins in a cell. We have tested our model by performing different simulations using as interaction rules those derived from an experimental interactome of Saccharomyces cerevisiae (1378 nodes, 2491 edges) and we have compared the dynamics of complex formation in a two and a three dimensional lattice model. ProtNet is a cellular automaton model, where each protein molecule or complex is explicitly represented and where simple interaction rules are applied to populations of discrete particles. This tool can be used to simulate the dynamics of protein interactions in the cell.

Protein interaction network for Alzheimer's disease using computational approach

Abstract: Alzheimer's disease (AD) is the most common form of dementia. It is the sixth leading cause of death in old age people. Despite recent advances in the field of drug design, the medical treatment for the disease is purely symptomatic and hardly effective. Thus there is a need to understand the molecular mechanism behind the disease in order to improve the drug aspects of the disease. We provided two contributions in the field of proteomics in drug design. First, we have constructed a protein-protein interaction network for Alzheimer's disease reviewed proteins with 1412 interactions predicted among 969 proteins. Second, the disease proteins were given confidence scores to prioritize and then analyzed for their homology nature with respect to paralogs and homologs. The homology persisted with the mouse giving a basis for drug design phase. The method will create a new drug design technique in the field of bioinformatics by linking drug design process with protein-protein interactions via signal pathways. This method can be improvised for other diseases in future.

Analyzing the Localization of Retail Stores with Complex Systems Tools

Abstract: Measuring the spatial distribution of locations of many entities (trees, atoms, economic activities, ...), and, more precisely, the deviations from purely random configurations, is a powerful method to unravel their underlying interactions. I study here the spatial organization of retail commercial activities. From pure location data, network analysis leads to a community structure that closely follows the commercial classification of the US Department of Labor. The interaction network allows to build a 'quality' index of optimal location niches for stores, which has been empirically tested.

The effects of human-game interaction, network externalities, and motivations on players’ use of mobile casual games

Abstract: The purpose of this paper is to empirically examine the factors that influence the acceptance and use of mobile casual games.,A theoretical model is proposed based on the theory of reasonable action, the uses and gratifications theory, the network externalities (NEs) paradigm and the human–computer interaction literature. An empirical study was conducted through an online survey of mobile casual gamers in Spain, using a convenience sample. The proposed model was tested by an analysis of the collected data through a structural equation model using the partial least squares method.,The results indicate that human–mobile game interaction and NEs have a significant indirect impact on intention to play (IP), through utilitarian, hedonic and relational motivations. In addition, the full mediation effect of attitude was found between these constructs and IP, which is a very important determinant of actual use.,This study is among the few that focuses on users’ acceptance of mobile games apps, the features of which differ significantly from personal computer and console games. It highlights the effects of human–game interaction and NEs on the adoption of mobile casual games. Hence, the study contributes to the theoretical and practical understanding of the factors that lead users to adopt an entertainment mobile application.