scispace - formally typeset
Search or ask a question
Topic

Interaction network

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


Papers
More filters
Journal ArticleDOI
01 Feb 2012-Genomics
TL;DR: This review aims to discuss the computational approaches used till date to construct a malaria protein interaction network and to catalog the functional predictions and biological inferences made from analysis of the PPI network.

31 citations

Journal ArticleDOI
TL;DR: From parameter values, it is found that concurrent and existing existing duplication and divergence models are insufficient for modeling protein interaction network evolution and a model enhancement based on heritable interaction sites on the surface of a protein is introduced that is more closely reflects the high clustering found in the empirical network.
Abstract: Motivation: Theoretical models of biological networks are valuable tools in evolutionary inference. Theoretical models based on gene duplication and divergence provide biologically plausible evolutionary mechanics. Similarities found between empirical networks and their theoretically generated counterpart are considered evidence of the role modeled mechanics play in biological evolution. However, the method by which these models are parameterized can lead to questions about the validity of the inferences. Selecting parameter values in order to produce a particular topological value obfuscates the possibility that the model may produce a similar topology for a large range of parameter values. Alternately, a model may produce a large range of topologies, allowing (incorrect) parameter values to produce a valid topology from an otherwise flawed model. In order to lend biological credence to the modeled evolutionary mechanics, parameter values should be derived from the empirical data. Furthermore, recent work indicates that the timing and fate of gene duplications are critical to proper derivation of these parameters. Results: We present a methodology for deriving evolutionary rates from empirical data that is used to parameterize duplication and divergence models of protein interaction network evolution. Our method avoids shortcomings of previous methods, which failed to consider the effect of subsequent duplications. From our parameter values, we find that concurrent and existing existing duplication and divergence models are insufficient for modeling protein interaction network evolution. We introduce a model enhancement based on heritable interaction sites on the surface of a protein and find that it more closely reflects the high clustering found in the empirical network. Contact: Debra@Colorado.edu Supplementary information: Supplementary data are available at Bioinformatics online.

31 citations

Journal ArticleDOI
TL;DR: It is proposed that most biological combinations of domains into proteins and complexes can be explained by a small number of key topological motifs.
Abstract: Motivation: The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Topological analysis of such networks can provide an understanding of and solutions for predicting properties of proteins and their evolution in terms of domains. A single paradigm for the analysis of interactions at different layers, such as domain and protein layers, is needed. Results: Applying a colored vertex graph model, we integrated two basic interaction layers under a unified model: (1) structural domains and (2) their protein/complex networks. We identified four basic and distinct elements in the model that explains protein interactions at the domain level. We searched for motifs in the networks to detect their topological characteristics using a pruning strategy and a hash table for rapid detection. We obtained the following results: first, compared with a random distribution, a substantial part of the protein interactions could be explained by domain-level structural interaction information. Second, there were distinct kinds of protein interaction patterns classified by specific and distinguishable numbers of domains. The intermolecular domain interaction was the most dominant protein interaction pattern. Third, despite the coverage of the protein interaction information differing among species, the similarity of their networks indicated shared architectures of protein interaction network in living organisms. Remarkably, there were only a few basic architectures in the model (>10 for a 4-node network topology), and we propose that most biological combinations of domains into proteins and complexes can be explained by a small number of key topological motifs. Contact: [email protected]

31 citations

Journal ArticleDOI
01 Oct 2004-Proteins
TL;DR: A unified representation of the protein–protein and complex–complex networks based on an underlying bipartite graph model that is an advance over existing models of the network and allows for weighting of connections between proteins shared in more than one complex.
Abstract: The protein interaction network presents one perspective for understanding cellular processes. Recent experiments employing high-throughput mass spectrometric characterizations have resulted in large data sets of physiologically relevant multiprotein complexes. We present a unified representation of such data sets based on an underlying bipartite graph model that is an advance over existing models of the network. Our unified representation allows for weighting of connections between proteins shared in more than one complex, as well as addressing the higher level organization that occurs when the network is viewed as consisting of protein complexes that share components. This representation also allows for the application of the rigorous MinMaxCut graph clustering algorithm for the determination of relevant protein modules in the networks. Statistically significant annotations of clusters in the protein-protein and complex-complex networks using terms from the Gene Ontology indicate that this method will be useful for posing hypotheses about uncharacterized components of protein complexes or uncharacterized relationships between protein complexes.

31 citations

Journal ArticleDOI
TL;DR: This review addresses the development and current progress of the resources available for systems biology in rice: Genome browsers and databases for the orthology identification, transcriptome analysis, protein-protein interaction network and functional gene network analyses, a co-expression network, metabolic pathway analysis for promoter analysis, and gene indexed mutants.
Abstract: Systems biology is an upcoming trend in the field of functional genomics. Recently, there has been a significant improvement in the resources for systems biology in Oryza sativa (rice), a model crop. These resources include whole-genome sequencing/re-sequencing data, transcriptomes, protein-protein interactomes, reactomes, functional gene network tools, and gene indexed mutant populations. The integration of diverse omics data can lead to greater understanding of the functional genomics of rice. In this review, we address the development and current progress of the resources available for systems biology in rice: Genome browsers and databases for the orthology identification, transcriptome analysis, protein-protein interaction network and functional gene network analyses, a co-expression network, metabolic pathway analysis for promoter analysis, and gene indexed mutants.

31 citations


Network Information
Related Topics (5)
Genome
74.2K papers, 3.8M citations
83% related
Regulation of gene expression
85.4K papers, 5.8M citations
81% related
Cluster analysis
146.5K papers, 2.9M citations
80% related
Gene
211.7K papers, 10.3M citations
79% related
Transcription factor
82.8K papers, 5.4M citations
78% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202337
202290
2021183
2020221
2019201
2018163