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Author Cannataro, Mario ♦ Guzzi, Pietro H. ♦ Veltri, Pierangelo
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Protein to protein interaction networks ♦ Interactomics ♦ Protein complexes ♦ Protein to protein interaction databases ♦ Systems biology
Abstract Studying proteins and their structures has an important role for understanding protein functionalities. Recently, due to important results obtained with proteomics, a great interest has been given to $\textit{interactomics},$ that is, the study of protein-to-protein interactions, called PPI, or more generally, interactions among macromolecules, particularly within cells. Interactomics means studying, modeling, storing, and retrieving protein-to-protein interactions as well as algorithms for manipulating, simulating, and predicting interactions. PPI data can be obtained from biological experiments studying interactions. Modeling and storing PPIs can be realized by using graph theory and graph data management, thus graph databases can be queried for further experiments. PPI graphs can be used as input for data-mining algorithms, where raw data are binary interactions forming interaction graphs, and analysis algorithms retrieve biological interactions among proteins (i.e., PPI biological meanings). For instance, predicting the interactions between two or more proteins can be obtained by mining interaction networks stored in databases. In this article we survey modeling, storing, analyzing, and manipulating PPI data. After describing the main PPI models, mostly based on graphs, the article reviews PPI data representation and storage, as well as PPI databases. Algorithms and software tools for analyzing and managing PPI networks are discussed in depth. The article concludes by discussing the main challenges and research directions in PPI networks.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-12-03
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 43
Issue Number 1
Page Count 36
Starting Page 1
Ending Page 36


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Source: ACM Digital Library