DocumentCode :
1147041
Title :
Reverse engineering gene regulatory networks
Author :
Huang, Yufei ; Tienda-Luna, Isabel M. ; Wang, Yufeng
Author_Institution :
Univ. of Texas at San Antonio, San Antonio, TX
Volume :
26
Issue :
1
fYear :
2009
Firstpage :
76
Lastpage :
97
Abstract :
Statistical models for reverse engineering gene regulatory networks are surveyed in this article. To provide readers with a system-level view of the modeling issues in this research, a graphical modeling framework is proposed. This framework serves as the scaffolding on which the review of different models can be systematically assembled. Based on the framework, we review many existing models for many aspects of gene regulation; the pros and cons of each model are discussed. In addition, network inference algorithms are also surveyed under the graphical modeling framework by the categories of point solutions and probabilistic solutions and the connections and differences among the algorithms are provided. This survey has the potential to elucidate the development and future of reverse engineering gene regulatory networks (GRNs) and bring statistical signal processing closer to the core of this research.
Keywords :
medical signal processing; probability; reverse engineering; statistical analysis; gene regulatory networks; graphical modeling framework; network inference algorithms; probabilistic solutions; reverse engineering; scaffolding; statistical signal processing; Biological system modeling; Biology computing; Computational systems biology; DNA; Inference algorithms; Land mobile radio cellular systems; Proteins; Reverse engineering; Robustness; Signal processing algorithms;
fLanguage :
English
Journal_Title :
Signal Processing Magazine, IEEE
Publisher :
ieee
ISSN :
1053-5888
Type :
jour
DOI :
10.1109/MSP.2008.930647
Filename :
4775882
Link To Document :
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