Overview of computational methods for the inference of gene regulatory networks

2005 ◽  
Vol 29 (3) ◽  
pp. 519-534 ◽  
Author(s):  
Mark P. Styczynski ◽  
Gregory Stephanopoulos
2010 ◽  
Vol 2 ◽  
pp. BECB.S5594 ◽  
Author(s):  
Zahra Zamani ◽  
Amirhossein Hajihosseini ◽  
Ali Masoudi-Nejad

Molecular biology focuses on genes and their interactions at the transcription, regulation and protein level. Finding genes that cause certain behaviors can make therapeutic interventions more effective. Although biological tools can extract the genes and perform some analyses, without the help of computational methods, deep insight of the genetic function and its effects will not occur. On the other hand, complex systems can be modeled by networks, introducing the main data as nodes and the links in-between as the transactions occurring within the network. Gene regulatory networks are examples that are modeled and analyzed in order to gain insight of their exact functions. Since a cell's specific functionality is greatly determined by the genes it expresses, translation or the act of converting mRNA to proteins is highly regulated by the control network that directs cellular activities. This paper briefly reviews the most important computational methods for analyzing, modeling and controlling the gene regulatory networks.


Author(s):  
Hendrik Hache

In this chapter, different methods and applications for reverse engineering of gene regulatory networks that have been developed in recent years are discussed and compared. Inferring gene networks from different kinds of experimental data are a challenging task that emerged, especially with the development of high throughput technologies. Various computational methods based on diverse principles were introduced to identify new regulations among genes. Mathematical aspects of the models are highlighted, and applications for reverse engineering are mentioned.


2017 ◽  
Vol 2 ◽  
pp. 115-122 ◽  
Author(s):  
Archana S. Iyer ◽  
Hatice U. Osmanbeyoglu ◽  
Christina S. Leslie

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