Literature DB >> 20416858

Reverse-engineering transcription control networks.

Timothy S Gardner1, Jeremiah J Faith.   

Abstract

Microarray technologies, which enable the simultaneous measurement of all RNA transcripts in a cell, have spawned the development of algorithms for reverse-engineering transcription control networks. In this article, we classify the algorithms into two general strategies: physical modeling and influence modeling. We discuss the biological and computational principles underlying each strategy, and provide leading examples of each. We also discuss the practical considerations for developing and applying the various methods.

Year:  2005        PMID: 20416858     DOI: 10.1016/j.plrev.2005.01.001

Source DB:  PubMed          Journal:  Phys Life Rev        ISSN: 1571-0645            Impact factor:   11.025


  59 in total

1.  Reverse engineering large-scale genetic networks: synthetic versus real data.

Authors:  Luwen Zhang; Mei Xiao; Yong Wang; Wu Zhang
Journal:  J Genet       Date:  2010-04       Impact factor: 1.166

2.  Information-theoretic inference of large transcriptional regulatory networks.

Authors:  Patrick E Meyer; Kevin Kontos; Frederic Lafitte; Gianluca Bontempi
Journal:  EURASIP J Bioinform Syst Biol       Date:  2007

3.  On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information.

Authors:  Catharina Olsen; Patrick E Meyer; Gianluca Bontempi
Journal:  EURASIP J Bioinform Syst Biol       Date:  2009-01-12

4.  NETWORKS, BIOLOGY AND SYSTEMS ENGINEERING: A CASE STUDY IN INFLAMMATION.

Authors:  P T Foteinou; E Yang; I P Androulakis
Journal:  Comput Chem Eng       Date:  2009-12-10       Impact factor: 3.845

5.  Reverse engineering of gene regulatory networks: a comparative study.

Authors:  Hendrik Hache; Hans Lehrach; Ralf Herwig
Journal:  EURASIP J Bioinform Syst Biol       Date:  2009-06-11

Review 6.  Learning transcriptional regulation on a genome scale: a theoretical analysis based on gene expression data.

Authors:  Ming Wu; Christina Chan
Journal:  Brief Bioinform       Date:  2011-05-26       Impact factor: 11.622

Review 7.  Systems analysis of high-throughput data.

Authors:  Rosemary Braun
Journal:  Adv Exp Med Biol       Date:  2014       Impact factor: 2.622

8.  minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information.

Authors:  Patrick E Meyer; Frédéric Lafitte; Gianluca Bontempi
Journal:  BMC Bioinformatics       Date:  2008-10-29       Impact factor: 3.169

9.  Evidence of highly regulated genes (in-Hubs) in gene networks of Saccharomyces cerevisiae.

Authors:  Jesper Lundström; Johan Björkegren; Jesper Tegnér
Journal:  Bioinform Biol Insights       Date:  2008-07-14

10.  A fast and efficient gene-network reconstruction method from multiple over-expression experiments.

Authors:  Dejan Stokić; Rudolf Hanel; Stefan Thurner
Journal:  BMC Bioinformatics       Date:  2009-08-17       Impact factor: 3.169

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