Literature DB >> 12843395

Inferring genetic networks and identifying compound mode of action via expression profiling.

Timothy S Gardner1, Diego di Bernardo, David Lorenz, James J Collins.   

Abstract

The complexity of cellular gene, protein, and metabolite networks can hinder attempts to elucidate their structure and function. To address this problem, we used systematic transcriptional perturbations to construct a first-order model of regulatory interactions in a nine-gene subnetwork of the SOS pathway in Escherichia coli. The model correctly identified the major regulatory genes and the transcriptional targets of mitomycin C activity in the subnetwork. This approach, which is experimentally and computationally scalable, provides a framework for elucidating the functional properties of genetic networks and identifying molecular targets of pharmacological compounds.

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Year:  2003        PMID: 12843395     DOI: 10.1126/science.1081900

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  307 in total

1.  Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli by using network component analysis.

Authors:  Katy C Kao; Young-Lyeol Yang; Riccardo Boscolo; Chiara Sabatti; Vwani Roychowdhury; James C Liao
Journal:  Proc Natl Acad Sci U S A       Date:  2003-12-23       Impact factor: 11.205

2.  Network component analysis: reconstruction of regulatory signals in biological systems.

Authors:  James C Liao; Riccardo Boscolo; Young-Lyeol Yang; Linh My Tran; Chiara Sabatti; Vwani P Roychowdhury
Journal:  Proc Natl Acad Sci U S A       Date:  2003-12-12       Impact factor: 11.205

3.  Programmable cells: interfacing natural and engineered gene networks.

Authors:  Hideki Kobayashi; Mads Kaern; Michihiro Araki; Kristy Chung; Timothy S Gardner; Charles R Cantor; James J Collins
Journal:  Proc Natl Acad Sci U S A       Date:  2004-05-24       Impact factor: 11.205

4.  Identification of genetic networks.

Authors:  Momiao Xiong; Jun Li; Xiangzhong Fang
Journal:  Genetics       Date:  2004-02       Impact factor: 4.562

5.  An S-System Parameter Estimation Method (SPEM) for biological networks.

Authors:  Xinyi Yang; Jennifer E Dent; Christine Nardini
Journal:  J Comput Biol       Date:  2012-02       Impact factor: 1.479

6.  Effective models of periodically driven networks.

Authors:  Jason Shulman; Lars Seemann; Gemunu H Gunaratne
Journal:  Biophys J       Date:  2011-12-07       Impact factor: 4.033

Review 7.  Advantages and limitations of current network inference methods.

Authors:  Riet De Smet; Kathleen Marchal
Journal:  Nat Rev Microbiol       Date:  2010-08-31       Impact factor: 60.633

8.  Discovery of drug mode of action and drug repositioning from transcriptional responses.

Authors:  Francesco Iorio; Roberta Bosotti; Emanuela Scacheri; Vincenzo Belcastro; Pratibha Mithbaokar; Rosa Ferriero; Loredana Murino; Roberto Tagliaferri; Nicola Brunetti-Pierri; Antonella Isacchi; Diego di Bernardo
Journal:  Proc Natl Acad Sci U S A       Date:  2010-08-02       Impact factor: 11.205

9.  Revealing strengths and weaknesses of methods for gene network inference.

Authors:  Daniel Marbach; Robert J Prill; Thomas Schaffter; Claudio Mattiussi; Dario Floreano; Gustavo Stolovitzky
Journal:  Proc Natl Acad Sci U S A       Date:  2010-03-22       Impact factor: 11.205

10.  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

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