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

by: TS Gardner, D di Bernardo, D Lorenz, JJ Collins

Science, Vol. 301, No. 5629. (4 July 2003), pp. 102-105.

View FullText article

DOI, HighWire, Pubmed, Hubmed

Reviews [Write a review of this article]

There are no reviews of this article

Find related articles from these CiteULike users

BTS-PT-AS-CS (group), vertikalist, rklancer, kentsis, dragonrez, rhem1224, rubendrr, adepace, neveaire, jayster4, Biostatistics (group), Computational-Genomics (group), Bioinformatics (group), EisenLab (group), Neuroscience (group), TCR_Signaling (group), cytoskeleton-and-afm (group), SysbioMPI-TCells (group), heliopais, roys

Find related articles with these CiteULike tags

biological_networks, collins_lab, genomic, grn, key--gene_expression, key--regulatory_network, key--systems_biology, mesophysics, mode_of_action, network, network_identification, network_inference, networks, no-tag, regression, reverse_engineering, sysbio, system_biology, tech--network_modeling

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.

@article{citeulike:339763, 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.}, address = {Center for BioDynamics and Department of Biomedical Engineering, Boston University, 44 Cummington Street, Boston, MA 02215, USA.}, author = {Gardner, T. S. and di Bernardo, D. and Lorenz, D. and Collins, J. J. }, citeulike-article-id = {339763}, doi = {http://dx.doi.org/10.1126/science.1081900}, issn = {1095-9203}, journal = {Science}, keywords = {grn, network\_inference}, month = {July}, number = {5629}, pages = {102--105}, posted-at = {2007-12-12 11:32:11}, priority = {3}, title = {Inferring genetic networks and identifying compound mode of action via expression profiling.}, url = {http://dx.doi.org/10.1126/science.1081900}, volume = {301}, year = {2003} }

RIS record

TY - JOUR ID - citeulike:339763 L3 - citeulike-article-id:339763 TI - Inferring genetic networks and identifying compound mode of action via expression profiling. JF - Science VL - 301 IS - 5629 SP - 102 EP - 105 AD - Center for BioDynamics and Department of Biomedical Engineering, Boston University, 44 Cummington Street, Boston, MA 02215, USA. SN - 1095-9203 N2 - 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. KW - grn KW - network_inference AU - Gardner, TS AU - di Bernardo, D AU - Lorenz, D AU - Collins, JJ PY - 2003/07/04/ UR - http://dx.doi.org/10.1126/science.1081900 ER -

RIS BibTeX