Dataset Information


Dynamics of Ecoli Aerobic to Anaerobic Switch Response

ABSTRACT: The experiment is a time course for the aerobic to anaerobic switch response in E. coli. The data was used to validate the utility of a set of predicted transcription factor gene interactions for modeling the dynamic regulatory response networks of this response. The transcription factor gene interaction predictions were generated by a semi-supervised classification method that takes advantage of a separate compendium of gene expression and a data set of curated interactions. Keywords: time course, environment change response Overall design: In total there are 11 samples measuring the gene expression response at the different time points from the change from aerobic to anaerobic conditions. The control sample was based on completely aerobic conditions before shutting off oxygen. The test samples correspond to 0min, 2min, 5min, 15min, 25min, 35min, 45min, and 55min after shutting off Oxygen. Technical replicates were taken for the 5min, 25min, and 55min time points. Two channel microarray labeling was used with Cy5 and Cy3 labels. The standard aerobic was labeled with Cy5 in the 0min, 2min, 5min replicate 1, and both 25min replicates. In all other experiments the standard was labeled with Cy3.

INSTRUMENT(S): IntegratedGenomics_Ecoli_14K_v1-12

SUBMITTER: Jason Ernst  

PROVIDER: GSE8323 | GEO | 2008-02-19



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A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.

Ernst Jason J   Beg Qasim K QK   Kay Krin A KA   Balázsi Gábor G   Oltvai Zoltán N ZN   Bar-Joseph Ziv Z  

PLoS computational biology 20080328 3

While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi-supervised REgulatory Netw  ...[more]

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