Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

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MG1655_temperature_O2_response_normalized_data_Tavazoie


ABSTRACT: Microarray transcriptional profiling was utilized to observe the global cellular state correlates of the physiological responses to temperature and oxygen changes. We provide evidence for the existence of correlations of Escherichia coli transcriptional responses to temperature and oxygen perturbations-precisely mirroring the co-occurrence of these parameters upon transitions between the outside world and the mammalian gastrointestinal-tract. Keywords: time course Physiological perturbations were carried out under a controlled environment in the context of bioreactor (Bioflo 110, New Brunswick Scientific) growth. Thermoelectric sensors and heaters were used to shift temperature profiles between 25 C and 37 C, and polarographic dissolved oxygen sensors (Mettler Toledo) and nitrogen gas was used to rapidly change oxygen saturation between anaerobic (0% dissolved oxygen) and aerobic (16-21% dissolved oxygen) condition. The cultures were maintained in exponential phase (O.D.600 0.2-0.4) through controlled dilution, where fresh media is pumped in and spent media is pumped out at a controlled rate. Prior to the perturbations, cells were maintained in the pre-transition environment for at least eight generations (8-24 hours). All experiments were performed in duplicate.

ORGANISM(S): Escherichia coli K12

SUBMITTER: yirchung Liu 

PROVIDER: E-GEOD-10855 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Predictive behavior within microbial genetic networks.

Tagkopoulos Ilias I   Liu Yir-Chung YC   Tavazoie Saeed S  

Science (New York, N.Y.) 20080508 5881


The homeostatic framework has dominated our understanding of cellular physiology. We question whether homeostasis alone adequately explains microbial responses to environmental stimuli, and explore the capacity of intracellular networks for predictive behavior in a fashion similar to metazoan nervous systems. We show that in silico biochemical networks, evolving randomly under precisely defined complex habitats, capture the dynamical, multidimensional structure of diverse environments by forming  ...[more]

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