Genome-scale analysis of the uses of the Escherichia coli genome: model-driven analysis of heterogeneous data sets. Export

@article{allen2003, abstract = {The recent availability of heterogeneous high-throughput data types has increased the need for scalable in silico methods with which to integrate data related to the processes of regulation, protein synthesis, and metabolism. A sequence-based framework for modeling transcription and translation in prokaryotes has been established and has been extended to study the expression state of the entire Escherichia coli genome. The resulting in silico analysis of the expression state highlighted three facets of gene expression in E. coli: (i) the metabolic resources required for genome expression and protein synthesis were found to be relatively invariant under the conditions tested; (ii) effective promoter strengths were estimated at the genome scale by using global mRNA abundance and half-life data, revealing genes subject to regulation under the experimental conditions tested; and (iii) large-scale genome location-dependent expression patterns with approximately 600-kb periodicity were detected in the E. coli genome based on the 49 expression data sets analyzed. These results support the notion that a structured model-driven analysis of expression data yields additional information that can be subjected to commonly used statistical analyses. The integration of heterogeneous genome-scale data (i.e., sequence, expression data, and mRNA half-life data) is readily achieved in the context of an in silico model.}, author = {Allen, T. E. and Herrg\r{a}rd, M. J. and Liu, M. and Qiu, Y. and Glasner, J. D. and Blattner, F. R. and Palsson}, citeulike-article-id = {3424546}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/14563874}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=14563874}, comment = {This paper looks at estimating amino acid usage based on the predicted requirements of amino acids for protein synthesis in a variety of conditions. Protein synthesis is estimated in a variety of conditions based on a transcription factors such transcription/translation rate, abundance, and half life. The authors found that the metabolic requirements for protein synthesis were relatively invariant across conditions, but that the amino acids with the most variation were tryptophan, cysteine, and lysine. The authors not that this invariance would not be true if a gene with an atypical amino acid composition were to be expressed to a high level.}, issn = {0021-9193}, journal = {Journal of bacteriology}, keywords = {amino-acid-usage, cost-benefit-analysis, density, ecoli, energy-production, flux-balance-analysis, gene-expression, gene-expression-degradation, gene-length, mdb-manuscript-1, mdb-thesis, metabolism, mrn-degradation, nutrient-limitation, promoters, reviewed, ribosomal, systems-biology, transcription, translation}, month = {November}, number = {21}, pages = {6392--6399}, posted-at = {2008-10-17 14:55:43}, priority = {0}, title = {Genome-scale analysis of the uses of the \emph{Escherichia coli} genome: model-driven analysis of heterogeneous data sets.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/14563874}, volume = {185}, year = {2003} }

TY - JOUR ID - allen2003 L3 - citeulike-article-id:3424546 N2 - The recent availability of heterogeneous high-throughput data types has increased the need for scalable in silico methods with which to integrate data related to the processes of regulation, protein synthesis, and metabolism. A sequence-based framework for modeling transcription and translation in prokaryotes has been established and has been extended to study the expression state of the entire Escherichia coli genome. The resulting in silico analysis of the expression state highlighted three facets of gene expression in E. coli: (i) the metabolic resources required for genome expression and protein synthesis were found to be relatively invariant under the conditions tested; (ii) effective promoter strengths were estimated at the genome scale by using global mRNA abundance and half-life data, revealing genes subject to regulation under the experimental conditions tested; and (iii) large-scale genome location-dependent expression patterns with approximately 600-kb periodicity were detected in the E. coli genome based on the 49 expression data sets analyzed. These results support the notion that a structured model-driven analysis of expression data yields additional information that can be subjected to commonly used statistical analyses. The integration of heterogeneous genome-scale data (i.e., sequence, expression data, and mRNA half-life data) is readily achieved in the context of an in silico model. IS - 21 JF - Journal of bacteriology SN - 0021-9193 EP - 6399 TI - Genome-scale analysis of the uses of the \emph{Escherichia coli} genome: model-driven analysis of heterogeneous data sets. VL - 185 SP - 6392 KW - amino-acid-usage KW - cost-benefit-analysis KW - density KW - ecoli KW - energy-production KW - flux-balance-analysis KW - gene-expression KW - gene-expression-degradation KW - gene-length KW - mdb-manuscript-1 KW - mdb-thesis KW - metabolism KW - mrn-degradation KW - nutrient-limitation KW - promoters KW - reviewed KW - ribosomal KW - systems-biology KW - transcription KW - translation N1 - This paper looks at estimating amino acid usage based on the predicted requirements of amino acids for protein synthesis in a variety of conditions. Protein synthesis is estimated in a variety of conditions based on a transcription factors such transcription/translation rate, abundance, and half life. The authors found that the metabolic requirements for protein synthesis were relatively invariant across conditions, but that the amino acids with the most variation were tryptophan, cysteine, and lysine. The authors not that this invariance would not be true if a gene with an atypical amino acid composition were to be expressed to a high level. AU - Allen, TE AU - Herrgård, MJ AU - Liu, M AU - Qiu, Y AU - Glasner, JD AU - Blattner, FR AU - Palsson PY - 2003/11// UR - http://view.ncbi.nlm.nih.gov/pubmed/14563874 ER -