Facilitating the development of controlled vocabularies for metabolomics technologies with text mining-4

<p><b>Copyright information:</b></p><p>Taken from "Facilitating the development of controlled vocabularies for metabolomics technologies with text mining"</p><p>http://www.biomedcentral.com/1471-2105/9/S5/S5</p><p>BMC Bioinformatics 2008;9(Suppl 5):S5-S5.</p><p>Published online 29 Apr 2008</p><p>PMCID:PMC2367623.</p><p></p

Facilitating the development of controlled vocabularies for metabolomics technologies with text mining-3

<p><b>Copyright information:</b></p><p>Taken from "Facilitating the development of controlled vocabularies for metabolomics technologies with text mining"</p><p>http://www.biomedcentral.com/1471-2105/9/S5/S5</p><p>BMC Bioinformatics 2008;9(Suppl 5):S5-S5.</p><p>Published online 29 Apr 2008</p><p>PMCID:PMC2367623.</p><p></p

Facilitating the development of controlled vocabularies for metabolomics technologies with text mining-2

<p><b>Copyright information:</b></p><p>Taken from "Facilitating the development of controlled vocabularies for metabolomics technologies with text mining"</p><p>http://www.biomedcentral.com/1471-2105/9/S5/S5</p><p>BMC Bioinformatics 2008;9(Suppl 5):S5-S5.</p><p>Published online 29 Apr 2008</p><p>PMCID:PMC2367623.</p><p></p

Performing statistical analyses on quantitative data in Taverna workflows: An example using R and maxdBrowse to identify differentially-expressed genes from microarray data-1

<p><b>Copyright information:</b></p><p>Taken from "Performing statistical analyses on quantitative data in Taverna workflows: An example using R and maxdBrowse to identify differentially-expressed genes from microarray data"</p><p>http://www.biomedcentral.com/1471-2105/9/334</p><p>BMC Bioinformatics 2008;9():334-334.</p><p>Published online 7 Aug 2008</p><p>PMCID:PMC2528018.</p><p></p

A schematic diagram showing the relationship between Taverna, the RShell processor, RServe and the R tool

<p><b>Copyright information:</b></p><p>Taken from "Performing statistical analyses on quantitative data in Taverna workflows: An example using R and maxdBrowse to identify differentially-expressed genes from microarray data"</p><p>http://www.biomedcentral.com/1471-2105/9/334</p><p>BMC Bioinformatics 2008;9():334-334.</p><p>Published online 7 Aug 2008</p><p>PMCID:PMC2528018.</p><p></p

Integration of proteome and metabolic control to show regulation of sulfur and C1 (folate) metabolic fluxes at the protein (enzyme) level

Selected proteins with levels consistently upregulated (red) or downregulated (green) with growth independently of culture conditions are shown. Sulfur, C1 metabolism, methyl cycle, methionine and -adenosylmethionine (SAM) fluxes towards methylation of proteins, rRNAs and tRNAs, and protein biosynthesis are shown here. Metabolic pathways and enzymes are from [42,82, 103-105] and the diagram is drawn with Cell Designer [136] and Adobe Illustrator [137]. Reverse methionine biosynthetic pathways [83] have been omitted for clarity. Metabolite abbreviations: THF, tetrahydrofolate; METTHF, 5,10-methylenetetrahydrofolate; MTHPTGLUT, 5-methyltetrahydropteroyltriglutamate (donor of the terminal methyl group in methionine biosynthesis); GT, glutathione; CYS, cysteine; CT, cystathionine; OAHS, -acetylhomoserine; HCYS, homocysteine; MET, methionine; SAM, -adenosylmethionine; SAH, -adenosylhomocysteine; D-SAM, decarboxylated -adenosylmethionine; MTA, methylthioadenosine. Metabolic steps (genes/enzymes): Met10p, sulfite reductase alpha subunit; Ecm17p, sulfite reductase beta subunit; , folylpolyglutamate synthetase (Met7p not detected; the relevance of polyglutamylation in the C1 metabolism branch was demonstrated at the transcriptional level (see text)); Met13p, methylenetetrahydrofolate reductase isozyme; Met6p, methionine synthase; Mes1p, methionyl-tRNA synthetase; Sam1p, S-adenosylmethionine synthetase isozyme; Sam2p, S-adenosylmethionine synthetase isozyme. Sah1p, S-adenosyl-L-homocysteine hydrolase; Ado1p, adenosine kinase.<p><b>Copyright information:</b></p><p>Taken from "Growth control of the eukaryote cell: a systems biology study in yeast"</p><p>http://jbiol.com/content/6/2/4</p><p>Journal of Biology 2007;6(2):4-4.</p><p>Published online 30 Apr 2007</p><p>PMCID:PMC2373899.</p><p></p

Integration of proteome and metabolic control to show regulation of carbon and nitrogen metabolic fluxes at the protein (enzyme) level

Shown here are the coupling of carbon and nitrogen fluxes at the level of glutamate dehydrogenase (Gdh1p, Gdh2p) and glutamine synthetase (Gln1p), the regulation of arginine biosynthesis at the carbamoyl phosphate synthetase (Cpa1p, Cpa2p) level and amino-acid biosynthesis, and amino-acid sensing by TOR. Selected proteins with levels consistently upregulated (red) with growth independently of culture conditions are shown. Enzymes responsible for the cytosolic 2-oxoglutarate pool: Aco1p and Aco2p, aconitase and putative aconitase isoenzyme; Odc1p and Odc2p, mitochondrial 2-oxoglutarate transporters; Idp2p, NADP-specific isocitrate dehydrogenase. Enzyme subunits coupling the oxidation of succinate to the transfer of electrons to ubiquinone: Sdh1p and Sdh2p, succinate dehydrogenase, flavoprotein, and iron-sulfur protein subunits, respectively. Metabolic diagram from [42, 91, 92] and drawn using Cell Designer [136] and Adobe Illustrator [137].<p><b>Copyright information:</b></p><p>Taken from "Growth control of the eukaryote cell: a systems biology study in yeast"</p><p>http://jbiol.com/content/6/2/4</p><p>Journal of Biology 2007;6(2):4-4.</p><p>Published online 30 Apr 2007</p><p>PMCID:PMC2373899.</p><p></p