Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets.
about
Identifying inhibitory compounds in lignocellulosic biomass hydrolysates using an exometabolomics approachQuantitative metabolomics of the thermophilic methylotroph Bacillus methanolicusTuberculosis Drug Development: History and Evolution of the Mechanism-Based Paradigm.Probiotics, enteric and diarrheal diseases, and global health.Metabolomic tools for secondary metabolite discovery from marine microbial symbiontsCentering, scaling, and transformations: improving the biological information content of metabolomics data.Distinguishing enzymes using metabolome data for the hybrid dynamic/static method.Exometabolomics approaches in studying the application of lignocellulosic biomass as fermentation feedstock.Substrate-baited nanoparticles: a catch and release strategy for enzyme recognition and harvesting.Deregulation of purine pathway in Bacillus subtilis and its use in riboflavin biosynthesisSystems perspectives on erythromycin biosynthesis by comparative genomic and transcriptomic analyses of S. erythraea E3 and NRRL23338 strains.Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds.Chicken, beams, and Campylobacter: rapid differentiation of foodborne bacteria via vibrational spectroscopy and MALDI-mass spectrometry.Comparative genomics and transcriptional profiles of Saccharopolyspora erythraea NRRL 2338 and a classically improved erythromycin over-producing strain.Translational research in infectious disease: current paradigms and challenges ahead.LC-MS-based metabolomics in drug metabolismPhenotypes and gene expression profiles of Saccharopolyspora erythraea rifampicin-resistant (rif) mutants affected in erythromycin productionGenome Sequence of Saccharopolyspora erythraea D, a Hyperproducer of Erythromycin.The undernourished neonatal mouse metabolome reveals evidence of liver and biliary dysfunction, inflammation, and oxidative stress.LC-MS-based Metabolomics of Xenobiotic-induced Toxicities.Metabolomic systems biology of trypanosomes.Looking back into the future: 30 years of metabolomics at TNO.Mass-spectrometry-based microbial metabolomics: recent developments and applications.Suitability of silica hydride stationary phase, aqueous normal phase chromatography for untargeted metabolomic profiling of Enterococcus faecium and Staphylococcus aureus.Central carbon metabolism in Mycobacterium tuberculosis: an unexpected frontier.Modeling Neisseria meningitidis metabolism: from genome to metabolic fluxes.Bringing metabolomics data into the forefront of systems biology.Metabolic profiling as a tool for understanding defense response of Taxus cuspidata cells to shear stress.Hygeia versus Asclepius.Elucidation of ethanol tolerance mechanisms in Saccharomyces cerevisiae by global metabolite profiling.Pharmacological Metabolomics in Trypanosomes
P2860
Q28658400-F0ADF9D7-C34A-4352-9A88-9BA85D0105EBQ28829591-0626B653-12E6-462D-B503-AC60B0F1B799Q30373724-7B5C6415-5255-4988-BBE9-961C5E1B9D20Q30425561-68939652-4AC0-4D70-805A-B6065DE304C9Q30829462-55449DCD-0535-431E-870C-62827DDB6277Q31043704-E35243A8-3686-4151-B531-8A0945435DBFQ31113070-76FE94EC-16D6-4BA1-A8B1-020A87B3B43FQ34041319-71DE2C6F-1990-42F5-B99E-EE77C5E1A166Q34244794-C20E2A5C-F9C0-4A1D-B7EE-DD2CA1C4EC2AQ34468708-2FA6D51D-1E71-45CB-814D-57799C87737FQ34883444-A2ACB995-5584-4401-B3C6-BCBD00DDAF79Q35007800-F9CA1674-2B72-4424-8D23-8939FD78ED85Q35829100-DBAB990B-3685-4B1A-BDE0-7498BE8F0AB0Q35984452-21940A32-6BF5-410B-BF0A-0B4FAABDF0D1Q35992621-A991296E-68EF-4A83-B6FF-0398A8B9B668Q36275152-6CC1615B-E1DE-4331-A654-2FD79A584015Q37154618-91E67BBD-6114-489E-AFD0-E10BA20D50AEQ37185511-5C0A858E-B701-4767-AF44-37CC4B73CF1AQ37590369-D2FA0878-CA2B-4C14-9FC0-EACFC72B6342Q37654881-70D25332-42B0-411F-BC5C-1C76FC1D25D2Q37693232-C8B9F626-4377-461E-8FE6-349BF6228CCAQ38102969-F973581B-A3CC-427C-9D4B-B59B1D471419Q38248756-62638268-3279-4B9D-B1B7-44E060DC6108Q38380491-D61F9925-67E0-4051-A5AC-B83C3CBB3D20Q41845465-07C5204A-1E40-461D-B2D1-A20F5244912CQ42744347-423E0A75-C487-476D-B3C8-508EC519CB3EQ43147920-DD875AFA-94F2-41F8-BBC3-5D8748CFFB41Q45928076-C0934317-FF53-4A93-9F83-E31F6E1AB4DFQ47766602-8A4E2F2E-19C6-40A6-BC80-012E4960C1FAQ51287973-9CBA7118-8CD3-46B3-9DD3-D7CCA75FB721Q58854359-4589E691-B610-404F-8896-4FB4602B5BB5
P2860
Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets.
description
2005 nî lūn-bûn
@nan
2005年の論文
@ja
2005年論文
@yue
2005年論文
@zh-hant
2005年論文
@zh-hk
2005年論文
@zh-mo
2005年論文
@zh-tw
2005年论文
@wuu
2005年论文
@zh
2005年论文
@zh-cn
name
Microbial metabolomics: replac ...... ection and ranking of targets.
@ast
Microbial metabolomics: replac ...... ection and ranking of targets.
@en
type
label
Microbial metabolomics: replac ...... ection and ranking of targets.
@ast
Microbial metabolomics: replac ...... ection and ranking of targets.
@en
prefLabel
Microbial metabolomics: replac ...... ection and ranking of targets.
@ast
Microbial metabolomics: replac ...... ection and ranking of targets.
@en
P2860
P1476
Microbial metabolomics: replac ...... lection and ranking of targets
@en
P2093
Mariët J van der Werf
P2860
P304
P356
10.1007/S10295-005-0231-4
P577
2005-05-14T00:00:00Z