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The role of metabolomics in systems biology |
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1 | (10) |
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1 | (1) |
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1 | (2) |
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Applications of metabolomics |
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3 | (1) |
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The role of metabolomics in systems biology |
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4 | (2) |
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6 | (5) |
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8 | (3) |
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Analytical methods from the perspective of method standardization |
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11 | (42) |
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11 | (1) |
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11 | (2) |
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Pre-analytical variability |
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13 | (15) |
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13 | (1) |
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Variability introduced during sampling |
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14 | (5) |
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Variability introduced during sample processing |
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19 | (9) |
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Intra-analytical variability |
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28 | (15) |
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29 | (8) |
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37 | (6) |
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43 | (1) |
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43 | (1) |
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44 | (9) |
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45 | (1) |
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45 | (6) |
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51 | (2) |
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53 | (22) |
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53 | (1) |
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53 | (3) |
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Data handling in metabolomics |
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54 | (2) |
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Standards, models, and formats |
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56 | (4) |
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Initiatives in metabolomics data standards |
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60 | (2) |
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60 | (1) |
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61 | (1) |
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61 | (1) |
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62 | (1) |
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Reporting standards in other fields |
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62 | (2) |
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62 | (2) |
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64 | (1) |
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64 | (2) |
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Issues in metabolomics standards |
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66 | (4) |
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The detailed nature of standards |
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66 | (2) |
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Controlled vocabularies and ontologies |
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68 | (1) |
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69 | (1) |
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70 | (5) |
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70 | (5) |
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The Golm Metabolome Database: a database for GC-MS based metabolite profiling |
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75 | (22) |
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75 | (1) |
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75 | (5) |
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77 | (1) |
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Cheminformatics databases |
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78 | (1) |
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Databases dedicated to metabolite profiling |
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79 | (1) |
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The Golm Metabolome Database (GMD) |
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80 | (1) |
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80 | (1) |
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Information exchange between databases |
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81 | (1) |
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The main work flows of metabolite profiling |
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82 | (5) |
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The metabolite profiling work flow: from sample to metabolite fingerprint and profile |
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83 | (2) |
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The metabolite mapping work flow: from metabolite to specific and selective GC-MS mass fragment |
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85 | (2) |
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The main database objects |
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87 | (3) |
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Modelling the ``MST'' database object |
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87 | (1) |
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Modelling the ``chemical substance'' database object |
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88 | (2) |
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90 | (7) |
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91 | (4) |
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95 | (2) |
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Reconstruction of dynamic network models from metabolite measurements |
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97 | (32) |
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97 | (1) |
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97 | (2) |
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Quantitative measurements of intracellular metabolites |
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99 | (4) |
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Use of metabolite measurements for identification of dynamic models |
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103 | (20) |
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Modular decomposition of the network |
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103 | (15) |
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In silico identification of whole cell metabolite dynamics through evolutionary algorithms and parallel computing |
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118 | (4) |
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Identification of kinetic rate expression from series of steady state observations |
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122 | (1) |
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123 | (6) |
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124 | (5) |
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Toward metabolome-based 13C flux analysis: a universal tool for measuring in vivo metabolic activity |
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129 | (30) |
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129 | (1) |
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129 | (3) |
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Fundamentals of metabolic flux analysis |
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132 | (1) |
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Principles of labeling experiments |
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133 | (2) |
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Current practice of stationary 13C flux analysis |
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135 | (9) |
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135 | (1) |
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From analytes to 13C labeling patterns |
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136 | (2) |
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From 13C labeling patterns to fluxes |
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138 | (6) |
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Toward metabolome-based 13C flux analysis |
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144 | (7) |
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Experimental proof-of-concept |
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144 | (1) |
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Analytics: lessons from metabolomics |
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145 | (2) |
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147 | (4) |
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151 | (8) |
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151 | (1) |
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151 | (6) |
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157 | (2) |
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Data acquisition, analysis, and mining: Integrative tools for discerning metabolic function in Saccharomyces cerevisiae |
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159 | (30) |
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159 | (1) |
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Yeast as a model system for metabolomics |
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159 | (2) |
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Metabolite analysis workflow |
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161 | (1) |
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162 | (3) |
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162 | (1) |
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162 | (1) |
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163 | (2) |
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165 | (1) |
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165 | (12) |
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166 | (3) |
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169 | (6) |
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175 | (1) |
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175 | (1) |
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176 | (1) |
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177 | (3) |
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180 | (9) |
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180 | (1) |
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180 | (9) |
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E. coli metabolomics: capturing the complexity of a ``simple'' model |
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189 | (46) |
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189 | (1) |
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189 | (1) |
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190 | (13) |
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Quenching of metabolism and metabolite extraction |
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191 | (2) |
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Main analytical methods tested with E. coli |
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193 | (5) |
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198 | (3) |
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Combining concentration data with enzyme activity and flux measurements |
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201 | (1) |
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Emerging metabolomic studies in E. coli |
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202 | (1) |
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Evaluating the size of the E. coli metabolome |
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203 | (3) |
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Hints from genome-based models |
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203 | (1) |
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203 | (1) |
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Improving metabolite identification |
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204 | (2) |
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Architecture/anatomy of the E. coli metabolome |
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206 | (1) |
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206 | (1) |
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206 | (1) |
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E. coli metabolomics as a powerful tool for functional genomics |
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207 | (6) |
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208 | (1) |
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Enzyme discovery using non-targeted metabolomics |
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208 | (4) |
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Deorphanizing enzymatic activities and filling-in metabolic pathway holes |
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212 | (1) |
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Phenotype microarrays as reporters of metabolic phenotype |
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212 | (1) |
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Metabolomics to facilitate metabolic engineering of E. coli |
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213 | (2) |
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Metabolomics in flux analysis |
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215 | (1) |
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Adaptive evolution in E. coli, metabolomics, and metabolic phenotype |
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215 | (1) |
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Metabolic models of E. coli: the role of metabolomics |
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216 | (2) |
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218 | (3) |
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Data integration and visualization |
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221 | (1) |
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Future prospects and developments |
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222 | (1) |
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223 | (12) |
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223 | (1) |
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224 | (10) |
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234 | (1) |
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The exo-metabolome in filamentous fungi |
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235 | (18) |
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235 | (1) |
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235 | (1) |
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Exo-metabolome and taxonomy |
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236 | (1) |
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Exo-metabolome and fungal growth |
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237 | (2) |
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Visualisation of the exo-metabolome |
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239 | (1) |
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Extraction of the exo-metabolome |
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240 | (2) |
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Analysis of the exo-metabolome by high performance liquid chromatography |
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242 | (5) |
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Direct infusion electrospray mass spectrometry for profiling |
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247 | (1) |
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Outlook - a polyphasic approach |
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248 | (5) |
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249 | (1) |
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249 | (4) |
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The importance of anatomy and physiology in plant metabolomics |
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253 | (26) |
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253 | (1) |
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253 | (2) |
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253 | (1) |
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254 | (1) |
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255 | (5) |
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255 | (1) |
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256 | (4) |
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Plant physiology - Challenges for plant metabolomics |
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260 | (7) |
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260 | (1) |
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260 | (2) |
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262 | (1) |
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Starch and other storage products |
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262 | (1) |
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263 | (3) |
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266 | (1) |
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Unique aspects of plant research |
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267 | (5) |
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267 | (1) |
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Breeding and QTL analysis |
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268 | (2) |
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270 | (2) |
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Recent, current and future of plant metabolomics |
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272 | (2) |
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272 | (2) |
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274 | (5) |
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274 | (5) |
Index |
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279 | |