The article by Oliver Fiehn discusses the integration of genomics, metabolome analysis, and biochemical modeling to understand metabolic networks. It highlights the importance of profiling metabolites, which are often overlooked compared to mRNA and protein profiling. The author outlines various metabolite-oriented approaches, including metabolite target analysis, metabolite profiling, metabolomics, and metabolic fingerprinting, and provides examples and potential applications for each. The article emphasizes the need for comprehensive and quantitative analysis of metabolites to understand metabolic networks, particularly in the context of functional genomics and plant breeding. It also addresses the challenges in sample preparation and data acquisition, and discusses the use of advanced analytical techniques such as mass spectrometry and nuclear magnetic resonance (NMR) for metabolite identification and quantification. The article concludes by emphasizing the importance of integrating experimental data with computational simulations to advance our understanding of metabolic networks and their biological functions.The article by Oliver Fiehn discusses the integration of genomics, metabolome analysis, and biochemical modeling to understand metabolic networks. It highlights the importance of profiling metabolites, which are often overlooked compared to mRNA and protein profiling. The author outlines various metabolite-oriented approaches, including metabolite target analysis, metabolite profiling, metabolomics, and metabolic fingerprinting, and provides examples and potential applications for each. The article emphasizes the need for comprehensive and quantitative analysis of metabolites to understand metabolic networks, particularly in the context of functional genomics and plant breeding. It also addresses the challenges in sample preparation and data acquisition, and discusses the use of advanced analytical techniques such as mass spectrometry and nuclear magnetic resonance (NMR) for metabolite identification and quantification. The article concludes by emphasizing the importance of integrating experimental data with computational simulations to advance our understanding of metabolic networks and their biological functions.