Compară metode

	Analiza metabolomică în serii de timp	Analiza Metabolomică
Domeniu	Bioinformatică	Bioinformatică
Familie	Process / pipeline	Process / pipeline
Anul apariției	2000s–2010s	1998–2002
Autorul original	Developed from general metabolomics workflows; longitudinal extensions pioneered by A. K. Smilde, R. Bino, and colleagues	Oliver et al. (coining of 'metabolomics'); Oliver Fiehn (systematic framework)
Tip	Quantitative longitudinal omics pipeline	Quantitative omics pipeline
Sursa seminală	Smilde, A. K., van der Werf, M. J., Bijlsma, S., van der Werff-van der Vat, B. J. C., & Jellema, R. H. (2005). Fusion of mass spectrometry-based metabolomics data. Analytical Chemistry, 77(20), 6729–6736.	Fiehn, O. (2002). Metabolomics — the link between genotypes and phenotypes. Plant Molecular Biology, 48(1-2), 155–171.
Denumiri alternative	longitudinal metabolomics, dynamic metabolomics, temporal metabolome profiling, kinetic metabolomics	metabolome profiling, metabolic profiling, metabonomics, metabolite profiling
Înrudite	6	6
Rezumat	Time-series metabolomics analysis profiles small-molecule metabolites from biological samples collected at multiple, ordered time points, enabling researchers to capture the dynamic flux of metabolic pathways in response to stimuli, disease progression, drug treatment, or developmental change. By integrating longitudinal statistical models with standard metabolomics preprocessing, the approach goes beyond a static metabolic snapshot to reveal how, when, and in what sequence metabolic responses unfold.	Metabolomics analysis is the large-scale, systematic measurement of small-molecule metabolites in a biological sample to characterise the metabolome — the complete set of metabolic intermediates and products present under defined conditions. By coupling high-throughput analytical platforms such as mass spectrometry (MS) or nuclear magnetic resonance (NMR) spectroscopy with multivariate statistics and pathway databases, metabolomics bridges the genotype–phenotype gap and captures the downstream functional output of genes, transcripts, and proteins in real time.