13C Metabolic Flux Analysis: From the Principle to Recent Applications
Luciana Calheiros Gomes,
Metabolic flux analysis (MFA) has become a fundamental tool for quantifying metabolic pathways, which is essential for in-depth understanding of biological systems. In experimentally based MFA, isotopically labeled substrates are supplied to a biological system and the resulting labeling patterns are analyzed to obtain internal flux information. Three main techniques are necessary for 13C MFA: (i) a steady state cell culture in a defined medium with 13C substrates; (ii) precise measurements of the labeling pattern of targeted metabolites by nuclear magnetic resonance (NMR) or mass spectrometry (MS); (iii) mathematical modeling for experimental design, data processing and flux calculation. Recently, important technical advances have been made. The high costs of labeled substrates generate a demand for small cell cultivation volumes. The development of analytical instruments allows the measurement of 13C enrichments with high accuracy and sensitivity. Moreover, powerful flux calculation algorithms have reduced computational efforts. Dynamic labeling experiments are also opening new possibilities for the investigation of specific pathways. While MFA is quite widely established in the study of microbial physiology, it is still a challenge to apply MFA to mammalian cells and plants. However, 13C MFA techniques are continuously enhanced to better discern compartmentalized behaviors, which can help to characterize diseased metabolic states and improve metabolic engineering efforts in plants and other complex systems. The main objective of this work is to present the basic experimental and analytical methods of 13C MFA, as well as representative examples of the latest approaches and findings of MFA in microorganisms, mammalian cells and plants.
Keywords: 13C MFA, dynamic flux analysis, gas chromatography-mass spectrometry, local flux analysis, metabolic engineering, metabolic flux analysis, nuclear magnetic resonance, steady-state, whole isotopomer modeling, optimization
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