Moiz Bilal, Li Andrew, Padmanabhan Surya, Sriram Ganesh, Clyne Alisa Morss
Department of Bioengineering, University of Maryland, College Park, MD 20742, USA.
Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA.
Metabolites. 2022 Nov 4;12(11):1066. doi: 10.3390/metabo12111066.
Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.
细胞代谢代表了在健康和疾病状态下基因、蛋白质和代谢物发生的协调变化。代谢通量组包括细胞内和细胞外的代谢反应速率（通量），因此它提供了对细胞表型的强大而综合的描述。然而，细胞内通量无法直接测量。相反，通量定量需要对来自同位素标记实验的数据进行复杂的数学和计算分析。在本综述中，我们描述了同位素辅助代谢通量分析（iMFA），这是一种用于通量组定量的严格计算方法，它整合代谢网络模型和实验数据以生成定量代谢通量图。我们强调了在哺乳动物模型中实施iMFA的实际考虑因素，以及iMFA在生理学和疾病的体外和体内研究中的应用。最后，我们确定了iMFA中充满希望的新前沿领域，这可能使我们能够充分释放iMFA在生物医学研究中的潜力。
