Interpreting metabolic complexity via isotope-assisted metabolic flux analysis |
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| Institution: | 1. Department of Bioengineering, University of Maryland, College Park, MD 20742, USA;2. Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA;1. Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO 80045, USA;1. Department of Therapeutic Discovery, Amgen Research, Amgen Inc., South San Francisco, CA 94080, USA;1. Department of Chemistry and Biology ‘A. Zambelli’, University of Salerno, Fisciano, (SA), Italy;2. National Research Council, Institute of Food Science, Avellino, Italy;1. Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA;2. Department of Life and Physical Sciences, Fisk University, Nashville, TN, 37232, USA;3. Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, USA;1. Department of Biomedicine, University of Bergen, Bergen, Norway;2. Department of Biological Sciences, University of Bergen, Bergen, Norway;3. Department of Surgery, Haukeland University Hospital, Bergen, Norway;4. Université Paris Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, France;1. Department of Biochemistry and Biophysics, Penn Center for Genome Integrity, Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA;2. Wallace H. Coulter Department of Biomedical Engineering, Emory University, Atlanta, GA 30322, USA;3. Howard Hughes Medical Institute, University of California at Berkeley, Berkeley, CA 94720, USA;4. Department of Molecular Biology, Princeton University, Washington Road, Princeton, NJ 08544, USA |
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| Abstract: | Isotope-assisted metabolic flux analysis (iMFA) is a powerful method to mathematically determine the metabolic fluxome from experimental isotope labeling data and a metabolic network model. While iMFA was originally developed for industrial biotechnological applications, it is increasingly used to analyze eukaryotic cell metabolism in physiological and pathological states. In this review, we explain how iMFA estimates the intracellular fluxome, including data and network model (inputs), the optimization-based data fitting (process), and the flux map (output). We then describe how iMFA enables analysis of metabolic complexities and discovery of metabolic pathways. Our goal is to expand the use of iMFA in metabolism research, which is essential to maximizing the impact of metabolic experiments and continuing to advance iMFA and biocomputational techniques. |
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