Kinetic isotope effects significantly influence intracellular metabolite [superscript 13]C labeling patterns and flux determination
Name
Stephanopoulos_Kinetic isotope.pdf
Size
935.9 KB
Format
Adobe PDF
Checksum (MD5)
74ce5ebc2b9cc729c8a5502c01007f46
Author(s)
Stephanopoulos, Gregory
Wasylenko, Thomas Michael
Date Issued
August 2013
Journal
Biotechnology Journal
Publisher
Wiley Blackwell
Citation
Wasylenko, Thomas M., and Gregory Stephanopoulos. “Kinetic Isotope Effects Significantly Influence Intracellular Metabolite [superscript 13]C Labeling Patterns and Flux Determination.” Biotechnology Journal 8, no. 9 (August 5, 2013): 1080–1089.
Version
Author's final manuscript
Abstract
Rigorous mathematical modeling of carbon-labeling experiments allows estimation of fluxes through the pathways of central carbon metabolism, yielding powerful information for basic scientific studies as well as for a wide range of applications. However, the mathematical models that have been developed for flux determination from [superscript 13]C labeling data have commonly neglected the influence of kinetic isotope effects on the distribution of [superscript 13]C label in intracellular metabolites, as these effects have often been assumed to be inconsequential. We have used measurements of the [superscript 13]C isotope effects on the pyruvate dehydrogenase enzyme from the literature to model isotopic fractionation at the pyruvate node and quantify the modeling errors expected to result from the assumption that isotope effects are negligible. We show that under some conditions kinetic isotope effects have a significant impact on the [superscript 13]C labeling patterns of intracellular metabolites, and the errors associated with neglecting isotope effects in [superscript 13]C-metabolic flux analysis models can be comparable in size to measurement errors associated with GC–MS. Thus, kinetic isotope effects must be considered in any rigorous assessment of errors in [superscript 13]C labeling data, goodness-of-fit between model and data, confidence intervals of estimated metabolic fluxes, and statistical significance of differences between estimated metabolic flux distributions.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/biot.201200276
