Analysis of steady-state carbon tracer experiments using akaike information criteria
Author:
Funder
National Institutes of Health
Publisher
Springer Science and Business Media LLC
Subject
Clinical Biochemistry,Biochemistry,Endocrinology, Diabetes and Metabolism
Link
https://link.springer.com/content/pdf/10.1007/s11306-021-01807-1.pdf
Reference52 articles.
1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723 https://doi.org/10.1109/tac.1974.1100705
2. Alger, J. R., Sherry, A. D., & Malloy, C. R. (2018). tcaSIM: A simulation program for optimal design of (13)C tracer experiments for analysis of metabolic flux by NMR and mass spectroscopy. Current Metabolomics, 6, 176–187. https://doi.org/10.2174/2213235X07666181219115856.
3. Anderson, D. R. (2008). Model based inference in the life sciences: a primer on evidence. New York, London: Springer.
4. Antoniewicz, M. R., Kelleher, J. K., & Stephanopoulos, G. (2006). Determination of confidence intervals of metabolic fluxes estimated from stable isotope measurements. Metabolic Engineering, 8, 324–37. https://doi.org/10.1016/j.ymben.2006.01.004.
5. Banwarth-Kuhn, M., & Sindi, S. (2020). How and why to build a mathematical model: A case study using prion aggregation. Journal of Biological Chemistry, 295, 5022–5035. https://doi.org/10.1074/jbc.REV119.009851.
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