Abstract
Although the Michaelis–Menten (MM) rate law has been widely used to estimate enzyme kinetic parameters, it works only under the condition of extremely low enzyme concentration. Furthermore, even when this condition is satisfied, parameter estimation is often imprecise due to the parameter identifiability issue. To overcome these limitations of the canonical approach to enzyme kinetics, we developed a Bayesian approach based on a modified form of the MM rate law, which is derived with the total quasi-steady state approximation. Here, we illustrate how to perform the Bayesian inference for the progress curve assay with our user-friendly computational R package. We also describe an optimal experimental design for the progress curve assay, with which enzyme kinetic parameters can be accurately and precisely estimated from minimal measurements of the progress curves.
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Acknowledgments
This work was supported by Global Ph. D. Fellowship Program 2019H1A2A1075303 (H.H.), (NRF-2017R1D1A3B03031008 (B.C.), NRF-2020R1F1A1A01066082 (B.C.), National Research Foundation of Korea grants NRF-2016 RICIB 3008468 (J.K.K.), the Institute for Basic Science IBS-R029-C3 (J.K.K.), and the TJ Park Science Fellowship of the POSCO TJ Park Foundation (J.K.K.).
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Hong, H., Choi, B., Kim, J.K. (2022). Beyond the Michaelis–Menten: Bayesian Inference for Enzyme Kinetic Analysis. In: Vanhaelen, Q. (eds) Computational Methods for Estimating the Kinetic Parameters of Biological Systems. Methods in Molecular Biology, vol 2385. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1767-0_3
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DOI: https://doi.org/10.1007/978-1-0716-1767-0_3
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