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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
