Uncertainty Quantification of Michaelis–Menten Kinetic Rates and Its Application to the Analysis of CRISPR‐Based Diagnostics
Year: 2022
Type: article
Abstract: Michaelis–Menten kinetics is an essential model to rationalize enzyme reactions. The quantification of Michaelis–Menten parameters can be very challenging as it is sensitive to even small experimental errors. We here present a quantification of the uncertainty inherent to the experimental determination of kinetic rate parameters for enzymatic reactions. We study the influence of several sources of uncertainty and bias, including the inner filter effect, pipetting errors, number of points in the Michaelis–Menten curve, and flat‐field correction. Using Monte Carlo simulations and analyses of experimental data, we compute typical uncertainties of , , and catalytic efficiency . As a salient example, we analyze the extraction of such parameters for CRISPR‐Cas systems. CRISPR diagnostics have recently attracted much interest and yet reports of these enzymatic kinetic rates have been highly unreliable and inconsistent.
Authors: Alexandre S. Avaro, Juan G. Santiago
Institution: Stanford University
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Funder: Stanford Bio-X
Award: G4887558610
OpenAlex ID: W4296324904