Error sensitivity and optimization of steady-state kinetic parameters using multidimensional chemical kinetic analysis

Enzyme behavior has been described using the Michaelis-Menten mechanism. The analysis of extended time domains provides a means to extract the Michaelis-Menten constants through direct fitting of raw data. We have developed a scheme for determining Michaelis-Menten rate constants by appropriate fitting of multidimensional experimental data sets to the closed form of the Michaelis-Menten model. We considered how varying parameters in experimental data affect the accuracy of the remaining parameter estimates. We determine how to improve experimental design to achieve a given accuracy, relative to the amount of intrinsic or external error. We analyze this scheme on data sets built around 20 hypothetical and 2 natural enzymes (kinesin and apyrase) to test error sensitivity in different parameter regimes. Overall, we provide evidence that our data fitting regime will tolerate significant experimental error in the raw data and still converge on the four Michaelis-Menten constants.