Comparison of five kinetic parameter retrieval techniques which are derived from the Michaelis-Menten enzyme kinetic model
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A study of least-squares estimation of Michaelis-Menten kinetic parameters Km and Vmax was performed using five kinetic parameter retrieval techniques. These techniques are derived from five linear plots which are the Direct Linear, Lineweaver-Burk, Eadie- Hofstee, Hanes and Inverse Hanes linear plots. Substrate concentration [S](t) and measured product formation rate V(t) with respect to time were used as the data basis for the system. These data can be obtained by numerical integration of three coupled firstorder ordinary differential equations which represent the Michaelis-Menten mechanism. The problem was to define the most accurate of these techniques, that calculate the Michaelis-Menten parameters Km and Vmax that are closest to one or both theoretical values of Km and Vmax when given Michaelis-Menten substrate concentration and product formation rate that have been perturbed with random noise. The Direct Linear and Eadie-Hofstee are the most accurate retrieval techniques, and the Lineweaver-Burk retrieval technique is the least accurate technique in estimating the Michaelis-Menten parameters Km and Vmax in most cases of all different combinations of the rate constants, values of random data error and numbers of discrete data points chosen in this work without an outlier in perturbed data.