Robust Nonlinear Regression in Enzyme Kinetic Parameters Estimation
| Autor: | Maja Marasović, Mladen Miloš, Tea Marasovic |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Article Subject 030102 biochemistry & molecular biology Chemistry Monte Carlo method Robust statistics Estimator enzyme kinetics Michaelis-Menten robust nonlinear regression General Chemistry lcsh:Chemistry 03 medical and health sciences Nonlinear system Nonlinear regression enzyme kinetics Michaelis-Menten equation 030104 developmental biology lcsh:QD1-999 Linearization Robustness (computer science) Outlier Applied mathematics Nonlinear regression |
| Zdroj: | Journal of Chemistry, Vol 2017 (2017) |
| ISSN: | 2090-9071 2090-9063 |
| Popis: | Accurate estimation of essential enzyme kinetic parameters, such asKmandVmax, is very important in modern biology. To this date, linearization of kinetic equations is still widely established practice for determining these parameters in chemical and enzyme catalysis. Although simplicity of linear optimization is alluring, these methods have certain pitfalls due to which they more often then not result in misleading estimation of enzyme parameters. In order to obtain more accurate predictions of parameter values, the use of nonlinear least-squares fitting techniques is recommended. However, when there are outliers present in the data, these techniques become unreliable. This paper proposes the use of a robust nonlinear regression estimator based on modified Tukey’s biweight function that can provide more resilient results in the presence of outliers and/or influential observations. Real and synthetic kinetic data have been used to test our approach. Monte Carlo simulations are performed to illustrate the efficacy and the robustness of the biweight estimator in comparison with the standard linearization methods and the ordinary least-squares nonlinear regression. We then apply this method to experimental data for the tyrosinase enzyme (EC 1.14.18.1) extracted fromSolanum tuberosum,Agaricus bisporus, andPleurotus ostreatus. The results on both artificial and experimental data clearly show that the proposed robust estimator can be successfully employed to determine accurate values ofKmandVmax. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|