Robust Nonlinear Regression in Enzyme Kinetic Parameters Estimation
Accurate estimation of essential enzyme kinetic parameters, such as Km and Vmax, 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...
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Wiley
2017-01-01
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| Series: | Journal of Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2017/6560983 |
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| author | Maja Marasović Tea Marasović Mladen Miloš |
| author_facet | Maja Marasović Tea Marasović Mladen Miloš |
| author_sort | Maja Marasović |
| collection | DOAJ |
| description | Accurate estimation of essential enzyme kinetic parameters, such as Km and Vmax, 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 from Solanum tuberosum, Agaricus bisporus, and Pleurotus ostreatus. The results on both artificial and experimental data clearly show that the proposed robust estimator can be successfully employed to determine accurate values of Km and Vmax. |
| format | Article |
| id | doaj-art-536dc75a9cb24ea4a28e08780beae5dc |
| institution | OA Journals |
| issn | 2090-9063 2090-9071 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
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| series | Journal of Chemistry |
| spelling | doaj-art-536dc75a9cb24ea4a28e08780beae5dc2025-08-20T02:05:32ZengWileyJournal of Chemistry2090-90632090-90712017-01-01201710.1155/2017/65609836560983Robust Nonlinear Regression in Enzyme Kinetic Parameters EstimationMaja Marasović0Tea Marasović1Mladen Miloš2Faculty of Chemistry and Technology, University of Split, Ruđera Boškovića 35, 21000 Split, CroatiaFaculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Ruđera Boškovića 32, 21000 Split, CroatiaFaculty of Chemistry and Technology, University of Split, Ruđera Boškovića 35, 21000 Split, CroatiaAccurate estimation of essential enzyme kinetic parameters, such as Km and Vmax, 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 from Solanum tuberosum, Agaricus bisporus, and Pleurotus ostreatus. The results on both artificial and experimental data clearly show that the proposed robust estimator can be successfully employed to determine accurate values of Km and Vmax.http://dx.doi.org/10.1155/2017/6560983 |
| spellingShingle | Maja Marasović Tea Marasović Mladen Miloš Robust Nonlinear Regression in Enzyme Kinetic Parameters Estimation Journal of Chemistry |
| title | Robust Nonlinear Regression in Enzyme Kinetic Parameters Estimation |
| title_full | Robust Nonlinear Regression in Enzyme Kinetic Parameters Estimation |
| title_fullStr | Robust Nonlinear Regression in Enzyme Kinetic Parameters Estimation |
| title_full_unstemmed | Robust Nonlinear Regression in Enzyme Kinetic Parameters Estimation |
| title_short | Robust Nonlinear Regression in Enzyme Kinetic Parameters Estimation |
| title_sort | robust nonlinear regression in enzyme kinetic parameters estimation |
| url | http://dx.doi.org/10.1155/2017/6560983 |
| work_keys_str_mv | AT majamarasovic robustnonlinearregressioninenzymekineticparametersestimation AT teamarasovic robustnonlinearregressioninenzymekineticparametersestimation AT mladenmilos robustnonlinearregressioninenzymekineticparametersestimation |