Utilization of integrated Michaelis-Menten equations for enzyme inhibition diagnosis and determination of kinetic constants using Solver supplement of Microsoft Office Excel

Computer Methods and Programs in Biomedicine

Volumn 109, Issue 1, 2013, Pages 26-31

  Bezerra, Rui M F ^a   Fraga, Irene ^b   Dias, Albino A ^a  

^a UNIVERSITY OF TRÁS OS MONTES AND ALTO DOURO   (Portugal)

^b UNIVERSITY OF TRÁS OS MONTES AND ALTO DOURO   (Portugal)

Author keywords

Akaike information criterion; Diagnosis of enzyme inhibition; Integrated Michaelis Menten equations; Nonlinear regression; Solver

Indexed keywords

AKAIKE INFORMATION CRITERION; ALKALINE PHOSPHATASE; EXCEL; INITIAL RATE; INTEGRATED MICHAELIS-MENTEN EQUATION; INTEGRATED MODELS; KINETIC CONSTANT; KINETIC EQUATIONS; MICHAELIS-MENTEN EQUATIONS; MICROSOFT OFFICE; NON-LINEAR REGRESSION; PRODUCT CONCENTRATION; PROGRESS CURVES; REACTION CATALYZED; SOLVER; SUM OF SQUARE ERRORS;

INTEGRAL EQUATIONS; KINETICS; PHOSPHATASES; RATE CONSTANTS;

NONLINEAR EQUATIONS;

ARTICLE; COMPUTER PROGRAM; ENZYME INHIBITION; ENZYME KINETICS; MICHAELIS MENTEN KINETICS; NONLINEAR REGRESSION ANALYSIS; STATISTICAL MODEL;

ALKALINE PHOSPHATASE; CATALYSIS; ENZYME INHIBITORS; ENZYMES; KINETICS; NITROPHENOLS; ORGANOPHOSPHORUS COMPOUNDS; SOFTWARE;

EID: 84869877898     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2012.08.017     Document Type: Article

References (17)

1. Orsi B.A., Tipton K.F. Kinetic analysis of progress curves. Methods Enzymol. 1979, 63:159-183.
2. Duggleby R.G. Quantitative analysis of the time courses of enzyme-catalyzed reactions. Methods 2001, 24:168-174.
3. Fernly H.N. Statistical estimations in enzyme kinetics: the integrated Michaelis-Menten. Eur. Biochem. J. 1974, 43:377-378.
4. m and V from a single enzyme reaction progress curve. Biochim. Biophys. Acta 1977, 480:1-13.
5. Liao F., Tian K.-C., Yang X., Zhou Q.-X., Zeng Z.-C., Zuo Y.-P. Kinetic substrate quantification by fitting the enzyme reaction curve to the integrated Michaelis-Menten equation. Anal. Bioanal. Chem. 2003, 375:756-762.
6. Goličnik M. Exact and approximate solutions for the Decades-old Michaelis-Menten equation: progress-curve analysis through integrated rate equations. Biochem. Mol. Biol. Edu. 2011, 39:117-125.
7. Bezerra R.M.F., Dias A.A. Utilization of integrated Michaelis-Menten equation to determine kinetic constants. Biochem. Mol. Biol. Educ. 2007, 35:145-150.
8. Myung I.J., Pitt M.A. Model comparison methods. Methods Enzymol. 2004, 383:351-366.
9. Motulsky H., Christopoulos A. Fitting Models to Biological Data Using Linear and Nonlinear Regression, a Practical Guide to Curve Fitting 2003, GraphPad Software Inc.
10. Harris D.C. Nonlinear least-squares curve fitting with Microsoft Excel Solver. J. Chem. Educ. 1998, 75:119-121.
11. Kemmer G., Keller S. Nonlinear least-squares data fitting in Excel spreadsheets. Nat. Protoc. 2010, 5:267-281.
12. Mannervik B. Regression analysis, experimental error and statistical criteria in the design and analysis of experiments for discrimination between rival kinetic models. Methods Enzymol. 1982, 87:370-391.
13. Akaike H. A new look at the statistical model identification. IEEE Trans. Automat. Contr. 1974, 19:716-723.
14. Pitt M.A., Myung I.J. When a good fit can be bad. Trends Cogn. Sci. 2002, 6:421-425.
15. Howell J.A., Stuck J.D. Kinetics of solka floc cellulose hydrolysis by Trichoderma viride cellulase. Biotechnol. Bioeng. 1975, 17:873-893.
16. T.-A. Hsu, Enzymatic hydrolysis of cellulose: the effect of solvent cadoxen pretreatment and the kinetics of soluble cellulose-cellobiosylhydrolase hydrolysis, PhD thesis. Purdue University, West Lafayette, IN, 1979.
17. Bezerra R.M.F., Dias A.A., Fraga I., Pereira A.N. Cellulose hydrolysis by cellobiohydrolase Cel7A shows mixed hyperbolic product inhibition. Appl. Biochem. Biotechnol. 2011, 165:178-189.