Kinetics of Explicit, Closed-form Solutions to Enzyme Progress Curves

Ref.: S. Schnell and C. Mendoza, Journal of Theoretical Biology, 187 (2): 207-212 (1997).

All users of EZ-Fit-IMM are advised to download the latest updated version of Software. The latest version includes revisions and 'bug' fixes. Users with an active license can download and activate the software with their current activation code.

EZ-Fit-IMM performs nonlinear regression curve-fitting of data to integrated Michaelis-Menten progress curve enzyme models selected from in internal library of 6 models. EZ-Fit-IMM models also adjusts for background response and any reaction time offset.

EZ-Fit-IMM is priced to make it affordable to professionals and students alike. But don't let the low price mislead you. EZ-Fit-IMM is robust and powerful using Quadratic Programming, Differential Evolution and Conjugate Gradient regression methods. Why pay hundreds and yearly license fees when you can have curve-fitting software for a fraction of the cost. Best of all, there are no time limit restrictions or number of uses.

With just a couple of clicks of the mouse, EZ-Fit-IMM simplifies curve fitting your data to six mathematical models. Select one or three equations from an internal library of 6 models, and EZ-Fit-IMM does the rest by fitting the curve to your data set, displaying the statistical results, model parameters, the best fit curve, initial rate plot, curvature inflection point and the residuals.

The EZ-Fit-IMM results of your curve-fit includes the fitted parameters, their standard errors , confidence limits, correlation matrix, gradients, and corrected Akaike value.

Explicit, Closed-form Solutions to Enzyme Progress Curves

EZ-Fit-IMM is a software program for estimating kinetic parameters from progress curve data analysis of enzyme catalyzed reactions that reduce to forms analogous to the Michaelis–Menten equation. The method uses the Lambert Omega W function to obtain explicit, closed-form solutions to progress curves of substrate depletion or product accumulation (Schnell and Mendoza, 1997). The explicit nature of this approach simplifies nonlinear estimation of the kinetic parameters since basic algebraic expressions are used. The applicability of this approach for estimating Vmax and Km using the integrated Michaelis–Menten equation was verified using experimental progress curve data of substrate depletion or product accumulation determinations over time rather than initial velocity–substrate concentration data pairs to estimate Vmax and Km. Using the Km and Vmax values, it then calculates the maximum curvature and suggests a relevant range of points from the progress curve on both sides of the point of inflection. For the method used in EZ-Fit-IMM, the recommendation initial substrate concentration [So] should be in the range of Km/2 to 2Km.

Reference:

1- Analysis of progress curves for enzyme-catalyzed reactions: application to unstable enzymes, coupled reactions and transient-state kinetics, Biochim. Biophys. Acta, 1205 (2): 268-274 (1994), R.G. Duggleby

2- Analysis of enzyme progress curves by non-linear regression Methods in Enzymology, 249: 61-90 (1995), R.G. Duggleby

3- Closed form solution for time-dependent enzyme kinetics Journal of Theoretical Biology, 187 (2): 207-212 (1997) S. Schnell and C. Mendoza

4- A Century of Enzyme Kinetics: Reliability of the Km and Vmax Estimates. Comments on Theoretical Biology, 8: 169–187 (2003), S. Schnell and P.K. Maini

5- Progress curve analysis for enzyme and microbial kinetic reactions using explicit solutions based on the Lambert W function, Journal of Microbiological Methods, 59 (3): 317-326 (2004), C.T. Goudar, S.K. Harris, M.J. McInervey, and J.M. Sulflita

6- Exact and Approximate Solutions for the Decades-old Michaelis-Menten Equation: Progress-curve Analysis Through Integrated Rate Equations, Biochemistry and Molecular Biology, 39 (2): 117–125 (2011), Marko Golicnik

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