The Experts below are selected from a list of 4473 Experts worldwide ranked by ideXlab platform
Santiago Schnell - One of the best experts on this subject based on the ideXlab platform.
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on the estimation errors of km and v from time course experiments using the michaelis menten Equation
Biophysical Chemistry, 2016Co-Authors: Wylie Stroberg, Santiago SchnellAbstract:Abstract The conditions under which the Michaelis–Menten Equation accurately captures the steady-state kinetics of a simple enzyme-catalyzed reaction is contrasted with the conditions under which the same Equation can be used to estimate parameters, KM and V, from progress curve data. Validity of the underlying assumptions leading to the Michaelis–Menten Equation are shown to be necessary, but not sufficient to guarantee accurate estimation of KM and V. Detailed error analysis and numerical “experiments” show the required experimental conditions for the independent estimation of both KM and V from progress curves. A timescale, tQ, measuring the portion of the time course over which the progress curve exhibits substantial curvature provides a novel criterion for accurate estimation of KM and V from a progress curve experiment. It is found that, if the initial substrate concentration is of the same order of magnitude as KM, the estimated values of the KM and V will correspond to their true values calculated from the microscopic rate constants of the corresponding mass-action system, only so long as the initial enzyme concentration is less than KM.
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validity of the michaelis menten Equation steady state or reactant stationary assumption that is the question
FEBS Journal, 2014Co-Authors: Santiago SchnellAbstract:The Michaelis–Menten Equation is generally used to estimate the kinetic parameters, V and KM, when the steady-state assumption is valid. Following a brief overview of the derivation of the Michaelis–Menten Equation for the single-enzyme, single-substrate reaction, a critical review of the criteria for validity of the steady-state assumption is presented. The application of the steady-state assumption makes the implicit assumption that there is an initial transient during which the substrate concentration remains approximately constant, equal to the initial substrate concentration, while the enzyme–substrate complex concentration builds up. This implicit assumption is known as the reactant stationary assumption. This review presents evidence showing that the reactant stationary assumption is distinct from and independent of the steady-state assumption. Contrary to the widely believed notion that the Michaelis–Menten Equation can always be applied under the steady-state assumption, the reactant stationary assumption is truly the necessary condition for validity of the Michaelis–Menten Equation to estimate kinetic parameters. Therefore, the application of the Michaelis–Menten Equation only leads to accurate estimation of kinetic parameters when it is used under experimental conditions meeting the reactant stationary assumption. The criterion for validity of the reactant stationary assumption does not require the restrictive condition of choosing a substrate concentration that is much higher than the enzyme concentration in initial rate experiments.
Marko Golicnik - One of the best experts on this subject based on the ideXlab platform.
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an alternative explicit model expression equivalent to the integrated michaelis menten Equation and its application to nonlinear saturation pharmacokinetics
Therapeutic Drug Monitoring, 2011Co-Authors: Marko GolicnikAbstract:Background:Many pharmacodynamic processes can be described by the nonlinear saturation kinetics that are most frequently based on the hyperbolic Michaelis-Menten Equation. Thus, various time-dependent solutions for drugs obeying such kinetics can be expressed in terms of the Lambert W(x)-omega funct
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explicit reformulations of time dependent solution for a michaelis menten enzyme reaction model
Analytical Biochemistry, 2010Co-Authors: Marko GolicnikAbstract:The exact closed-form solution to the Michaelis-Menten Equation is expressed in terms of the Lambert W(x) function. However, the utility of this solution is limited because the W(x) function is not widely available in curve-fitting software. Based on various approximations to the W(x) function, different explicit Equations expressed in terms of the elementary functions are proposed here as useful shortcuts to fit time depletion of substrate concentration directly to progress curves using commonly available nonlinear regression computer programs. The results are compared with those obtained by fitting other algebraic Equations that have been proposed previously in the literature.
Chetan T Goudar - One of the best experts on this subject based on the ideXlab platform.
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progress curve analysis for enzyme and microbial kinetic reactions using explicit solutions based on the lambert w function
Journal of Microbiological Methods, 2004Co-Authors: Chetan T Goudar, Steve K Harris, Michael J Mcinerney, Joseph M SuflitaAbstract:Abstract We present a simple method for estimating kinetic parameters from progress curve analysis of biologically catalyzed reactions that reduce to forms analogous to the Michaelis–Menten Equation. Specifically, the Lambert W function is used to obtain explicit, closed-form solutions to differential rate expressions that describe the dynamics of substrate depletion. The explicit nature of the new solutions greatly simplifies nonlinear estimation of the kinetic parameters since numerical techniques such as the Runge–Kutta and Newton–Raphson methods used to solve the differential and integral forms of the kinetic Equations, respectively, are replaced with a simple algebraic expression. The applicability of this approach for estimating Vmax and Km in the Michaelis–Menten Equation was verified using a combination of simulated and experimental progress curve data. For simulated data, final estimates of Vmax and Km were close to the actual values of 1 μM/h and 1 μM, respectively, while the standard errors for these parameter estimates were proportional to the error level in the simulated data sets. The method was also applied to hydrogen depletion experiments by mixed cultures of bacteria in activated sludge resulting in Vmax and Km estimates of 6.531 μM/h and 2.136 μM, respectively. The algebraic nature of this solution, coupled with its relatively high accuracy, makes it an attractive candidate for kinetic parameter estimation from progress curve data.
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parameter estimation using a direct solution of the integrated michaelis menten Equation
Biochimica et Biophysica Acta, 1999Co-Authors: Chetan T Goudar, Jagadeesh R Sonnad, Ronald G DugglebyAbstract:A novel method of estimating enzyme kinetic parameters is presented using the Lambert ω function coupled with non-linear regression. Explicit expressions for the substrate and product concentrations in the integrated Michaelis-Menten Equation were obtained using the ω function which simplified kinetic parameter estimation as root-solving and numerical integration of the Michaelis-Menten Equation were avoided. The ω function was highly accurate in describing the substrate and product concentrations in the integrated Michaelis-Menten Equation with an accuracy of the order of 10−16 when double precision arithmetic was used. Progress curve data from five different experimental systems were used to demonstrate the suitability of the ω function for kinetic parameter estimation. In all cases, the kinetic parameters obtained using the ω function were almost identical to those obtained using the conventional root-solving technique. The availability of highly efficient algorithms makes the computation of ω simpler than root-solving or numerical integration. The accuracy and simplicity of the ω function approach make it an attractive alternative for parameter estimation in enzyme kinetics.
Ronald G Duggleby - One of the best experts on this subject based on the ideXlab platform.
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parameter estimation using a direct solution of the integrated michaelis menten Equation
Biochimica et Biophysica Acta, 1999Co-Authors: Chetan T Goudar, Jagadeesh R Sonnad, Ronald G DugglebyAbstract:A novel method of estimating enzyme kinetic parameters is presented using the Lambert ω function coupled with non-linear regression. Explicit expressions for the substrate and product concentrations in the integrated Michaelis-Menten Equation were obtained using the ω function which simplified kinetic parameter estimation as root-solving and numerical integration of the Michaelis-Menten Equation were avoided. The ω function was highly accurate in describing the substrate and product concentrations in the integrated Michaelis-Menten Equation with an accuracy of the order of 10−16 when double precision arithmetic was used. Progress curve data from five different experimental systems were used to demonstrate the suitability of the ω function for kinetic parameter estimation. In all cases, the kinetic parameters obtained using the ω function were almost identical to those obtained using the conventional root-solving technique. The availability of highly efficient algorithms makes the computation of ω simpler than root-solving or numerical integration. The accuracy and simplicity of the ω function approach make it an attractive alternative for parameter estimation in enzyme kinetics.
Jae Kyoung Kim - One of the best experts on this subject based on the ideXlab platform.
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beyond the michaelis menten Equation accurate and efficient estimation of enzyme kinetic parameters
Scientific Reports, 2017Co-Authors: Boseung Choi, Grzegorz A Rempala, Jae Kyoung KimAbstract:Examining enzyme kinetics is critical for understanding cellular systems and for using enzymes in industry. The Michaelis-Menten Equation has been widely used for over a century to estimate the enzyme kinetic parameters from reaction progress curves of substrates, which is known as the progress curve assay. However, this canonical approach works in limited conditions, such as when there is a large excess of substrate over enzyme. Even when this condition is satisfied, the identifiability of parameters is not always guaranteed, and often not verifiable in practice. To overcome such limitations of the canonical approach for the progress curve assay, here we propose a Bayesian approach based on an Equation derived with the total quasi-steady-state approximation. In contrast to the canonical approach, estimates obtained with this proposed approach exhibit little bias for any combination of enzyme and substrate concentrations. Importantly, unlike the canonical approach, an optimal experiment to identify parameters with certainty can be easily designed without any prior information. Indeed, with this proposed design, the kinetic parameters of diverse enzymes with disparate catalytic efficiencies, such as chymotrypsin, fumarase, and urease, can be accurately and precisely estimated from a minimal amount of timecourse data. A publicly accessible computational package performing such accurate and efficient Bayesian inference for enzyme kinetics is provided.
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beyond the michaelis menten Equation accurate and efficient estimation of enzyme kinetic parameters
bioRxiv, 2017Co-Authors: Boseung Choi, Grzegorz A Rempala, Jae Kyoung KimAbstract:Examining enzyme kinetics is critical for understanding cellular systems and for using enzymes in industry. The Michaelis-Menten Equation has been widely used for over a century to estimate the enzyme kinetic parameters from reaction progress curves of substrates, which is known as the progress curve assay. However, this canonical approach works in limited conditions, such as when there is a large excess of substrate over enzyme. Even when this condition is satisfied, the identifiability of parameters is not always guaranteed, and often not verifiable in practice. To overcome such limitations of the canonical approach for the progress curve assay, here we propose a Bayesian approach based on an Equation derived with the total quasi-steady-state approximation. In contrast to the canonical approach, estimates obtained with this proposed approach exhibit little bias for any combination of enzyme and substrate concentrations. Importantly, unlike the canonical approach, an optimal experiment to identify parameters with certainty can be easily designed without any prior information. Indeed, with this proposed design, the kinetic parameters of diverse enzymes with disparate catalytic efficiencies, such as chymotrypsin, fumarase, and urease, can be accurately and precisely estimated from a minimal amount of timecourse data. A publicly accessible computational package performing the Bayesian inference for such accurate and efficient enzyme kinetics is provided.