scholarly journals Error sensitivity and optimization of steady-state kinetic parameters using multidimensional chemical kinetic analysis

2018 ◽  
Author(s):  
Joseph S. Eskew ◽  
Christopher G. Connell ◽  
Jared C. Cochran
Keyword(s):  
Experimental Data ◽  
Chemical Kinetic ◽  
Parameter Estimates ◽  
Data Sets ◽  
Raw Data ◽  
Test Error ◽  
Error Sensitivity ◽  
Steady State Kinetic ◽  
Time Domains ◽  
State Kinetic

AbstractEnzyme behavior has been described using the Michaelis-Menten mechanism. The analysis of extended time domains provides a means to extract the Michaelis-Menten constants through direct fitting of raw data. We have developed a scheme for determining Michaelis-Menten rate constants by appropriate fitting of multidimensional experimental data sets to the closed form of the Michaelis-Menten model. We considered how varying parameters in experimental data affect the accuracy of the remaining parameter estimates. We determine how to improve experimental design to achieve a given accuracy, relative to the amount of intrinsic or external error. We analyze this scheme on data sets built around 20 hypothetical and 2 natural enzymes (kinesin and apyrase) to test error sensitivity in different parameter regimes. Overall, we provide evidence that our data fitting regime will tolerate significant experimental error in the raw data and still converge on the four Michaelis-Menten constants.

scholarly journals A novel approach to distinguish between enzyme mechanisms: quasi-steady-state kinetic analysis of the prostaglandin H synthase peroxidase reaction

2003 ◽  
Vol 372 (3) ◽  
pp. 713-724 ◽  
Author(s):  
Peter V. VRZHESHCH ◽  
Elena A. BATANOVA ◽  
Alevtina T. MEVKH ◽  
Sergei D. VARFOLOMEEV ◽  
Irina G. GAZARYAN ◽  
...  
Keyword(s):  
Experimental Data ◽  
Steady State ◽  
Catalytic Cycle ◽  
Prostaglandin H Synthase ◽  
Compound I ◽  
Novel Approach ◽  
Steady State Kinetic ◽  
Enzyme Intermediates ◽  
Prostaglandin H ◽  
State Kinetic

A method of analysis for steady-state kinetic data has been developed that allows relationships between key partial reactions in the catalytic cycle of a functioning enzyme to be determined. The novel approach is based on a concept of scalar and vector ‘kinetic connectivities’ between enzyme intermediates in an arbitrary enzyme mechanism. The criterion for the agreement between experimental data and a proposed kinetic model is formulated as the kinetic connectivity of intermediate forms of the enzyme. This concept has advantages over conventional approaches and is better able to describe the complex kinetic behaviour of prostaglandin H synthase (PGHS) when catalysing the oxidation of adrenaline by H2O2. To interpret the experimental data for PGHS, a generalized model for multi-substrate enzyme reactions was developed with provision for irreversible enzyme inactivation. This model showed that two enzyme intermediates must undergo inactivation during the catalytic cycle. These forms are proposed to be PGHS compound I and a compound I–adrenaline complex.

On the interpretation of age–prevalence curves for trypanosome infections of tsetse flies

Parasitology ◽  
1998 ◽  
Vol 116 (2) ◽  
pp. 149-156 ◽  
Author(s):  
M. E. J. WOOLHOUSE ◽  
J. W. HARGROVE
Keyword(s):  
Experimental Data ◽  
Glossina Pallidipes ◽  
Tsetse Fly ◽  
Tsetse Flies ◽  
Parameter Estimates ◽  
Published Data ◽  
Data Sets ◽  
Trypanosoma Vivax ◽  
Epidemiological Models ◽  
Maturation Period

Epidemiological models are used to analyse 8 published data sets reporting age–prevalence curves for trypanosome infections of the tsetse fly Glossina pallidipes. A model assuming a fixed maturation period and a rate of infection which is independent of fly age is adequate for Trypanosoma vivax-type infections, explaining 98% of observed variance in prevalence by site and age, allowing that the rate of infection may be site dependent. This model is not adequate for T. congolense-type infections and the fit can be improved by allowing (i) the rates of infection to decline with age (although non-teneral flies remain susceptible), (ii) a fraction of resistant flies, which may vary between sites, (iii) increased mortality of infected flies and (iv) variation in the maturation period. Models with these features can explain up to 97% of observed variance. Parameter estimates from published experimental data suggest that all may contribute in practice but that (i) and/or (ii) are likely to be the most important.

scholarly journals Polyconvex hyperelastic modeling of rubberlike materials

2021 ◽  
Vol 43 (7) ◽  
Author(s):  
Cyprian Suchocki ◽  
Stanisław Jemioło
Keyword(s):  
Experimental Data ◽  
Power Law ◽  
Curve Fitting ◽  
Constitutive Relations ◽  
Data Sets ◽  
Power Law Model ◽  
Stored Energy ◽  
Data Approximation ◽  
Exponential Power ◽  
Hyperelastic Models

AbstractIn this work a number of selected, isotropic, invariant-based hyperelastic models are analyzed. The considered constitutive relations of hyperelasticity include the model by Gent (G) and its extension, the so-called generalized Gent model (GG), the exponential-power law model (Exp-PL) and the power law model (PL). The material parameters of the models under study have been identified for eight different experimental data sets. As it has been demonstrated, the much celebrated Gent’s model does not always allow to obtain an acceptable quality of the experimental data approximation. Furthermore, it is observed that the best curve fitting quality is usually achieved when the experimentally derived conditions that were proposed by Rivlin and Saunders are fulfilled. However, it is shown that the conditions by Rivlin and Saunders are in a contradiction with the mathematical requirements of stored energy polyconvexity. A polyconvex stored energy function is assumed in order to ensure the existence of solutions to a properly defined boundary value problem and to avoid non-physical material response. It is found that in the case of the analyzed hyperelastic models the application of polyconvexity conditions leads to only a slight decrease in the curve fitting quality. When the energy polyconvexity is assumed, the best experimental data approximation is usually obtained for the PL model. Among the non-polyconvex hyperelastic models, the best curve fitting results are most frequently achieved for the GG model. However, it is shown that both the G and the GG models are problematic due to the presence of the locking effect.

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