Abstract
In this chapter, we demonstrate the advantage of the simultaneous multicurve nonlinear least-squares analysis over that of the conventional single-curve analysis. Fitting results are subjected to thorough Monte Carlo analysis for rigorous assessment of confidence intervals and parameter correlations. The comparison is performed on a practical example of simulated steady-state reaction kinetics complemented with isothermal calorimetry (ITC) data resembling allosteric behavior of rabbit muscle pyruvate kinase (RMPK). Global analysis improves accuracy and confidence limits of model parameters. Cross-correlation between parameters is also reduced with accompanying enhancement of the model-testing power. This becomes especially important for validation of models with “difficult” highly cross-correlated parameters. We show how proper experimental design and critical evaluation of data can improve the chance of differentiating models.
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Acknowledgments
Supported by NIH GM 775551 and the Robert A. Welch Foundation (JCL) and grant MSM 0021620835 of the Ministry of Education Youth and Sports of the Czech Republic (PH).
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Herman, P., Lee, J.C. (2012). The Advantage of Global Fitting of Data Involving Complex Linked Reactions. In: Fenton, A. (eds) Allostery. Methods in Molecular Biology, vol 796. Springer, New York, NY. https://doi.org/10.1007/978-1-61779-334-9_22
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DOI: https://doi.org/10.1007/978-1-61779-334-9_22
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