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The advantage of global fitting of data involving complex linked reactions
P. Herman, JC. Lee,
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, Research Support, N.I.H., Extramural, práce podpořená grantem
- MeSH
- kalorimetrie MeSH
- králíci MeSH
- metoda Monte Carlo MeSH
- metoda nejmenších čtverců MeSH
- pyruvátkinasa chemie metabolismus MeSH
- zvířata MeSH
- Check Tag
- králíci MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
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.
Citace poskytuje Crossref.org
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- $a 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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