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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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On model ensemble analyses of nonmonotonic data.

Tomas Radivoyevitch1, Charles A Kunos

  • 1Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 44106, USA. txr24@case.edu

Nucleosides, Nucleotides & Nucleic Acids
|February 7, 2012
PubMed
Summary

To improve nonlinear model fitting for mammalian ribonucleotide reductase (RNR) activity, this study proposes a novel parameter estimation method. This approach successfully rescues nonmonotonic fits, preventing false model rejections in RNR ATP activity analysis.

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Area of Science:

  • Biochemistry
  • Enzymology
  • Computational Biology

Background:

  • Mammalian ribonucleotide reductase (RNR) exhibits complex, nonmonotonic activity in response to varying ATP concentrations.
  • Automated model fitting processes can incorrectly reject valid nonmonotonic models due to poor initial parameter estimates, leading to false model rejections.
  • This issue is particularly relevant when analyzing RNR activity data, which often displays nonmonotonic behavior.

Purpose of the Study:

  • To develop and validate a method for improving the initial parameter estimation in nonlinear model fitting for RNR activity.
  • To rescue potentially valid nonmonotonic fits that might otherwise be rejected.
  • To investigate the fitting of RNR activity troughs using models with and without a third ATP binding site.

Main Methods:

  • Proposed a novel approach using final parameter estimates from neighboring models as initial estimates for subsequent fits.
  • Defined model neighbors based on differences in represented complexes (differing by at most one ligand).
  • Applied this method to analyze RNR activity versus ATP data, focusing on nonmonotonic behavior.

Main Results:

  • The proposed method successfully rescues fits that would otherwise be classified as monotonic and poorly fitting.
  • Demonstrated that troughs in RNR activity versus ATP can be fitted effectively by models with differing ATP binding site requirements.
  • Showed that the choice of initial parameter values significantly impacts the outcome of automated model fitting.

Conclusions:

  • The proposed parameter estimation strategy enhances the reliability of automated nonlinear model fitting for complex biological systems like RNR.
  • This method allows for a more accurate assessment of underlying reaction mechanisms, distinguishing between models that require different numbers of ATP binding sites.
  • Accurate initial parameter estimation is crucial for avoiding false model rejections and for robustly characterizing enzyme kinetics.