Mathematical modeling of G protein-coupled receptor function: What can we learn from empirical and mechanistic models?

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Empirical and mechanistic models differ in their approaches to the analysis of pharmacological effect. Whereas the parameters of the former are not physical constants those of the latter embody the nature, often complex, of biology. Empirical models are exclusively used for curve fitting, merely to characterize the shape of the E/[A] curves. Mechanistic models, on the contrary, enable the examination of mechanistic hypotheses by parameter simulation. Regretfully, the many parameters that mechanistic models may include can represent a great difficulty for curve fitting, representing, thus, a challenge for computational method development. In the present study some empirical and mechanistic models are shown and the connections, which may appear in a number of cases between them, are analyzed from the curves they yield. It may be concluded that systematic and careful curve shape analysis can be extremely useful for the understanding of receptor function, ligand classification and drug discovery, thus providing a common language for the communication between pharmacologists and medicinal chemists. © Springer Science+Business Media Dordrecht 2014.
Original languageEnglish
Pages (from-to)159-181
JournalAdvances in Experimental Medicine and Biology
Publication statusPublished - 1 Jan 2014


  • Biased agonism
  • Curve fitting
  • Empirical modeling
  • Evolutionary algorithm
  • Functional selectivity
  • G protein
  • GPCR
  • Hill coefficient
  • Intrinsic efficacy
  • Inverse agonism
  • Mathematical modeling
  • Mechanistic modeling
  • Operational model
  • Parameter optimization
  • Receptor constitutive activity
  • Receptor dimer
  • Receptor oligomerization
  • Signal transduction
  • Two-state model
  • β-arrestin


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