TY  - CHAP
AU  - David Roche
AU  - Debora Gil
AU  - Jesus Giraldo
PY  - 2014//
TI  - Mathematical modeling of G protein-coupled receptor function: What can we learn from empirical and mechanistic models?
BT  - G Protein-Coupled Receptors – Modeling and Simulation Advances in Experimental Medicine and Biology
SP  - 159
EP  - 181
VL  - 796
IS  - 3
PB  - Springer Netherlands
KW  - β-arrestin
KW  - biased agonism
KW  - curve fitting
KW  - empirical modeling
KW  - evolutionary algorithm
KW  - functional selectivity
KW  - G protein
KW  - GPCR
KW  - Hill coefficient
KW  - intrinsic efficacy
KW  - inverse agonism
KW  - mathematical modeling
KW  - mechanistic modeling
KW  - operational model
KW  - parameter optimization
KW  - receptor dimer
KW  - receptor oligomerization
KW  - receptor constitutive activity
KW  - signal transduction
KW  - two-state model
N2  - 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.
SN  - 0065-2598
SN  - 978-94-007-7422-3
UR  - http://dx.doi.org/10.1007/978-94-007-7423-0_8
N1  - IAM; 600.075
ID  - David Roche2014
ER  -