Computer simulation and data analysis of effector-target interactions: the extraction of binding parameters from effector and target conjugate frequencies data by using linear and nonlinear data-fitting transformations.
Binding isotherms for effector-target conjugation when effector conjugate frequencies are measured by holding constant the number of effector cells and by varying the number of target cells are characterized by two parameters, the maximum effector conjugate frequency, alpha max, and gamma, which is related to the dissociation constant of the conjugates formed, K d. The suitability of four linear transformations of these binding isotherms, as well as nonlinear data-fitting techniques, to provide estimates of alpha max and gamma is discussed. The strength and weakness of these procedures were investigated by calculating alpha max and gamma from different sets of 100 or 500 replicate "experiments," which were generated by using an algorithm that provides noise contributions to the conjugate frequencies with gaussian distributed errors. Both unweighted and weighted data points were used in these calculations. A similar analysis can also be performed for binding isotherms in which target conjugate frequencies are measured at different values of effector cells by holding constant the number of target cells. In this case, the binding isotherms are characterized by two parameters, the maximum target conjugate frequency, beta max, and delta, which is also related to K d. The results obtained demonstrate that if the experimental conditions are chosen properly, linear transformations and nonlinear fitting techniques provide reliable estimates for the binding parameters. Not all procedures, however, provide estimates with the same accuracy, and special emphasis to this fact must be given if the binding assays are performed at low values of the number of effector cells.
Galvez
J
J
Laboratory of Physical Chemistry, Faculty of Science, Murcia, Spain.
Cabrera
L
L
Lajarin
F
F
Garcia-Peñarrubia
P
P
D000465:Algorithms; D003198:Computer Simulation; D003602:Cytotoxicity, Immunologic / Q000502:physiology*; D006801:Humans; D007111:Immunity, Cellular; D016014:Linear Models; D018448:Models, Immunological; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D015203:Reproducibility of Results