Modelling of the immunoassay element of an optical immunosensor

Several factors affect the viability of biosensor design. A computer-based model is being developed to enable the sources and effect of noise and vari ability within the sensor to be analysed. The work now presented details th e modelling of the biochemical aspect of the biosensor model-the immunoassa y. The equilibrium equations that describe the chemical reactions that occur w hen a sample containing the analyte is added to the immunosensor are cast a s a sum of squares function that can be minimized using an optimization pro cedure. The optimization returns the concentrations of each species at equi librium and the procedure is incorporated within a Monte Carlo simulation, which allows the variations in the resulting concentrations to be determine d. Three classes of optimization technique are considered, classical regressio n techniques and two intelligent optimization techniques: simulated anneali ng and genetic algorithms. Several methods of imposing constraints are impl emented and the issue of local minima is discussed. Classical regression pr ocedures were found to be superior to the intelligent optimizations examine d.