Optimal design of nanoengineered implantable optical sensors using a genetic algorithm

A genetic algorithm as a design tool for optimized optical glucose sensors is presented. These proposed sensors are fabricated by assembling ultrathin polyelectrolyte films on the surface of calcium alginate microspheres containing glucose oxidase and an oxygen-quenched ruthenium fluorophore. The sensors are rendered ratiometric by inclusion of a complementary reference fluorophore via polyelectrolyte-dye conjugates. The genetic algorithm, in conjunction with a computational model of the chemical sensor, selects the optimal values for diffusivities of glucose and oxygen in the polyelectrolyte films, the enzyme concentration, microsphere radius, and film thickness that give the optimum sensor response. The values given by the genetic algorithm will be used to design future sensor prototypes.