proceeding Papers A computational design for high dimensional biochemical experiments


The usual high dimensionality encountered in biochemical experimentation makes the design of these combinatorial experiments challenging. Traditional statistical design of experiments approach may be impracticable when the number of independent variables increases considerably. In this work we present a multi-agent search technique called Particle Swarm Optimization (PSO) as a methodology for designing high dimensional biochemical systems. To study the performance of the proposed algorithm, we compare the PSO approach with a genetic algorithm one on a simulated case related to the vesicle formation in biochemistry.

Paper Details


M. Forlin


6th Workshop on Simulation