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Estimating reaction constants in stochastic biological systems with a multi-swarm PSO running on GPUs

We present a parameter estimation method, based on particle swarm optimization (PSO) and embedding the tau-leaping algorithm, for the efficient estimation of reaction constants in stochastic models of biological systems, using as target a set of discrete-time measurements of molecular amounts sampled in different experimental conditions. To account for the multiplicity of data, we consider a multi-swarm formulation of PSO. The whole method is developed for GPGPU architecture to reduce the computational costs.