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.
Estimating reaction constants in stochastic biological systems with a multi-swarm PSO running on GPUs / M.S. Nobile, D. Besozzi, P. Cazzaniga, G. Mauri, D. Pescini - In: GECCO Companion '12 : proceedings of the fourteenth International conference on genetic and evolutionary computation conference companion : Philadelphia, PA, USA, july 07 - 11, 2012 / [a cura di] T. Soule. - New York : Association for computing machinery, 2012. - ISBN 9781450311786. - pp. 1421-1422 (( Intervento presentato al 14. convegno International conference on Genetic and evolutionary computation conference companion (GECCO) tenutosi a Philadelphia, USA nel 2012 [10.1145/2330784.2330964].
Estimating reaction constants in stochastic biological systems with a multi-swarm PSO running on GPUs
D. BesozziSecondo
;
2012
Abstract
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.
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