A GPU-Based Multi-Swarm PSO Method for Parameter Estimation in Stochastic Biological Systems Exploiting Discrete-Time Target Series

August 1st, 2012


Parameter estimation (PE) of biological systems is one of the most challenging problems in Systems Biology. Here we present a PE method that integrates particle swarm optimization (PSO) to estimate the value of kinetic constants, and a stochastic simulation algorithm to reconstruct the dynamics of the system. The fitness of candidate solutions, corresponding to vectors of reaction constants, is defined as the point-to-point distance between a simulated dynamics and a set of experimental measures, carried out using discrete-time sampling and various initial conditions. A multi-swarm PSO topology with different modalities of particles migration is used to account for the different laboratory conditions in which the experimental data are usually sampled. The whole method has been specifically designed and entirely executed on the GPU to provide a reduction of computational costs. We show the effectiveness of our method and discuss its performances on an enzymatic kinetics and a prokaryotic gene expression network.

(M. Nobile, D. Besozzi, P. Cazzaniga, G. Mauri and D. Pescini: “A GPU-based multi-swarm PSO method for parameter estimation in stochastic biological systems exploiting discrete-time target series”,  in M. Giacobini, L. Vanneschi, W. Bush, editors, Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Springer, vol. 7246 of LNCS. pp. 74-85, 2012. [DOI])