Paper Info
Title
Estimating reaction constants in stochastic biological systems with a multi-swarm PSO running on GPUs
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.
Year
DOI
Venue
2012
10.1145/2330784.2330964
GECCO (Companion)
Keywords
Field
DocType
efficient estimation,different experimental condition,multi-swarm pso,stochastic biological system,biological system,gpgpu architecture,discrete-time measurement,molecular amount,multi-swarm formulation,computational cost,estimating reaction constant,parameter estimation method,whole method,discrete time,systems biology,particle swarm optimization,stochastic model,system biology,gpu computing,parameter estimation,biological systems
Particle swarm optimization,Mathematical optimization,Embedding,Swarm behaviour,Computer science,Tau-leaping,Multi-swarm optimization,General-purpose computing on graphics processing units,Stochastic modelling,Artificial intelligence,Estimation theory,Machine learning
Conference
Citations 
PageRank 
References 
10
0.56
3
Authors
5
Name
Order
Citations
PageRank
Marco S. Nobile114323.69
Daniela Besozzi239139.10
Paolo Cazzaniga323527.16
Giancarlo Mauri42106297.38
Dario Pescini527425.92