Paper Info
Title
A GPU-Based multi-swarm PSO method for parameter estimation in stochastic biological systems exploiting discrete-time target series
Abstract
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.
Year
DOI
Venue
2012
10.1007/978-3-642-29066-4_7
EvoBIO
Keywords
Field
DocType
pe method,different laboratory condition,gpu-based multi-swarm pso method,different modality,stochastic biological system,biological system,experimental data,experimental measure,multi-swarm pso topology,candidate solution,discrete-time target series,parameter estimation,systems biology,whole method
Swarm behaviour,Computer science,SIMD,Artificial intelligence,Estimation theory,Stochastic simulation,Particle swarm optimization,Mathematical optimization,Algorithm,Systems biology,Sampling (statistics),Discrete time and continuous time,Machine learning
Conference
Citations 
PageRank 
References 
20
1.13
7
Authors
5
Name
Order
Citations
PageRank
Marco S. Nobile114323.69
Daniela Besozzi239139.10
Paolo Cazzaniga323527.16
Giancarlo Mauri42106297.38
Dario Pescini527425.92