The simulation and analysis of mathematical models of biological systems require a complete knowledge of the reaction kinetic constants. Unfortunately, these values are often difficult to measure, but they can be inferred from experimental data in a process known as Parameter Estimation (PE). In this work, we tackle the PE problem using Particle Swarm Optimization (PSO) coupled with three different reboot strategies, which aim to reinitialize particle positions to avoid local optima. In particular, we highlight the better performance of PSO coupled with the reboot strategies with respect to standard PSO. Finally, since the PE requires a huge number of simulations at each iteration of PSO, we exploit cupSODA, a GPU-powered deterministic simulator, which performs all simulations and fitness evaluations in parallel.

Spolaor, S., Tangherloni, A., Rundo, L., Cazzaniga, P., Nobile, M. (2019). Estimation of kinetic reaction constants: exploiting reboot strategies to improve PSO’s performance. In Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2017 (pp.92-102). Springer Verlag [10.1007/978-3-030-14160-8_10].

Estimation of kinetic reaction constants: exploiting reboot strategies to improve PSO’s performance

Spolaor S.
;
Tangherloni A.;Rundo L.;Cazzaniga P.;Nobile M. S.
2019

Abstract

The simulation and analysis of mathematical models of biological systems require a complete knowledge of the reaction kinetic constants. Unfortunately, these values are often difficult to measure, but they can be inferred from experimental data in a process known as Parameter Estimation (PE). In this work, we tackle the PE problem using Particle Swarm Optimization (PSO) coupled with three different reboot strategies, which aim to reinitialize particle positions to avoid local optima. In particular, we highlight the better performance of PSO coupled with the reboot strategies with respect to standard PSO. Finally, since the PE requires a huge number of simulations at each iteration of PSO, we exploit cupSODA, a GPU-powered deterministic simulator, which performs all simulations and fitness evaluations in parallel.
paper
cupSODA; GPGPU computing; Parameter estimation; Particle swarm optimization; Systems biology;
English
14th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2017
2017
Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2017
978-3-030-14159-2
2019
10834
92
102
none
Spolaor, S., Tangherloni, A., Rundo, L., Cazzaniga, P., Nobile, M. (2019). Estimation of kinetic reaction constants: exploiting reboot strategies to improve PSO’s performance. In Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2017 (pp.92-102). Springer Verlag [10.1007/978-3-030-14160-8_10].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/298370
Citazioni
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
Social impact