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

Simone Spolaor; Andrea Tangherloni; Leonardo Rundo; Paolo Cazzaniga; Marco S. Nobile

doi:10.1007/978-3-030-14160-8_10

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

Simone Spolaor
, Andrea Tangherloni
, Leonardo Rundo
, Paolo Cazzaniga
, Marco S. Nobile

Information Systems IE&IS

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

Samenvatting

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.

Originele taal-2	Engels
Titel	Computational Intelligence Methods for Bioinformatics and Biostatistics - 14th International Meeting, CIBB 2017, Revised Selected Papers
Redacteuren	Alberto Policriti, Gunnar W. Klau, Andrea Bracciali, Massimo Bartoletti, Roberto Tagliaferri, Annalisa Barla, Leif Peterson
Plaats van productie	Cham
Uitgeverij	Springer
Pagina's	92-102
Aantal pagina's	11
ISBN van elektronische versie	978-3-030-14160-8
ISBN van geprinte versie	978-3-030-14159-2
DOI's	https://doi.org/10.1007/978-3-030-14160-8_10
Status	Gepubliceerd - 14 feb. 2019
Evenement	14th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2017 - Cagliari, Italië Duur: 7 sep. 2017 → 9 sep. 2017

Publicatie series

Naam	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10834 LNBI
ISSN van geprinte versie	0302-9743
ISSN van elektronische versie	1611-3349

Congres

Congres	14th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2017
Land/Regio	Italië
Stad	Cagliari
Periode	7/09/17 → 9/09/17

Financiering

Acknowledgement. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GeForce GTX Titan X GPU used for this research.

Toegang tot document

10.1007/978-3-030-14160-8_10

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Link to publication in Scopus

Citeer dit

Spolaor, S., Tangherloni, A., Rundo, L., Cazzaniga, P., & Nobile, M. S. (2019). Estimation of kinetic reaction constants: exploiting reboot strategies to improve PSO’s performance. In A. Policriti, G. W. Klau, A. Bracciali, M. Bartoletti, R. Tagliaferri, A. Barla, & L. Peterson (editors), Computational Intelligence Methods for Bioinformatics and Biostatistics - 14th International Meeting, CIBB 2017, Revised Selected Papers (blz. 92-102). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10834 LNBI). Springer. https://doi.org/10.1007/978-3-030-14160-8_10

Spolaor, Simone ; Tangherloni, Andrea ; Rundo, Leonardo et al. / Estimation of kinetic reaction constants : exploiting reboot strategies to improve PSO’s performance. Computational Intelligence Methods for Bioinformatics and Biostatistics - 14th International Meeting, CIBB 2017, Revised Selected Papers. redacteur / Alberto Policriti ; Gunnar W. Klau ; Andrea Bracciali ; Massimo Bartoletti ; Roberto Tagliaferri ; Annalisa Barla ; Leif Peterson. Cham : Springer, 2019. blz. 92-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Estimation of kinetic reaction constants: exploiting reboot strategies to improve PSO{\textquoteright}s performance",

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.",

keywords = "cupSODA, GPGPU computing, Parameter estimation, Particle swarm optimization, Systems biology",

author = "Simone Spolaor and Andrea Tangherloni and Leonardo Rundo and Paolo Cazzaniga and Nobile, \{Marco S.\}",

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Spolaor, S, Tangherloni, A, Rundo, L, Cazzaniga, P & Nobile, MS 2019, Estimation of kinetic reaction constants: exploiting reboot strategies to improve PSO’s performance. in A Policriti, GW Klau, A Bracciali, M Bartoletti, R Tagliaferri, A Barla & L Peterson (reds.), Computational Intelligence Methods for Bioinformatics and Biostatistics - 14th International Meeting, CIBB 2017, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10834 LNBI, Springer, Cham, blz. 92-102, 14th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2017, Cagliari, Italië, 7/09/17. https://doi.org/10.1007/978-3-030-14160-8_10

Estimation of kinetic reaction constants: exploiting reboot strategies to improve PSO’s performance. / Spolaor, Simone; Tangherloni, Andrea; Rundo, Leonardo et al.
Computational Intelligence Methods for Bioinformatics and Biostatistics - 14th International Meeting, CIBB 2017, Revised Selected Papers. reds. / Alberto Policriti; Gunnar W. Klau; Andrea Bracciali; Massimo Bartoletti; Roberto Tagliaferri; Annalisa Barla; Leif Peterson. Cham: Springer, 2019. blz. 92-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10834 LNBI).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

TY - GEN

T1 - Estimation of kinetic reaction constants

T2 - 14th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2017

AU - Spolaor, Simone

AU - Tangherloni, Andrea

AU - Rundo, Leonardo

AU - Cazzaniga, Paolo

AU - Nobile, Marco S.

PY - 2019/2/14

Y1 - 2019/2/14

N2 - 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.

AB - 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.

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KW - Particle swarm optimization

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - Klau, Gunnar W.

A2 - Bracciali, Andrea

A2 - Bartoletti, Massimo

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A2 - Barla, Annalisa

A2 - Peterson, Leif

PB - Springer

CY - Cham

Y2 - 7 September 2017 through 9 September 2017

ER -

Spolaor S, Tangherloni A, Rundo L, Cazzaniga P, Nobile MS. Estimation of kinetic reaction constants: exploiting reboot strategies to improve PSO’s performance. In Policriti A, Klau GW, Bracciali A, Bartoletti M, Tagliaferri R, Barla A, Peterson L, redacteurs, Computational Intelligence Methods for Bioinformatics and Biostatistics - 14th International Meeting, CIBB 2017, Revised Selected Papers. Cham: Springer. 2019. blz. 92-102. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-14160-8_10

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

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