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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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.

Original language	English
Title of host publication	Computational Intelligence Methods for Bioinformatics and Biostatistics - 14th International Meeting, CIBB 2017, Revised Selected Papers
Editors	Alberto Policriti, Gunnar W. Klau, Andrea Bracciali, Massimo Bartoletti, Roberto Tagliaferri, Annalisa Barla, Leif Peterson
Place of Publication	Cham
Publisher	Springer
Pages	92-102
Number of pages	11
ISBN (Electronic)	978-3-030-14160-8
ISBN (Print)	978-3-030-14159-2
DOIs	https://doi.org/10.1007/978-3-030-14160-8_10
Publication status	Published - 14 Feb 2019
Event	14th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2017 - Cagliari, Italy Duration: 7 Sept 2017 → 9 Sept 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10834 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2017
Country/Territory	Italy
City	Cagliari
Period	7/09/17 → 9/09/17

Keywords

cupSODA
GPGPU computing
Parameter estimation
Particle swarm optimization
Systems biology

Access to Document

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

Cite this

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 (Eds.), Computational Intelligence Methods for Bioinformatics and Biostatistics - 14th International Meeting, CIBB 2017, Revised Selected Papers (pp. 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. editor / Alberto Policriti ; Gunnar W. Klau ; Andrea Bracciali ; Massimo Bartoletti ; Roberto Tagliaferri ; Annalisa Barla ; Leif Peterson. Cham : Springer, 2019. pp. 92-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0d55208064cf4f0abd5935def7bf0992,

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.}",

year = "2019",

month = feb,

day = "14",

doi = "10.1007/978-3-030-14160-8_10",

language = "English",

isbn = "978-3-030-14159-2",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "92--102",

editor = "Alberto Policriti and Klau, {Gunnar W.} and Andrea Bracciali and Massimo Bartoletti and Roberto Tagliaferri and Annalisa Barla and Leif Peterson",

booktitle = "Computational Intelligence Methods for Bioinformatics and Biostatistics - 14th International Meeting, CIBB 2017, Revised Selected Papers",

address = "Germany",

note = "14th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2017 ; Conference date: 07-09-2017 Through 09-09-2017",

}

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 (eds), 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, pp. 92-102, 14th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2017, Cagliari, Italy, 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. ed. / Alberto Policriti; Gunnar W. Klau; Andrea Bracciali; Massimo Bartoletti; Roberto Tagliaferri; Annalisa Barla; Leif Peterson. Cham: Springer, 2019. p. 92-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10834 LNBI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › 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.

KW - cupSODA

KW - GPGPU computing

KW - Parameter estimation

KW - Particle swarm optimization

KW - Systems biology

UR - http://www.scopus.com/inward/record.url?scp=85063043461&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-14160-8_10

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

M3 - Conference contribution

AN - SCOPUS:85063043461

SN - 978-3-030-14159-2

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

SP - 92

EP - 102

BT - Computational Intelligence Methods for Bioinformatics and Biostatistics - 14th International Meeting, CIBB 2017, Revised Selected Papers

A2 - Policriti, Alberto

A2 - Klau, Gunnar W.

A2 - Bracciali, Andrea

A2 - Bartoletti, Massimo

A2 - Tagliaferri, Roberto

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, editors, Computational Intelligence Methods for Bioinformatics and Biostatistics - 14th International Meeting, CIBB 2017, Revised Selected Papers. Cham: Springer. 2019. p. 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

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this