Convergence dynamics of biochemical models to the global optimum

Ivars Mozga; Egils Stalidzans

Convergence dynamics of biochemical models to the global optimum

Ivars Mozga^*
, Egils Stalidzans

^*Corresponding author for this work

Latvia University of Life Sciences and Technologies

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

4 Citations (Scopus)

Abstract

Stochastic nature of convergence of steady state stochastic global optimization methods causes several seemingly attractive approaches to reduce the length of the optimization procedure. The properties of convergence dynamics of evolutionary programming (EP) and particle swarm (PS) are studied optimizing yeast glycolysis by COPASI software adjusting parameters of one, five, ten and fifteen reactions with five identical runs for each case. Results indicate the potential and risks of shortening the optimization time improving the possibilities of systematic search of adjustable parameter combinations. The choice of optimization method depending on the model size and the number of adjustable parameters should be based on number of tests on the convergence quality, speed and repeatability.

Original language	English
Title of host publication	2011 E-Health and Bioengineering Conference, EHB 2011 - Proceedings
Publication status	Published - 2011
Externally published	Yes
Event	2011 E-Health and Bioengineering Conference, EHB 2011 - Iasi, Romania Duration: 24 Nov 2011 → 26 Nov 2011

Publication series

Name	2011 E-Health and Bioengineering Conference, EHB 2011

Conference

Conference	2011 E-Health and Bioengineering Conference, EHB 2011
Country/Territory	Romania
City	Iasi
Period	24/11/11 → 26/11/11

OECD Field of Science

1.2 Computer and Information Sciences

Keywords

bioprocess design
convergence dynamics
dynamic modelling
kinetic parameters
optimization

Cite this

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title = "Convergence dynamics of biochemical models to the global optimum",

abstract = "Stochastic nature of convergence of steady state stochastic global optimization methods causes several seemingly attractive approaches to reduce the length of the optimization procedure. The properties of convergence dynamics of evolutionary programming (EP) and particle swarm (PS) are studied optimizing yeast glycolysis by COPASI software adjusting parameters of one, five, ten and fifteen reactions with five identical runs for each case. Results indicate the potential and risks of shortening the optimization time improving the possibilities of systematic search of adjustable parameter combinations. The choice of optimization method depending on the model size and the number of adjustable parameters should be based on number of tests on the convergence quality, speed and repeatability.",

keywords = "bioprocess design, convergence dynamics, dynamic modelling, kinetic parameters, optimization",

author = "Ivars Mozga and Egils Stalidzans",

year = "2011",

language = "English",

isbn = "9781457702921",

series = "2011 E-Health and Bioengineering Conference, EHB 2011",

booktitle = "2011 E-Health and Bioengineering Conference, EHB 2011 - Proceedings",

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TY - GEN

T1 - Convergence dynamics of biochemical models to the global optimum

AU - Mozga, Ivars

AU - Stalidzans, Egils

PY - 2011

Y1 - 2011

N2 - Stochastic nature of convergence of steady state stochastic global optimization methods causes several seemingly attractive approaches to reduce the length of the optimization procedure. The properties of convergence dynamics of evolutionary programming (EP) and particle swarm (PS) are studied optimizing yeast glycolysis by COPASI software adjusting parameters of one, five, ten and fifteen reactions with five identical runs for each case. Results indicate the potential and risks of shortening the optimization time improving the possibilities of systematic search of adjustable parameter combinations. The choice of optimization method depending on the model size and the number of adjustable parameters should be based on number of tests on the convergence quality, speed and repeatability.

AB - Stochastic nature of convergence of steady state stochastic global optimization methods causes several seemingly attractive approaches to reduce the length of the optimization procedure. The properties of convergence dynamics of evolutionary programming (EP) and particle swarm (PS) are studied optimizing yeast glycolysis by COPASI software adjusting parameters of one, five, ten and fifteen reactions with five identical runs for each case. Results indicate the potential and risks of shortening the optimization time improving the possibilities of systematic search of adjustable parameter combinations. The choice of optimization method depending on the model size and the number of adjustable parameters should be based on number of tests on the convergence quality, speed and repeatability.

KW - bioprocess design

KW - convergence dynamics

KW - dynamic modelling

KW - kinetic parameters

KW - optimization

UR - https://www.scopus.com/pages/publications/84857878322

M3 - Conference paper

AN - SCOPUS:84857878322

SN - 9781457702921

T3 - 2011 E-Health and Bioengineering Conference, EHB 2011

BT - 2011 E-Health and Bioengineering Conference, EHB 2011 - Proceedings

T2 - 2011 E-Health and Bioengineering Conference, EHB 2011

Y2 - 24 November 2011 through 26 November 2011

ER -

Convergence dynamics of biochemical models to the global optimum

Abstract

Publication series

Conference

OECD Field of Science

Keywords

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