Biochemical network of drug-induced enzyme production: Parameter estimation based on the periodic dosing response measurement

Volodymyr Lynnyk; Štěpán Papáček; Branislav Rehák

Kybernetika (2021)

  • Volume: 57, Issue: 6, page 1005-1018
  • ISSN: 0023-5954

Abstract

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The well-known bottleneck of systems pharmacology, i. e., systems biology applied to pharmacology, refers to the model parameters determination from experimentally measured datasets. This paper represents the development of our earlier studies devoted to inverse (ill-posed) problems of model parameters identification. The key feature of this research is the introduction of control (or periodic forcing by an input signal being a drug intake) of the nonlinear model of drug-induced enzyme production in the form of a system of ordinary differential equations. First, we tested the model features under periodic dosing, and subsequently, we provided an innovative method for a parameter estimation based on the periodic dosing response measurement. A numerical example approved the satisfactory behavior of the proposed algorithm.

How to cite

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Lynnyk, Volodymyr, Papáček, Štěpán, and Rehák, Branislav. "Biochemical network of drug-induced enzyme production: Parameter estimation based on the periodic dosing response measurement." Kybernetika 57.6 (2021): 1005-1018. <http://eudml.org/doc/297696>.

@article{Lynnyk2021,
abstract = {The well-known bottleneck of systems pharmacology, i. e., systems biology applied to pharmacology, refers to the model parameters determination from experimentally measured datasets. This paper represents the development of our earlier studies devoted to inverse (ill-posed) problems of model parameters identification. The key feature of this research is the introduction of control (or periodic forcing by an input signal being a drug intake) of the nonlinear model of drug-induced enzyme production in the form of a system of ordinary differential equations. First, we tested the model features under periodic dosing, and subsequently, we provided an innovative method for a parameter estimation based on the periodic dosing response measurement. A numerical example approved the satisfactory behavior of the proposed algorithm.},
author = {Lynnyk, Volodymyr, Papáček, Štěpán, Rehák, Branislav},
journal = {Kybernetika},
keywords = {dynamical system; systems pharmacology; biochemical network; input-output regulation; parameter estimation; fast Fourier transform},
language = {eng},
number = {6},
pages = {1005-1018},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Biochemical network of drug-induced enzyme production: Parameter estimation based on the periodic dosing response measurement},
url = {http://eudml.org/doc/297696},
volume = {57},
year = {2021},
}

TY - JOUR
AU - Lynnyk, Volodymyr
AU - Papáček, Štěpán
AU - Rehák, Branislav
TI - Biochemical network of drug-induced enzyme production: Parameter estimation based on the periodic dosing response measurement
JO - Kybernetika
PY - 2021
PB - Institute of Information Theory and Automation AS CR
VL - 57
IS - 6
SP - 1005
EP - 1018
AB - The well-known bottleneck of systems pharmacology, i. e., systems biology applied to pharmacology, refers to the model parameters determination from experimentally measured datasets. This paper represents the development of our earlier studies devoted to inverse (ill-posed) problems of model parameters identification. The key feature of this research is the introduction of control (or periodic forcing by an input signal being a drug intake) of the nonlinear model of drug-induced enzyme production in the form of a system of ordinary differential equations. First, we tested the model features under periodic dosing, and subsequently, we provided an innovative method for a parameter estimation based on the periodic dosing response measurement. A numerical example approved the satisfactory behavior of the proposed algorithm.
LA - eng
KW - dynamical system; systems pharmacology; biochemical network; input-output regulation; parameter estimation; fast Fourier transform
UR - http://eudml.org/doc/297696
ER -

References

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