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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  1. Barton, H. A., Chiu, W. A., Setzer, R. W., Andersen, M. E., Bailer, A. J., Bois, F. Y., DeWoskin, R. S., Hays, S., Johanson, G., Jones, N., Loizou, G., MacPhail, R. C., Portier, C. J., Spendiff, M., Tan, Y.-M., 10.1093/toxsci/kfm100, Toxicolog. Sci. 99 (2007), 2, 395-402. DOI10.1093/toxsci/kfm100
  2. Bois, F. Y., , Toxicology Letters 120 (2001), 1-3, 385-394. DOI
  3. Cintrón-Arias, A., Banks, H. T., Capaldi, A., Lloyd, A. L., 10.1515/JIIP.2009.034, J. Inverse and Ill-posed Problems 17 (2009), 6. MR2547126DOI10.1515/JIIP.2009.034
  4. Clewell, H., Andersen, M., 10.1177/074823378500100408, Toxicology and Industr. Health 1 (1985), 111-–131. DOI10.1177/074823378500100408
  5. Clewell, H. J., Gentry, P. R., Covington, T. R., Sarangapani, R., Teeguarden, J. G., , Toxicolog. Sci. 79 (2004), 2, :381-393. DOI
  6. Clewell, R., Andersen, M., Barton, H., , Environmental Health Perspectives 110 (2002), 85–-93. DOI
  7. Tebbens, J. Duintjer, Matonoha, C., Matthios, A., Papáček, Š., 10.21136/AM.2019.0284-18, Appl. Math. 64 (2019), 2, 253-277. MR3936970DOI10.21136/AM.2019.0284-18
  8. Galetin, A., Burt, H., Gibbons, L., Houston, J. B., , Drug Metabolism and Disposition 34 (2005), 1, 166-175. DOI
  9. Michal, D. S. Gerhard, Biochemical Pathways: An Atlas of Biochemistry and Molecular Biology. Second edition., Wiley, 2012. 
  10. Gerlowski, L. E., Jain, R. K., , J. Pharmaceut. Sci. 72 (1983), 10, 1103-1127. DOI
  11. Huang, J., Nonlinear output regulation: theory and applications., Advances in design and control. Society for Industrial and Applied Mathematics, Philadelphia, 2004. MR2308004
  12. Jia, G., Stephanopoulos, G., Gunawan, R., , BMC Systems Biology 6 (2012), 142, 1799–-1819. DOI
  13. Krishnan, K., Characterization and Application of Physiologically Based Pharmacokinetic Models in Risk Assessment., Technical Report, World Health Organization, 2010. 
  14. Luke, N. S., DeVito, M. J., Shah, I., El-Masri, H. A., 10.1007/s11538-010-9508-5, Bull. Math. Biol. 72 (2010), 7, 1799–-1819. MR2728006DOI10.1007/s11538-010-9508-5
  15. MATLAB, Simulink Toolbox. Simulation and Model-Based Design., The MathWorks Inc., Natick 2020. 
  16. Papáček, Š., Čelikovský, S., Rehák, B., Štys, D., , Math. Computers Simul. 80 (2010), 6, 1302-1309. MR2610091DOI
  17. Papáček, Š., Lynnyk, V., Rehák, B., Regulatory network of drug-induced enzyme production: parameter estimation based on the periodic dosing response measurement., In: Programs and Algorithms of Numerical Mathematics 20, Institute of Mathematics, Czech Academy of Sciences, 2021. 
  18. Rehák, B., , Asian J. Control 14 (2011), 4, 1150-1154. MR2955458DOI
  19. Rehák, B., Čelikovský, S., , Automatica 44 (2008), 5, 1358-1365. MR2531803DOI
  20. Rehák, B., Čelikovský, S., Papáček, Š., , IEEE Trans. Automat. Control 53 (Special Issue) (2008), 101-108. MR2605133DOI
  21. Rehák, B., Čelikovský, S., Ruiz-León, J., Orozco-Mora, J., A comparison of two FEM-based methods for the solution of the nonlinear output regulation problem., Kybernetika 45 (2009), 427-444. MR2543132
  22. Rostami-Hodjegan, A., Tucker, G., , Drug Discovery Today: Technologies 1 (2004), 4, 441-448. DOI
  23. Rowland, M., Peck, C., Tucker, G., , Ann. Rev. Pharmacology and Toxicology 51 (2011), 1, 45-73. DOI
  24. Sakamoto, N., Rehák, B., Iterative methods to compute center and center-stable manifolds with application to the optimal output regulation problem., In: IEEE Conference on Decision and Control and European Control Conference, 2011. 
  25. Svecova, L., Vrzal, R., Burysek, L., Anzenbacherova, E., Cerveny, L., Grim, J., Trejtnar, F., Kunes, J., Pour, M., Staud, F., Anzenbacher, P., Dvorak, Z., Pavek, P., , Drug Metabolism and Disposition 36 (2007), 2, 339-348. DOI
  26. Zhao, P., Rowland, M., Huang, S.-M., , Clinical Pharmacology and Therapeutics 92 (2012), 17-–20. DOI

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