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Parameter estimation for models of chemical reaction networks from experimental data of reaction rates

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Abstract
For the purpose of precise mathematical modelling of chemical reaction networks, useful techniques for estimating their parameters from experimental data are necessary. In this manuscript, we propose a new parameter estimation method for enzymatic chemical reaction networks from time-series experimental data of reaction rates. The main idea is based on retrieving time-series data of the species' concentrations from the available experimental data of reaction rates by making use of parametric Bezier curves. The least-squares method is applied to these retrieved data in order to determine the best-fitting values of the parameters in the corresponding mathematical model. Subsequently, we demonstrate the applicability of our parameter estimation method on three examples of enzymatic chemical reaction networks, including a model of ryanodine receptor adaptation and a model of protein kinase cascades. We also address the issue of identifiability of chemical reaction network models from reaction rates.
Keywords
Computer Science Applications, Control and Systems Engineering, Systems biology, bottom-up modelling approach, system identification, Bezier curves, least squares method, IDENTIFIABILITY, SYSTEMS, IDENTIFICATION, REDUCTION

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MLA
Gasparyan, Manvel, et al. “Parameter Estimation for Models of Chemical Reaction Networks from Experimental Data of Reaction Rates.” INTERNATIONAL JOURNAL OF CONTROL, vol. 96, no. 2, 2023, pp. 392–407, doi:10.1080/00207179.2021.1998636.
APA
Gasparyan, M., Van Messem, A., & Rao, S. (2023). Parameter estimation for models of chemical reaction networks from experimental data of reaction rates. INTERNATIONAL JOURNAL OF CONTROL, 96(2), 392–407. https://doi.org/10.1080/00207179.2021.1998636
Chicago author-date
Gasparyan, Manvel, Arnout Van Messem, and Shodhan Rao. 2023. “Parameter Estimation for Models of Chemical Reaction Networks from Experimental Data of Reaction Rates.” INTERNATIONAL JOURNAL OF CONTROL 96 (2): 392–407. https://doi.org/10.1080/00207179.2021.1998636.
Chicago author-date (all authors)
Gasparyan, Manvel, Arnout Van Messem, and Shodhan Rao. 2023. “Parameter Estimation for Models of Chemical Reaction Networks from Experimental Data of Reaction Rates.” INTERNATIONAL JOURNAL OF CONTROL 96 (2): 392–407. doi:10.1080/00207179.2021.1998636.
Vancouver
1.
Gasparyan M, Van Messem A, Rao S. Parameter estimation for models of chemical reaction networks from experimental data of reaction rates. INTERNATIONAL JOURNAL OF CONTROL. 2023;96(2):392–407.
IEEE
[1]
M. Gasparyan, A. Van Messem, and S. Rao, “Parameter estimation for models of chemical reaction networks from experimental data of reaction rates,” INTERNATIONAL JOURNAL OF CONTROL, vol. 96, no. 2, pp. 392–407, 2023.
@article{8745343,
  abstract     = {{For the purpose of precise mathematical modelling of chemical reaction networks, useful techniques for estimating their parameters from experimental data are necessary. In this manuscript, we propose a new parameter estimation method for enzymatic chemical reaction networks from time-series experimental data of reaction rates. The main idea is based on retrieving time-series data of the species' concentrations from the available experimental data of reaction rates by making use of parametric Bezier curves. The least-squares method is applied to these retrieved data in order to determine the best-fitting values of the parameters in the corresponding mathematical model. Subsequently, we demonstrate the applicability of our parameter estimation method on three examples of enzymatic chemical reaction networks, including a model of ryanodine receptor adaptation and a model of protein kinase cascades. We also address the issue of identifiability of chemical reaction network models from reaction rates.}},
  author       = {{Gasparyan, Manvel and Van Messem, Arnout and Rao, Shodhan}},
  issn         = {{0020-7179}},
  journal      = {{INTERNATIONAL JOURNAL OF CONTROL}},
  keywords     = {{Computer Science Applications,Control and Systems Engineering,Systems biology,bottom-up modelling approach,system identification,Bezier curves,least squares method,IDENTIFIABILITY,SYSTEMS,IDENTIFICATION,REDUCTION}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{392--407}},
  title        = {{Parameter estimation for models of chemical reaction networks from experimental data of reaction rates}},
  url          = {{http://doi.org/10.1080/00207179.2021.1998636}},
  volume       = {{96}},
  year         = {{2023}},
}

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