Parameter estimation for models of chemical reaction networks from experimental data of reaction rates
- Author
- Manvel Gasparyan, Arnout Van Messem (UGent) and Shodhan Rao (UGent)
- Organization
- 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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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8745343
- 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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