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Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway

Analyzing metabolic pathways in systems biology requires accurate kinetic parameters that represent the simulated in vivo processes. Simulation of the fermentation pathway in the Saccharomyces cerevisiae kinetic model help saves much time in the optimization process. Fitting the simulated model into...

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Autores principales: Ismail, Ahmad Muhaimin, Remli, Muhammad Akmal, Choon, Yee Wen, Nasarudin, Nurul Athirah, Ismail, Nor-Syahidatul N., Ismail, Mohd Arfian, Mohamad, Mohd Saberi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: De Gruyter 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10389048/
https://www.ncbi.nlm.nih.gov/pubmed/37341516
http://dx.doi.org/10.1515/jib-2022-0051
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author Ismail, Ahmad Muhaimin
Remli, Muhammad Akmal
Choon, Yee Wen
Nasarudin, Nurul Athirah
Ismail, Nor-Syahidatul N.
Ismail, Mohd Arfian
Mohamad, Mohd Saberi
author_facet Ismail, Ahmad Muhaimin
Remli, Muhammad Akmal
Choon, Yee Wen
Nasarudin, Nurul Athirah
Ismail, Nor-Syahidatul N.
Ismail, Mohd Arfian
Mohamad, Mohd Saberi
author_sort Ismail, Ahmad Muhaimin
collection PubMed
description Analyzing metabolic pathways in systems biology requires accurate kinetic parameters that represent the simulated in vivo processes. Simulation of the fermentation pathway in the Saccharomyces cerevisiae kinetic model help saves much time in the optimization process. Fitting the simulated model into the experimental data is categorized under the parameter estimation problem. Parameter estimation is conducted to obtain the optimal values for parameters related to the fermentation process. This step is essential because insufficient identification of model parameters can cause erroneous conclusions. The kinetic parameters cannot be measured directly. Therefore, they must be estimated from the experimental data either in vitro or in vivo. Parameter estimation is a challenging task in the biological process due to the complexity and nonlinearity of the model. Therefore, we propose the Artificial Bee Colony algorithm (ABC) to estimate the parameters in the fermentation pathway of S. cerevisiae to obtain more accurate values. A metabolite with a total of six parameters is involved in this article. The experimental results show that ABC outperforms other estimation algorithms and gives more accurate kinetic parameter values for the simulated model. Most of the estimated kinetic parameter values obtained from the proposed algorithm are the closest to the experimental data.
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spelling pubmed-103890482023-08-01 Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway Ismail, Ahmad Muhaimin Remli, Muhammad Akmal Choon, Yee Wen Nasarudin, Nurul Athirah Ismail, Nor-Syahidatul N. Ismail, Mohd Arfian Mohamad, Mohd Saberi J Integr Bioinform Workshop Analyzing metabolic pathways in systems biology requires accurate kinetic parameters that represent the simulated in vivo processes. Simulation of the fermentation pathway in the Saccharomyces cerevisiae kinetic model help saves much time in the optimization process. Fitting the simulated model into the experimental data is categorized under the parameter estimation problem. Parameter estimation is conducted to obtain the optimal values for parameters related to the fermentation process. This step is essential because insufficient identification of model parameters can cause erroneous conclusions. The kinetic parameters cannot be measured directly. Therefore, they must be estimated from the experimental data either in vitro or in vivo. Parameter estimation is a challenging task in the biological process due to the complexity and nonlinearity of the model. Therefore, we propose the Artificial Bee Colony algorithm (ABC) to estimate the parameters in the fermentation pathway of S. cerevisiae to obtain more accurate values. A metabolite with a total of six parameters is involved in this article. The experimental results show that ABC outperforms other estimation algorithms and gives more accurate kinetic parameter values for the simulated model. Most of the estimated kinetic parameter values obtained from the proposed algorithm are the closest to the experimental data. De Gruyter 2023-06-22 /pmc/articles/PMC10389048/ /pubmed/37341516 http://dx.doi.org/10.1515/jib-2022-0051 Text en © 2023 the author(s), published by De Gruyter, Berlin/Boston https://creativecommons.org/licenses/by/4.0/This work is licensed under the Creative Commons Attribution 4.0 International License.
spellingShingle Workshop
Ismail, Ahmad Muhaimin
Remli, Muhammad Akmal
Choon, Yee Wen
Nasarudin, Nurul Athirah
Ismail, Nor-Syahidatul N.
Ismail, Mohd Arfian
Mohamad, Mohd Saberi
Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway
title Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway
title_full Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway
title_fullStr Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway
title_full_unstemmed Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway
title_short Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway
title_sort artificial bee colony algorithm in estimating kinetic parameters for yeast fermentation pathway
topic Workshop
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10389048/
https://www.ncbi.nlm.nih.gov/pubmed/37341516
http://dx.doi.org/10.1515/jib-2022-0051
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