Ahmad Muhaimin Ismail, Muhammad Akmal Remli, Yee Wen Choon, Nurul Athirah Nasarudin, Nor-Syahidatul N Ismail, Mohd Arfian Ismail, Mohd Saberi Mohamad
{"title":"酵母发酵路径动力学参数估计中的人工蜂群算法。","authors":"Ahmad Muhaimin Ismail, Muhammad Akmal Remli, Yee Wen Choon, Nurul Athirah Nasarudin, Nor-Syahidatul N Ismail, Mohd Arfian Ismail, Mohd Saberi Mohamad","doi":"10.1515/jib-2022-0051","DOIUrl":null,"url":null,"abstract":"<p><p>Analyzing metabolic pathways in systems biology requires accurate kinetic parameters that represent the simulated <i>in vivo</i> processes. Simulation of the fermentation pathway in the <i>Saccharomyces cerevisiae</i> 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 <i>in vitro</i> or <i>in vivo</i>. 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 <i>S. cerevisiae</i> 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.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10389048/pdf/","citationCount":"0","resultStr":"{\"title\":\"Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway.\",\"authors\":\"Ahmad Muhaimin Ismail, Muhammad Akmal Remli, Yee Wen Choon, Nurul Athirah Nasarudin, Nor-Syahidatul N Ismail, Mohd Arfian Ismail, Mohd Saberi Mohamad\",\"doi\":\"10.1515/jib-2022-0051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Analyzing metabolic pathways in systems biology requires accurate kinetic parameters that represent the simulated <i>in vivo</i> processes. Simulation of the fermentation pathway in the <i>Saccharomyces cerevisiae</i> 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 <i>in vitro</i> or <i>in vivo</i>. 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 <i>S. cerevisiae</i> 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.</p>\",\"PeriodicalId\":53625,\"journal\":{\"name\":\"Journal of Integrative Bioinformatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10389048/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Integrative Bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1515/jib-2022-0051\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Integrative Bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/jib-2022-0051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
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 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.