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Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point

Rhamnolipids are bio surfactants which are extra-cellular glycolipids composed of l-rhamnose and 3-hydroxyalkanoics. Rhamnolipids are produced through fermentation process by using Pseudomonas sp. as the species. An alteration to the traditional procedures in order to achieve increase in the product...

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Autores principales: Satya Eswari, J., kavya, Kannekanti
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5112228/
https://www.ncbi.nlm.nih.gov/pubmed/27854078
http://dx.doi.org/10.1186/s13568-016-0279-8
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author Satya Eswari, J.
kavya, Kannekanti
author_facet Satya Eswari, J.
kavya, Kannekanti
author_sort Satya Eswari, J.
collection PubMed
description Rhamnolipids are bio surfactants which are extra-cellular glycolipids composed of l-rhamnose and 3-hydroxyalkanoics. Rhamnolipids are produced through fermentation process by using Pseudomonas sp. as the species. An alteration to the traditional procedures in order to achieve increase in the production of biosurfactants, a numerous process technologies have been adopted in fed batch mode. Fed batch mode facilitates the high production of product by avoiding the substrate or product inhibition line of attack. To overcome the controlling parameters which reduce the product yield, optimal control profiles are designed. In order to develop viable control methods for fed-batch fermentation of Rhamnolipid production, multiple substrate feeding strategies were employed and their efficiencies were compared with different substrates concentration of glucose, nitrogen and phosphorous. The product formation depends upon the substrate feeding strategy and so, the fed-batch fermentation was carried out by using P. aeruginosa providing substrates at manifold rates. With the obtained experimental data, using the kinetic models (logistic equation and by Luedeking Piret), the kinetic parameters were estimated. These kinetic parameters were implemented in tabu search algorithm and this programme was executed in Dev-C++, optimal control profiles were generated as a result. These obtained optimal control profiles have shown an increase in productivity of rhamnolipid with a decline in computational time. Through this procedure, the optimal control profiles of substrate feeding strategies of glucose, nitrogen and phosphorous were estimated. In comparison with other algorithms like genetic algorithm, Tabu Search algorithm was able to generate an accurate optimal control profiles with a reduction in their intricacy.
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spelling pubmed-51122282016-12-02 Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point Satya Eswari, J. kavya, Kannekanti AMB Express Original Article Rhamnolipids are bio surfactants which are extra-cellular glycolipids composed of l-rhamnose and 3-hydroxyalkanoics. Rhamnolipids are produced through fermentation process by using Pseudomonas sp. as the species. An alteration to the traditional procedures in order to achieve increase in the production of biosurfactants, a numerous process technologies have been adopted in fed batch mode. Fed batch mode facilitates the high production of product by avoiding the substrate or product inhibition line of attack. To overcome the controlling parameters which reduce the product yield, optimal control profiles are designed. In order to develop viable control methods for fed-batch fermentation of Rhamnolipid production, multiple substrate feeding strategies were employed and their efficiencies were compared with different substrates concentration of glucose, nitrogen and phosphorous. The product formation depends upon the substrate feeding strategy and so, the fed-batch fermentation was carried out by using P. aeruginosa providing substrates at manifold rates. With the obtained experimental data, using the kinetic models (logistic equation and by Luedeking Piret), the kinetic parameters were estimated. These kinetic parameters were implemented in tabu search algorithm and this programme was executed in Dev-C++, optimal control profiles were generated as a result. These obtained optimal control profiles have shown an increase in productivity of rhamnolipid with a decline in computational time. Through this procedure, the optimal control profiles of substrate feeding strategies of glucose, nitrogen and phosphorous were estimated. In comparison with other algorithms like genetic algorithm, Tabu Search algorithm was able to generate an accurate optimal control profiles with a reduction in their intricacy. Springer Berlin Heidelberg 2016-11-16 /pmc/articles/PMC5112228/ /pubmed/27854078 http://dx.doi.org/10.1186/s13568-016-0279-8 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Article
Satya Eswari, J.
kavya, Kannekanti
Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point
title Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point
title_full Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point
title_fullStr Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point
title_full_unstemmed Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point
title_short Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point
title_sort optimal feed profile for the rhamnolipid kinetic models by using tabu search: metabolic view point
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5112228/
https://www.ncbi.nlm.nih.gov/pubmed/27854078
http://dx.doi.org/10.1186/s13568-016-0279-8
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