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

Abstract

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.

Microbial production of rhamnolipids: opportunities, challenges and strategies

“…glucose) [ 29 , 66 ]. The feeding profile of substrates, especially the carbon source, is also an important parameter as fed-batch cultivation is found to be the more effective than batch cultivation to obtain high rhamnolipid titers [ 57 , 65 – 67 ]. The key to fed-batch cultivation is to control the substrate concentration at a minimal level that allows optimal microbial growth without catabolite repression or substrate inhibition, and most substrates are utilized for rhamnolipid formation rather than biomass formation.…”

“…The key to fed-batch cultivation is to control the substrate concentration at a minimal level that allows optimal microbial growth without catabolite repression or substrate inhibition, and most substrates are utilized for rhamnolipid formation rather than biomass formation. Therefore, kinetic models for substrate utilization, product formation and microbial growth will be useful in developing fed-batch fermentation strategies to dramatically improve the rhamnolipid yield, as evident in several studies [ 64 , 67 , 68 ]. Controlling pH during fermentation also helps in achieving higher rhamnolipid production.…”

Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors

“…Unlike logistic regression, BNs allow for estimating the subsequent probability of any target variable given any set of conditioning variables 15 , 16 , which can predict the probability of having dyslipidemia in a more flexible manner. Tabu search is a metaheuristic approach proposed by Glover and it is one of the most efficient optimization techniques that incorporates adaptive memory to escape local search and find the global optimum 17 . Hence, we applied BNs optimized with a tabu search algorithm to jointly model dyslipidemia and its related factors and determine how these factors impact dyslipidemia, to offer comprehensive strategies for effectively reducing the incidence of hyperlipidemia.…”

Application of a Tabu search-based Bayesian network in identifying factors related to hypertension

“…[11,12] Tabu search is an efficient global optimization technique that incorporates adaptive memory to move beyond a local search to find the global optimum. [13] This method avoids repetition of the same solutions by maintaining a mechanism called a “Tabu list” and activates good solutions using aspiration criteria. [13] In recent years, the Tabu search algorithm has often been applied in a variety of fields because of its advantages, including solving global optimization problems.…”

“…[13] This method avoids repetition of the same solutions by maintaining a mechanism called a “Tabu list” and activates good solutions using aspiration criteria. [13] In recent years, the Tabu search algorithm has often been applied in a variety of fields because of its advantages, including solving global optimization problems. Therefore, we used BNs optimized with a Tabu search algorithm to model hypertension and related factors and determine how these factors were related to each other.…”