Statistical optimization of medium for the production of recombinant hirudin from Saccharomyces cerevisiae using response surface methodology

Rao, K. Jagannadha; Kim, Chul Ho; Rhee, Shin Hyung

doi:10.1016/s0032-9592(99)00129-6

Cited by 135 publications

(68 citation statements)

References 7 publications

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“…RSM is a statistical modeling method applied for multiple regression analysis using the obtained quantitative data for designed experiment for solving the multivariable equations (Jagannadha Rao et al, 2000). It determines the optimum conditions of Pseudonocardia endophytica VUK-10 for the production of the L-asparaginase under a wide array of physical conditions.…”

Section: Optimization Of Screened Medium Components By Response Surfamentioning

confidence: 99%

Optimization of culture conditions by Response Surface Methodology and Unstructured kinetic modeling for L-Asparaginase production by Pseudonocardia endophytica VUK-10

Managamuri¹,

Ganduri²,

Poda³

et al. 2017

J App Pharm Sci

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L-asparaginase is an anti-tumor enzyme and widely accepted as chemotherapeutic agent which has activity against acute lymphoblastic leukemia. The current study targets the production of L-asparaginase by Pseudonocardia endophytica VUK-10 by a statistically designed model. Experiments were performed according to central composite design of RSM with five independent variables such as time, pH, temperature, concentrations of maltose and L-asparagine concentration for optimization. All the five conditions had significant interaction with other variables for the maximum response (L-asparaginase production). Maximum L-asparaginase production was recorded as 7.42 IU/ml slightly higher than the model predicted value of 6.8 IU/ml, from statistical optimization studies. An unstructured kinetic model was proposed to depict the profiles of biomass, substrate utilization and L-asparaginase production in optimized medium under shake flask level. The logistic and Leudeking-Piret expressions were modified to predict the kinetic model parameters (µmax, X0, Xmax, α, β, γ and η) and we found that L-asparaginase production was growth-associated. High significant correlation (R 2 ) values of 0.86, 0.96 and 0.94 were observed with the experimental and predicted results for Pseudonocardia endophytica VUK-10 growth, L-asparaginase activity and Maltose utilization, respectively. The results obtained from medium optimization using RSM and unstructured mathematical models describe the L-asparaginase fermentation kinetics more effectively.

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Section: Optimization Of Screened Medium Components By Response Surfamentioning

confidence: 99%

Optimization of culture conditions by Response Surface Methodology and Unstructured kinetic modeling for L-Asparaginase production by Pseudonocardia endophytica VUK-10

Managamuri¹,

Ganduri²,

Poda³

et al. 2017

J App Pharm Sci

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“…RSM is an empirical statistical modeling technique employed for multiple regression analysis using quantitative data obtained from properly designed experiments to solve multivariate equations simultaneously [20,21]. RSM is used to determine the optimum density, for the nanofluids.…”

Section: Rsm Optimization Processmentioning

confidence: 99%

The RSM approach to develop a new correlation for density of metal-oxide aqueous nanofluids

Montazer

Salami

Yarmand

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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“…The present work aims to optimize the factors (nutrition and culture conditions) directly responsible for rhamnolipid production using statistical designs and response surface methodology (RSM) for higher yield. RSM is use in various experiments for optimization of products such as bacterial medium composition [22], enzymatic activity [23,24] algal growth [25] etc.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Nutrient Requirements and Culture Conditions for the Production of Rhamnolipid from Pseudomonas aeruginosa (MTCC 7815) using Mesua ferrea Seed Oil

2013

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Environmental awareness has led to a serious consideration for biological surfactants and hence nonedible vegetable oils may serve as a substitute carbon source for bio-surfactant production (rhamnolipid) which might be an alternative to complex synthetic surfactants. There are reports of rhamnolipid production from plant based oil giving higher production than that of glucose because of their hydrophobicity and high carbon content. Therefore the contribution of non-edible oil such as Mesua ferrea seed oil could serve as a good carbon source for rhamnolipid production. Moreover the use of rhamnolipid production from non-edible plant based seed oil has not been reported elsewhere. The present work focus on the optimal production of rhamnolipid by considering both micro and macro nutrients and culture conditions using response surface methodology. The study observes that micronutrients play a significant role in rhamnolipid production from Pseudomonas aeruginosa (MTCC 7815). The investigation results with the statistically optimize parameters able to produce a higher rhamnolipid production and this methodology could be used to optimize the nutrients requirements and culture conditions. The present findings would assist in bioremediation of crude oil contaminated ecosystems.

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Statistical optimization of medium for the production of recombinant hirudin from Saccharomyces cerevisiae using response surface methodology

Cited by 135 publications

References 7 publications

Optimization of culture conditions by Response Surface Methodology and Unstructured kinetic modeling for L-Asparaginase production by Pseudonocardia endophytica VUK-10

Optimization of culture conditions by Response Surface Methodology and Unstructured kinetic modeling for L-Asparaginase production by Pseudonocardia endophytica VUK-10

The RSM approach to develop a new correlation for density of metal-oxide aqueous nanofluids

Optimization of Nutrient Requirements and Culture Conditions for the Production of Rhamnolipid from Pseudomonas aeruginosa (MTCC 7815) using Mesua ferrea Seed Oil

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