Response surface methodology and artificial neural network modeling of an aqueous two-phase system for purification of a recombinant alkaline active xylanase
(2016) In Process Biochemistry 51(3). p.452-462- Abstract
- A two-stage polyethylene glycol (PEG)-phosphate aqueous two-phase system was used for purification of a highly thermostable and alkaline active recombinant xylanase. Response surface methodology (RSM) and artificial neural network (ANN) have been used to develop predictive models for simulation and optimization of purification process. Effects of pH, PEG molecular weight, concentrations of phosphate, PEG and NaCl on the partitioning of the target enzyme and the contaminants were studied using a central composite design of experiments. The best first stage purification was achieved using 6% PEG 6000, 20% phosphate and pH 6. The optimum back extraction stage system consist of 10% phosphate, 10% NaCl, pH 10 and the first stage separation top... (More)
- A two-stage polyethylene glycol (PEG)-phosphate aqueous two-phase system was used for purification of a highly thermostable and alkaline active recombinant xylanase. Response surface methodology (RSM) and artificial neural network (ANN) have been used to develop predictive models for simulation and optimization of purification process. Effects of pH, PEG molecular weight, concentrations of phosphate, PEG and NaCl on the partitioning of the target enzyme and the contaminants were studied using a central composite design of experiments. The best first stage purification was achieved using 6% PEG 6000, 20% phosphate and pH 6. The optimum back extraction stage system consist of 10% phosphate, 10% NaCl, pH 10 and the first stage separation top phase. After the two stage phase separations, about 78% of the original enzyme activity was recovered and the specific activity of the purified enzyme was increased by a factor of 6.7. Also, the aqueous two-phase system was scaled-up 100 times. After back-extraction, the specific activity increased 6.56 times with 72% total yield. A similar design was also used to obtain a training set for ANN. A comparison between the model results and experimental data gave high correlation coefficient (R2) and showed that both models were able to predict the partitioning behavior. The results demonstrated a higher prediction accuracy of ANN compared to RSM. This superiority of ANN over other multi factorial approaches could make this estimation technique a very helpful tool for purification process. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/bf137da9-d4d0-4341-9872-b56f235cb2e8
- author
- Rahimpour, Farshad LU ; Hatti-Kaul, Rajni LU and Mamo, Gashaw LU
- organization
- publishing date
- 2016
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Process Biochemistry
- volume
- 51
- issue
- 3
- pages
- 452 - 462
- publisher
- Elsevier
- external identifiers
-
- scopus:84958120827
- ISSN
- 1359-5113
- DOI
- 10.1016/j.procbio.2015.12.018
- language
- English
- LU publication?
- yes
- id
- bf137da9-d4d0-4341-9872-b56f235cb2e8
- date added to LUP
- 2019-06-27 22:45:58
- date last changed
- 2022-03-25 21:19:28
@article{bf137da9-d4d0-4341-9872-b56f235cb2e8,
abstract = {{A two-stage polyethylene glycol (PEG)-phosphate aqueous two-phase system was used for purification of a highly thermostable and alkaline active recombinant xylanase. Response surface methodology (RSM) and artificial neural network (ANN) have been used to develop predictive models for simulation and optimization of purification process. Effects of pH, PEG molecular weight, concentrations of phosphate, PEG and NaCl on the partitioning of the target enzyme and the contaminants were studied using a central composite design of experiments. The best first stage purification was achieved using 6% PEG 6000, 20% phosphate and pH 6. The optimum back extraction stage system consist of 10% phosphate, 10% NaCl, pH 10 and the first stage separation top phase. After the two stage phase separations, about 78% of the original enzyme activity was recovered and the specific activity of the purified enzyme was increased by a factor of 6.7. Also, the aqueous two-phase system was scaled-up 100 times. After back-extraction, the specific activity increased 6.56 times with 72% total yield. A similar design was also used to obtain a training set for ANN. A comparison between the model results and experimental data gave high correlation coefficient (R2) and showed that both models were able to predict the partitioning behavior. The results demonstrated a higher prediction accuracy of ANN compared to RSM. This superiority of ANN over other multi factorial approaches could make this estimation technique a very helpful tool for purification process.}},
author = {{Rahimpour, Farshad and Hatti-Kaul, Rajni and Mamo, Gashaw}},
issn = {{1359-5113}},
language = {{eng}},
number = {{3}},
pages = {{452--462}},
publisher = {{Elsevier}},
series = {{Process Biochemistry}},
title = {{Response surface methodology and artificial neural network modeling of an aqueous two-phase system for purification of a recombinant alkaline active xylanase}},
url = {{http://dx.doi.org/10.1016/j.procbio.2015.12.018}},
doi = {{10.1016/j.procbio.2015.12.018}},
volume = {{51}},
year = {{2016}},
}