Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/4390
Title: Purification and characterization of intracellular alpha galactosidases from novel Acinetobacter Sp.
Researcher: Sirisha, E
Guide(s): Lakshmi Narasu, M
Keywords: Biotechnology
Enzyme Technology
Intracellular Alpha Galactosidases
Acinetobacter
Upload Date: 24-Aug-2012
University: Jawaharlal Nehru Technological University
Completed Date: June, 2011
Abstract: Glycosyl Hydrolases are a group of carbohydrate enzymes with ability to catalyze hydrolytic cleavage of glycosidic bonds between carbohydrate molecules. Alpha-galactosidase is a glycosyl hydrolase enzyme with a wide range of substrate specificity. The enzyme catalysis the hydrolysis of and#945; -1-6 linked terminal galactose residues from galacto-oligosaccharides and branched polysaccharides. Alphagalactosidases have immense potential applications in different industrial sectors such as food industry, paper and pulp industry, animal feed processing, guar gum processing etc. Besides, alphagalactosidases also play significant role in treatment of X-linked lyosomal storage disorder, Fabry s disease, blood group conversion and xenotransplantation. newlineConsidering the importance of alpha-galactosidase, an intracellular alpha-galactosidase enzyme with broad pH and temperature stability was isolated from novel Acinetobacter sp. Maximum enzyme production (7 U/ml) was obtained when cultures were grown on a medium comprising raffinose 25g/L, tryptone 10g/L, K2HPO4 10g/L, MgSO4.7H2O 1g/L and FeSO4.7H2O 1g/L (pH 7.0) at 36°C, pH 7.0 and agitation speed of 170rpm for 12 hours. Alphagalactosidase production in submerged fermentation was further optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters-pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (trypton) and K2HPO4 were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between fermentation parameters and enzyme yield. The predicted values were further optimized by genetic algorithm (GA). The predictability of neural networks is further analysed by using mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) and coefficient of R2 for training and testing data. Using hybrid neural networks and genetic algorithm, alpha-galactosidase production was increased from 7.5U/ml to 10.2U/ml.
Pagination: xix, 207p.
URI: http://hdl.handle.net/10603/4390
Appears in Departments:Faculty of Biotechnology

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02_certificate.pdf118.02 kBAdobe PDFView/Open
03_declaration.pdf118.05 kBAdobe PDFView/Open
04_acknowledgements.pdf119.77 kBAdobe PDFView/Open
05_abstract.pdf153.83 kBAdobe PDFView/Open
06_table of contents.pdf169.14 kBAdobe PDFView/Open
07_list of tables.pdf128.62 kBAdobe PDFView/Open
08_list of figures.pdf157.91 kBAdobe PDFView/Open
09_list of abbreviations.pdf126.7 kBAdobe PDFView/Open
10_chapter 1.pdf1.2 MBAdobe PDFView/Open
11_chapter 2.pdf1.11 MBAdobe PDFView/Open
12_chapter 3.pdf644 kBAdobe PDFView/Open
13_chapter 4.pdf4.27 MBAdobe PDFView/Open
14_chapter 5.pdf180.16 kBAdobe PDFView/Open
15_chapter 6.pdf271.5 kBAdobe PDFView/Open
16_chapter 7.pdf178.98 kBAdobe PDFView/Open
17_chapter 8.pdf1.57 MBAdobe PDFView/Open


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