Application of response surface methodology and artificial neural networks for optimization of recombinant Oryza sativa non-symbiotic hemoglobin 1 production by escherichia coli in medium containing byproduct glycerol

Bioresource Technology

Volumn 101, Issue 19, 2010, Pages 7537-7544

  Giordano, Pablo C ^a   Martínez, Hugo D ^a   Iglesias, Alberto A ^a   Beccaria, Alejandro J ^a   Goicoechea, Héctor C ^a  

^a UNIVERSIDAD NACIONAL DEL LITORAL   (Argentina)

Author keywords

Artificial neural networks; Byproduct glycerol; Escherichia coli; Recombinant protein; Response surface methodology

Indexed keywords

DEEP NEURAL NETWORKS; ESCHERICHIA COLI; GLYCEROL; HEMOGLOBIN; NEURAL NETWORKS; PROTEINS; SODIUM; SODIUM CHLORIDE; SURFACE PROPERTIES; YEAST;

BIODIESEL PRODUCTION; BIOMASS CONCENTRATIONS; DESIRABILITY FUNCTION; MULTIPLE RESPONSE OPTIMIZATION; PROTEIN CONCENTRATIONS; RESPONSE SURFACE METHODOLOGY; SHAKE-FLASK CULTURES; STATISTICAL PARAMETERS;

RECOMBINANT PROTEINS;

BIODIESEL; GLYCEROL; HEMOGLOBIN 1; RECOMBINANT PROTEIN; UNCLASSIFIED DRUG; HEMOGLOBIN; NON SYMBIOTIC HEMOGLOBIN 1 PROTEIN, RICE; NON-SYMBIOTIC HEMOGLOBIN 1 PROTEIN, RICE; VEGETABLE PROTEIN;

ALCOHOL; ARTIFICIAL NEURAL NETWORK; BIOFUEL; BIOMASS; COLIFORM BACTERIUM; CONCENTRATION (COMPOSITION); DIESEL; HEMOGLOBIN; OPTIMIZATION; PHYSIOLOGICAL RESPONSE; RECOMBINATION; RICE; SODIUM CHLORIDE; SYMBIOSIS; YEAST;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOFUEL PRODUCTION; BIOMASS; CULTURE MEDIUM; ESCHERICHIA COLI; NONHUMAN; PRIORITY JOURNAL; PROTEIN SYNTHESIS; RESPONSE SURFACE METHOD; RICE; BIOSYNTHESIS; BIOTECHNOLOGY; CHEMISTRY; DRUG EFFECT; GENETIC RECOMBINATION; GENETICS; ISOLATION AND PURIFICATION; METABOLISM; METHODOLOGY; REPRODUCIBILITY;

ESCHERICHIA COLI; ORYZA SATIVA;

BIOMASS; BIOTECHNOLOGY; CULTURE MEDIA; ESCHERICHIA COLI; GLYCEROL; HEMOGLOBINS; NEURAL NETWORKS (COMPUTER); ORYZA SATIVA; PLANT PROTEINS; RECOMBINATION, GENETIC; REPRODUCIBILITY OF RESULTS;

EID: 77957743717     PISSN: 09608524     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biortech.2010.05.001     Document Type: Article

References (29)

1. Almeida e Silva, J.B., Lima, U.A., Taqueda, M.E.S., Gauragna, F.G., 2003. Use of response surface methodology for selection of nutrient levels for culturing Paecilomyces variotii in eucalyptus hemicellulosic hydrolysate. Bioresour. Technol. 87, 45-50.
2. 2-binding kinetics of a recombinant protein synthesized in Escherichia coli. Plant Physiol. 115, 1259-1266.
3. Bertani, G., 2004. Lysogeny at mid-twentieth century: P1, P2 and other experimental systems. J. Bacteriol. 186, 595-600.
4. Cheng, S., Song, Q., Wei, D., Gao, B., 2007. High-level production penicillin G acylase from Alcaligenes faecalis in recombinant Escherichia coli with optimization of carbon sources. Enzyme Microbiol. Technol. 41, 326-330.
5. del Brío, B.M., Sanz Molina, A., 2002. Redes neuronales y sistemas difusos. 2da edicion. Alfaomega Grupo Editor.
6. Derringer, G., Suich, R., 1980. Simultaneous optimization of several response variables. J. Qual. Technol. 12, 214-219.
7. Despagne, F., Massart, D.L., 1998. Neural networks in multivariate calibration. Analyst 123, 157R-178R.
8. Didier, C., Etcheverrigaray, M., Kratje, R., Goicoechea, H.C., 2007. Crossed mixture design and multiple response analysis for developing complex culture media used in recombinant protein production. Chemomet. Intell. Lab. Syst. 86, 1-9.
9. Hao, D.C., Zhu, P.H., Yang, S.L., Yang, L., 2006. Optimization of recombinant cytochrome P450 2C9 protein production in Escherichia coli DH5α by statistically-based experimental design. World J. Microbiol. Biotechnol. 22, 1169-1176.
10. Hartley, H.O., 1959. Smallest composite designs for quadratic response surfaces. Biometrics 15, 611-624.
11. Havel, J., Peña, E.M., Rojas-Hernández, A., Doucet, J.-P., Panaye, A., 1998. Neural networks for optimization of high-performance capillary zone electrophoresis methods. A new method using a combination of experimental design and artificial neural networks. J. Chromatogr., A 793, 317-329.
12. Imandi, S.B., Bandaru, V.V.R., Somalanka, S.R., Garapati, H.R., 2007. Optimization of medium constituents for the production of citric acid from byproduct glycerol using Doehlert experimental design. Enzyme Microbiol. Technol. 40, 1367-1372.
13. Laemmli, U.K., 1970. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature 227, 680-685.
14. Lamers, P., McCormick, K., Hilbert, J.A., 2008. The emerging liquid biofuel market in Argentina: implications for domestic demand and international trade. Energy Policy 36, 1479-1490.
15. Leardi, R., 2009. Experimental design in chemistry: a tutorial. Anal. Chim. Acta 652, 161-172.
16. Leonardi, D., Lamas, M.C., Salomón, C.J., Olivieri, A., 2009. Development of novel formulations for Chagas' disease. Optimization of benznidazol chitosan microparticles based on artificial neural networks. Int. J. Pharm. 367, 140-147.
17. Ma, X., Zheng, W., Wang, T., Wei, D., Ma, Y., 2006. Optimization and high-level expression of a functional GST-tagged rHLT-B in Escherichia coli and GM1 binding ability of purified rHLT-B. J. Microbiol. 44, 293-300.
18. Marengo, E., Gianotti, V., Angioni, S., Gennaro, M.C., 2004. Optimization by experimental design and artificial neural networks of the ion-interaction reversed-phase liquid chromatographic separation of twenty cosmetic preservatives. J. Chromatogr., A. 1029, 57-65.
19. Matsui, T., Sato, H., Yamamuro, H., Misawa, S., Shinzato, N., Matsuda, H., Takahashi, J., Sato, S., 2008. High cell density cultivation of recombinant Escherichia coli for hirudin variant 1 production. J. Biotechnol. 134, 88-92.
20. Montgomery, D.C., 1991. Diseño y análisis de experimentos. Grupo Editorial Iberoamericana 3.
21. Moreira, G.A., Micheloud, G.A., Beccaria, A.J., Goicoechea, H.C., 2007. Optimization of the Bacillus thuringiensis var. kurstaki HD-1 d-endotoxins production by using experimental mixture design and artificial neural networks. Biochem. Eng. J. 35, 48-55.
22. Murarka, A., Dharmadi, Y., Yazdani, S.S., Gonzalez, R., 2008. Fermentatine utilization of glycerol by Escherichia coli and its implications for the production of fuels and chemicals. Appl. Environ. Mibrobiol. 74, 1124-1135.
23. Myers, R.H., Montgomery, D.C., 1995. Response Surface Methodology. JohnWiley & Sons Inc., New York.
24. Nikerel, E., Oner, E., Kirdar, B., Yildirim, R., 2006. Optimization of medium composition for biomass production of recombinant Escherichia coli cells using response surface methodology. Biochem. Eng. J. 32, 1-6.
25. Ramchuran, S.O., Holst, O., Nordberg Karlsson, E., 2005. Effect of postinduction nutrient feed composition and use of lactose as inducer during production of thermostable xylanase in Escherichia coli glucose-limited fed-batch cultivations. J. Biosci. Bioeng. 99, 477-484.
26. Ren, X., Yu, D., Han, S., Feng, Y., 2006. Optimization of recombinant hyperthermophilic esterase production from agricultural waste using response surface methodology. Bioresour. Technol. 97, 2345-2349.
27. Tabandeh, F., Khodabandeh, M., Yakhchali, B., 2008. Response surface methodology for optimizing the induction conditions of recombinant interferon beta during high cell density culture. Chem. Eng. Sci. 63, 2477-2483.
28. Yazdani, S.S., Gonzalez, R., 2007. Anaerobic fermentation of glycerol: a path to economic viability for the biofuels industry. Curr. Opin. Biotechnol. 18, 213-219.
29. Zhao, J., Wang, Y., Chu, J., Zhang, S., Zhuang, Y., 2008. Statistical optimization of medium for the production of pyruvate oxidase by the recombinant Escherichia coli. J. Ind. Microbiol. Biotechnol. 35, 257-262.