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Statistical medium optimization and production of a hyperthermostable lipase from Burkholderia cepacia in a bioreactor
Author(s) -
Rathi P.,
Goswami V.K.,
Sahai V.,
Gupta R.
Publication year - 2002
Publication title -
journal of applied microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.889
H-Index - 156
eISSN - 1365-2672
pISSN - 1364-5072
DOI - 10.1046/j.1365-2672.2002.01780.x
Subject(s) - burkholderia , bioreactor , lipase , pseudomonas , microbiology and biotechnology , food science , biology , chemistry , bacteria , biochemistry , enzyme , botany , genetics
Aim: Statistical medium optimization for maximum production of a hyperthermostable lipase from Burkholderia cepacia and its validation in a bioreactor. Methods and Results:Burkholderia cepacia was grown in shake flasks containing 1% glucose, 0·1% KH 2 PO 4 , 0·5% NH 4 Cl, 0·24% (NH 4 ) 2 HPO 4 , 0·01% MgSO 4 .7H 2 O and 1% emulsified palm oil, at 45 °C and pH 7·0, agitated at 250 rev min −1 with 6‐h‐old inoculum (2% v/v) for 20 h. A fourfold enhancement in lipase production (50 U ml −1 ) and an approximately three fold increase in specific activity (160 U mg −1 ) by B. cepacia was obtained in a 14 litre bioreactor within 15 h after statistical optimization following shake flask culture. The statistical model was obtained using face centred central composite design (FCCCD) with five variables: glucose, palm oil, incubation time, inoculum density and agitation. The model suggested no interactive effect of the five factors, although incubation period, inoculum and carbon concentration were the important variables. Conclusions: The maximum lipase production was 50 U ml −1 , with specific activity 160 U mg −1 protein, in a 14 litre bioreactor after 15 h in a medium obtained after statistical optimization in shake flasks. Further, the model predicted reduction in time for lipase production with reduction in total carbon supply. Significance and Impact of the Study: Statistical optimization allows quick optimization of a large number of variables. It also provides a deep insight into the regulatory role of various parameters involved in enzyme production.