Optimizing medium composition for TaqI endonuclease

production by recombinant Escherichia coli cells

using response surface methodology

I. Emrah Nikerela, Ebru Toksoyb, Betul Kırdara, Ramazan Yıldırıma,*

aDepartment of Chemical Engineering, Bogazici University, 34342 Bebek-Istanbul, TurkeybDepartment of Chemical Engineering, Marmara University, 80815 Goztepe-Istanbul, Turkey

Received 24 October 2003; accepted 9 June 2004

www.elsevier.com/locate/procbio

Process Biochemistry 40 (2005) 1633–1639

Abstract

The effect of medium composition on the TaqI endonuclease production, by recombinant Escherichia coli cells carrying a plasmid

encoding TaqI endonuclease, was investigated using response surface methodology. The concentration of glucose, di-ammonium hydrogen

phosphate, potassium di-hydrogen and magnesium sulphate in media were changed according to a central composite rotatable design

consisting of 29 experiments and enzyme yields were determined. The results were fitted to a second order polynomial with an R2 of 0.828.

The model equation was then optimized using the Nelder–Mead simplex method to maximize enzyme yield within the experimental range

studied. The optimum medium composition was found to be 6 g L�1 glucose, 1.5 g L�1 (NH4)2HPO4, 8 g L�1 KH2PO4, and 0.8 g L�1

MgSO4�7H2O. The model prediction of 179 � 106 U g DCW�1 enzyme yield at optimum conditions was experimentally verified. This value

is higher than any value obtained in the initial experiments as well as in the previously reported studies. The response surface methodology

was found to be useful in improving the production of recombinant TaqI endonuclease in E. coli.

# 2004 Elsevier Ltd. All rights reserved.

Keywords: TaqI endonuclease; Medium design; Recombinant enzyme production; Response surface methodology; Statistical optimization

1. Introduction

The emergence of a new biotechnological industry utiliz-

ing recombinant DNA technologies for the production of

highly specific biomolecules has increased the demand for

sophisticated tools such as restriction enzymes, which

enable the researchers to cut and paste DNA fragments.

The TaqI restriction-modification system of the thermo-

philic Gram-negative eubacterium Thermus aquaticus YT-1

consists of a type II restriction endonuclease that is highly

specific to the double stranded palindromic target sequence

50-TCGA-30 and TaqI methylase enzyme. TaqI methylase

modifies the adenine residue on each strand of the same

sequence by transferring a methyl group from its cofactor

* Corresponding author. Tel.: +90-212-3581540x2248;

fax: +90-212-2872460.

E-mail address: yildirra@boun.edu.tr (R. Yıldırım).

0032-9592/$ – see front matter # 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.procbio.2004.06.017

S-adenosyl-L-methionine. Since the optimum temperature

for the growth of this bacterium is around 70 8C, the TaqI

endonuclease is capable of cleaving DNA at the specific sites

at temperatures markedly higher than other restriction endo-

nucleases [1]. In addition, enzymes from thermophiles are

more stable at conventional temperatures than those from

mesophiles, and have prolonged shelf lives. These enzymes

are extensively used in many molecular biology and bio-

technology applications.

The isolation of TaqI restriction endonuclease from

Thermus aquaticus is associated with technical difficulties

such as enzyme loss during downstream processing, and

high operation costs. Therefore, the cloning of the gene

encoding TaqI endonuclease into Escherichia coli is con-

sidered to be advantageous for high-level production of this

enzyme.

The extracellular production of TaqI restriction endonu-

clease as a fusion protein by recombinant E. coli cells has

I.E. Nikerel et al. / Process Biochemistry 40 (2005) 1633–16391634

been reported [2] and stability and extracellular secretion of

the fusion protein were further improved by the co-expres-

sion of TaqI methylase [3]. Cytoplasmic expression of TaqI

endonuclease in recombinant E. coli under the control of

various promoters has also been reported [4–6]. Higher

levels of TaqI endonuclease production were obtained by

the cloning of TaqI endonuclease gene under the control of

strong T7 phage promoter. Co-expression of methylase

activity under the control of a constitutive promoter, either

by cloning the TaqI methylase gene on a second plasmid or

by constructing a recombinant plasmid harboring both the

endonuclease and methylase genes, greatly improved the

yield and specific TaqI endonuclease productivity of recom-

binant cells [6].

Investigation of the fermentation characteristics of these

recombinant cells in different media both in shake flasks

and bioreactors suggested that the careful optimization of

medium composition may possibly lead to higher produc-

tivity in terms of TaqI endonuclease activity.

The optimization of fermentation conditions is an impor-

tant problem in the development of economically feasible

bioprocesses. Combinatorial interactions of medium com-

ponents with the cell metabolism and the production of the

desired compound are numerous and the optimum processes

may be developed using an effective experimental design

procedure [7]. Response surface methodology (RSM),

which is a collection of statistical techniques for designing

Table 1

TaqI endonuclease production by recombinant E. coli in different media

Med number Glucose (NH4)2HPO4 KH2PO4 MgSO4 E

M1 �1(10) �1(2.5) �1(10) �1(0.5)

M2 1(30) �1(2.5) �1(10) �1(0.5)

M3 �1(10) 1(7.5) �1(10) �1(0.5)

M4 1(30) 1(7.5) �1(10) �1(0.5)

M5 �1(10) �1(2.5) 1(20) �1(0.5)

M6 1(30) �1(2.5) 1(20) �1(0.5)

M7 �1(10) 1(7.5) 1(20) �1(0.5)

M8 1(30) 1(7.5) 1(20) �1(0.5)

M9 �1(10) �1(2.5) �1(10) 1(1.5)

M10 1(30) �1(2.5) �1(10) 1(1.5)

M11 �1(10) 1(7.5) �1(10) 1(1.5)

M12 1(30) 1(7.5) �1(10) 1(1.5)

M13 �1(10) �1(2.5) 1(20) 1(1.5)

M14 1(30) �1(2.5) 1(20) 1(1.5)

M15 �1(10) 1(7.5) 1(20) 1(1.5)

M16 1(30) 1(7.5) 1(20) 1(1.5)

M17 0(20) 0(5) 0(15) 0(1)

M18 0(20) 0(5) 0(15) 0(1)

M19 0(20) 0(5) 0(15) 0(1)

M20 0(20) 0(5) 0(15) 0(1)

M21 0(20) 0(5) 0(15) 0(1)

M22 1.4(30) 0(5) 0(15) 0(1)

M23 �1.4(6) 0(5) 0(15) 0(1)

M24 0(20) �1.4(1.5) 0(15) 0(1) 1

M25 0(20) 1.4(8.5) 0(15) 0(1)

M26 0(20) 0(5) �1.4(8) 0(1)

M27 0(20) 0(5) 1.4(22) 0(1)

M28 0(20) 0(5) 0(15) �1.4(0.3)

M29 0(20) 0(5) 0(15) 1.4(1.7)

experiments, building models, evaluating the effects of

factors and searching for the optimum conditions have

successfully been used in the optimization of bioprocesses

[8–13]. Niccolai et al. [14] have reported the maximization

of the production of constitutively expressed Helicobacter

pylori neutrophil activating protein (NAP) in recombinant

E. coli using response surface methodology.

In the present study, the individual and interactive effects

of medium components on recombinant TaqI endonuclease

production in E. coli were investigated and medium composi-

tion was optimized to maximize enzyme yield using RSM.

2. Materials and methods

2.1. Cell cultivation

The plasmid (pTaqR + M) encoding TaqI restriction

endonuclease under the control of the strong T7 RNA

polymerase promoter and Taq methylase under the control

of constitutive tetracycline resistance gene promoter was

constructed by Toksoy et al. [6].

Recombinant cells were cultivated in 1L Erlenmeyer

flasks containing 100 mL of medium specified according

to the experimental design, in an orbital shaker at 37 8C at

180 rpm rotational speed and induced at their mid-exponen-

tial phase using 1mM IPTG.

nz. Act. (U L�1) � 10�6 Biomass (g/L) Yield (U gDCW�1) � 10�6

65 0.659 98.6

82.5 0.672 122.8

27.5 0.597 46.1

42.5 0.591 71.9

25 0.585 42.7

25 0.619 40.4

17.5 0.621 28.2

55 0.997 55.2

47.5 0.585 81.3

57.5 0.998 57.6

62.5 1.332 46.9

45 1.265 35.6

45 0.982 45.8

7.5 1.051 7.1

70 1.014 69.1

50 0.753 66.4

45 0.911 49.4

42.5 0.900 47.2

40 0.960 41.7

40 0.981 40.8

40 1.020 39.2

42.5 0.877 48.5

92.5 0.951 97.3

02.5 0.798 128.4

50 1.279 39.1

50 0.908 55.1

37.5 1.194 31.4

1.5 0.77 1.9

30 1.453 20.6

I.E. Nikerel et al. / Process Biochemistry 40 (2005) 1633–1639 1635

Table 2

Parameters found for the full quadratic model and their significances

Yielda

Value P-value

b0 44.98 8.786 � 106

b1 �3.515 0.381

b2 �10.14 0.02070

b3 �12.00 0.00804

b4 �3.510 0.382

b5 13.42 0.03682

b6 4.956 0.272

b7 �1.981 0.655

b8 �9.444 0.04712

b9 18.96 0.00571

b10 15.17 0.00356

b11 8.081 0.08364

b12 �1.703 0.774

b13 8.744 0.06349

b14 �18.03 0.00784

a Yield = b0 + b1 � Glucose + b2 � (NH4)2HPO4 + b3 � KH2PO4 + b4

� MgSO4 + b5 � Glucose� Glucose + b6 � Glucose � (NH4)2HPO4 + b7

� Glucose � KH2PO4 + b8 � Glucose � MgSO4 + b9 � (NH4)2HPO4 �(NH4)2HPO4 + b10 � (NH4)2HPO4 � KH2PO4 + b11 � (NH4)2HPO4 �MgSO4 + b12 � KH2PO4 � KH2PO4 + b13 � KH2PO4 � MgSO4 + b14

� MgSO4 � MgSO4.

2.2. Experimental design

The production medium contained carbon source

(glucose), inorganic nitrogen ((NH4)2HPO4), potassium

(KH2PO4), magnesium (MgSO4�7H2O) and various vita-

mins and trace metals. The concentrations of glucose,

(NH4)2HPO4, KH2PO4 and MgSO4�7H2O were varied as

parameters, while the levels of thiamine and trace metal

solution were kept constant as described by Toksoy et al. [6].

A 24 full factorial central composite design (CCD) with

eight star points and five replicates at the centre points was

employed to fit a second order polynomial model. Twenty-

nine experiments were required for this procedure as given

in Table 1 [15]. The independent variables are coded for

statistical calculations according to the following equation:

Xi ¼Ui � U0

DU(1)

where, Xi is the independent variable coded value; Ui, the

real value of the independent variable; U0, real value of the

independent variable on the centre point and DU is the step

change. The modified R medium used in previous studies

was taken as the centre point [6]. The TaqI endonuclease

yield, which is defined as the enzyme activity per gram dry

cell weight (U gDCW�1) was used as the response variable.

MATLAB software (version 6.0, Stat-Ease Inc., Minnea-

polis, USA) was used for regression and graphical analyses

of the data obtained. The optimal concentrations of the

critical medium components were obtained by the

Nelder–Mead simplex search method.

2.3. Assays

Cells were grown in different medium compositions and

induced with 1 mM IPTG at an optical density of 0.65–0.7 at

600 nm. After 2 h of induction, the cells were harvested by

centrifugation at 4000 rpm for 10 min (Nuve NF 652,

TURKEY). The biomass concentration in gDCW L�1 and

TaqI endonuclease activity in crude extracts were deter-

mined as described by Toksoy et al. [6]. One unit of TaqI

endonuclease activity is defined as the amount required to

digest 1 mg of lambda DNA to completion in 1 h at 65 8C.

Table 3

ANOVA table for full quadratic model

Source SS SS (%) MS F F significance df

Regression 20277.4 83 1448.4 4.809 0.00291 14

Residual 4216.8 17 301.20 14

LOF error 4139.4 17(98) 413.94 21.3945 0.00481 10

Pure error 77.39 0(2) 19.35 4

Total 24494.2 100 28

3. Results and discussion

The biomass concentration and TaqI endonuclease activ-

ity were measured in 29 different medium compositions and

the corresponding enzyme yield (with an accuracy of � 5 �106 U gDCW�1) are presented in Table 1.

3.1. Construction of the representative model

The experimental results in Table 1 were fitted to a full

quadratic second order polynomial equation by applying

multiple regression analysis using MATLAB. The coded

variables were used in regression because they resulted in a

better fit than real values. The parameters found for this

equation and their significance levels are shown in Table 2.

The coefficient of multiple determination, R2 was found to

be 0.828, which means that the model could explain 82.8 per

cent of the total variations in the system. The relatively high

value of R2 indicates that the second order polynomial

equation is capable of representing the system under the

given experimental domain.

In order to check the adequacy of the model to represent

the system further, an analysis of variance (ANOVA) table

was constructed (Table 3). The F statistic was found to be

4.809 corresponding to F significance of 0.00291, which is

far below the confidence level of 0.05 indicating that the

model is adequate. This is also evident from the fact that the

plot of predicted versus experimental enzyme yields in Fig. 1

is close to y = x showing that the prediction of experimental

data is quite satisfactory.

3.2. Optimization of medium

The full quadratic model equation was optimized using

the Nelder–Mead simplex method to maximize enzyme

yield within the experimental range studied. The optimum

I.E. Nikerel et al. / Process Biochemistry 40 (2005) 1633–16391636

Fig. 1. Predicted vs. experimental enzyme yield.

medium composition was found to be �1.4 (6 g L�1) for

glucose, �1.4 (1.5 g L�1) for (NH4)2HPO4, �1.4 (8 g L�1)

for KH2PO4, and �0.4 (0.8 g L�1) for MgSO4�7H2O with a

prediction of 179.2 � 106 U gDCW�1 enzyme yield.

A new experiment was performed using the optimum

medium composition keeping all the other conditions the

same as those in initial experiments and the enzyme yield

was determined 178 � 106 U gDCW�1 which is in an

excellent agreement with the model prediction of 179.2 �106 U gDCW�1. It should also be noted that this yield is

much higher than the yields at the centre point (39.2–49.4 �106 U gDCW�1) as well as any other yield obtained in the

initial 29 experiments.

It can be concluded therefore that the predictive power of

the model is quite satisfactory, and the optimum medium

composition found is a real optimum under the conditions

studied.

3.3. Factor effects

To investigate the effects of four medium components as

well as their interaction on enzyme yield, the insignificant

terms in full quadratic model were removed using backward

Table 4

Parameters and their significance levels for the reduced model

Yielda

P-value

b0 44.36 5.128 � 10�7

b1 �10.14 0.01309

b2 �12.00 0.00430

b3 13.15 0.02654

b4 �9.444 0.03394

b5 18.69 0.00289

b6 15.17 0.00162

b7 8.081 0.06535

b8 8.744 0.04776

b9 �18.30 0.00340

a Yield = b0 + b1 � (NH4)2HPO4 + b2 � KH2PO4 + b3 � Glucose �Glucose + b4�Glucose�MgSO4 + b5� (NH4)2HPO4� (NH4)2HPO4 +

b6 � (NH4)2HPO4 � KH2PO4 + b7 � (NH4)2HPO4 � MgSO4 + b8 �KH2PO4 � MgSO4 + b9 � MgSO4 � MgSO4.

elimination method [15]. Table 4 summarizes the parameters

and their significance levels for the reduced model. The

linear main effect of glucose and MgSO4�7H2O, glucose-

MgSO4�7H2O interaction, glucose-(NH4)2HPO4 interaction

and the quadratic effect of KH2PO4 were found to be

insignificant compared to the other terms in the quadratic

equation as expected since the P-values of these terms were

larger than those of other terms in Table 2.

R2 slightly decreased from 0.828 to 0.788 by backward

elimination, while R2adjusted increased from 0.655 to 0.688 as

expected since the number of terms decreased from 14 to 9

in the model. F significance, on the other hand, decreased

dramatically from 0.00291 to 8.967 � 10�5 (Table 5)

indicating that the reduced model is statistically more sig-

nificant.

The reduced model was tested using the optimum med-

ium composition of the full quadratic model and the enzyme

yield of 168 � 106 U gDCW�1 was predicted. This value is

quite close to 179 � 106 U gDCW�1 prediction of the full

quadratic model and to the corresponding experimental

value of 178 � 106 U gDCW�1. This suggests that backward

elimination identified the insignificant terms fairly well and

therefore it can be used to analyze the effects of medium

components on the enzyme yield.

On the other hand, the reduced model resulted in its own

optimum medium composition of 1.4 (34 g L�1) for glucose,

�1.4 (1.5 g L�1) for (NH4)2HPO4, �1.4 (8 g L�1) for

KH2PO4, and �1 (0.5 l L�1) for MgSO4�7H2O with a pre-

dicted enzyme yield of 186 � 106 U gDCW�1. It should be

noted that the prediction of glucose is quite different than

�1.4 (6 g L�1) of the full quadratic model although the other

factors have the same or close optimum values. Apparently,

Table 5

ANOVA table for reduced model

Source SS SS (%) MS F F significance sf

Regression 308677 79 34297.5 7.845 8.967�10�5 9

Residual 83068.8 21 4372.0 19

LOF error 59973.6 15(72) 4613.4 1.1985 0.435 13

Pure error 23095.2 6(28) 3849.2 6

Total 391746 100 28

I.E. Nikerel et al. / Process Biochemistry 40 (2005) 1633–1639 1637

Fig. 2. Effects of medium components on enzyme yield. Open and filled symbols are experimental results at centre and optimum point, respectively; dashed and

solid lines are model prediction at centre and optimum point, respectively.

elimination of the linear effect of glucose as well as its

interactions with (NH4)2HPO4, and KH2PO4, reduced the

sensitivity of the model to � sign in coded variable. Indeed,

(glucose) � (glucose) term in reduced model has the same

value for 1.4 and �1.4, and the other glucose containing

(glucose) � (MgSO4�7H2O) term, may not be sufficient to

account for the sign change in glucose level. There can be

two possible explanations for this. First, the enzyme yield

may be indeed higher at the edges of glucose concentra-

tions than the intermediate values, and the reduced model

captured this trend. Second, it may just be a limitation of

the reduced model that it could not distinguish 1.4

(34 g L�1) glucose level from the real optimum of �1.4

(6 g L�1).

Both explanations turned out to be partially true. A new

experiment that was performed with the optimum medium

composition of reduced model (with 1.4. glucose level)

resulted 161 � 106 U gDCW�1 enzyme yield. Although

this value is lower than the 179 � 106 U gDCW�1 yield

of the full quadratic model (with �1.4 glucose level), it is

still much higher than the yield in any of 29 initial experi-

ments indicating that glucose indeed leads to a minimum

yield at the intermediate concentrations with the lower end

of range (�1.4) being better than the higher end (1.4) as far

as the enzyme yield is concerned. However, the discrepancy

between model prediction of 186 � 106 U gDCW�1 and the

experimental result of 161 � 106 U gDCW�1 for the

reduced model is much higher than that of the full quadratic

model confirming that the reduced model has some limita-

tions. It can be concluded that the second order term present

in both models dominantly explains the effects of glucose

concentrations with some contribution of other terms. This is

also evident from Fig. 2a that compares the effect of glucose

on enzyme yields under two distinct compositions of other

medium components. The solid line represents the model

prediction of glucose effect keeping the concentrations of

other factors constant at their optimum levels, while the

dashed line shows the model prediction when the other

factors are in their central compositions (experiments

M21, M22 and M23). The experimental values where the

data are available are also shown as filled and open circles

for the optimum and central composition of other factors,

respectively, and they were found to be in good agreement

with proposed model. The almost identical shapes of two

predicted responses suggest that the overall impact of inter-

actions of glucose with other components is not significant,

otherwise two plots would have different shapes. Although

the reduced model predicts that glucose and MgSO4�7H2O

interaction is significant (Fig. 3a), this interaction seems to

be balanced by other small interactions so that the glucose

has similar effects on enzyme yields at centre and optimum

compositions of other components.

The dependence of enzyme yield on (NH4)2HPO4 con-

centration was also analyzed by comparing its effects at the

optimum and centre compositions of other factors. Fig. 2b

shows that enzyme yield decreases with increasing

(NH4)2HPO4 at the centre point. The trend in this plot

suggests that first and second order effects should be present.

This was captured by the model considering that neither

linear nor the second order term was removed by backward

I.E. Nikerel et al. / Process Biochemistry 40 (2005) 1633–16391638

Fig. 3. Significant interactions among medium components predicted by the full quadratic model.

elimination. The behaviour of the model in optimum com-

position (solid line), however, is different than that of centre

composition (dashed line) indicating the presence of

some significant interactions. Indeed, the interaction of

(NH4)2HPO4 with KH2PO4, and MgSO4�7H2O as given in

Fig. 3b and 3c, respectively, were not removed by backward

elimination, and the (NH4)2HPO4 and KH2PO4 interaction is

the most significant term in full quadratic model with the

smallest P-value of 0.00356. This is expected since they

form the HPO42�/H2PO4

� buffer couple to maintain the pH.

Fig. 2c shows the effect of KH2PO4 on enzyme yield at

optimum and centre composition of other variables. The

model successfully predicted the linear dependence of

experimental yield on KH2PO4 at centre compositions of

other factors. The removal of the second order term by

backward elimination also confirms this linear dependence.

The un-parallelism between two plots may be attributed to

(NH4)2HPO4 and KH2PO4 interaction (Fig. 3b) as discussed

above. The contribution of KH2PO4 and MgSO4�7H2O

interaction (Fig. 3d) should be less significant since the

P-value (0.0836) is much greater than P-value (0.00356) of

the (NH4)2HPO4 and KH2PO4.

The backward elimination removed only the linear term

for MgSO4�7H2O from full quadratic model leaving the

second order term and interactions as significant. This is

supported by the experimental results as well as the model

prediction of enzyme yield when all the other factors were at

their centre levels as seen in Fig. 2d The same trend is

exhibited by the MgSO4�7H2O plot at optimum levels of

other parameters indicating that although this component

interacts with all other components (Fig. 3a, c and d), the

overall impact of interactions do not seem to contribute as

much as the second order terms otherwise there would be

some dissimilarity between the two plots. This figure also

indicates that the optimum level found for MgSO4�7H2O

was reasonable.

As conclusion, it was found that the medium containing

6 g L�1 of glucose, 1.5 g L�1 of (NH4)2HPO4, 8 g L�1 of

KH2PO4, and 0.8 g L�1 of MgSO4�7H2O should be used to

maximize TaqI endonuclease production under the condi-

tions studied. This medium composition resulted in an

enzyme yield of 178 � 106 U gDCW�1 which is much

higher than those obtained in the initial experiments as well

as the values reported in literature. This indicates that the

medium design using statistical techniques such as the

response surface methodology can be very useful in improv-

ing the production of recombinant TaqI endonuclease in

E. coli as well as in similar bioprocesses.

I.E. Nikerel et al. / Process Biochemistry 40 (2005) 1633–1639 1639

Acknowledgements

This research was supported by Bogazici University

Research Fund through Project No: 99A501.

References

[1] Slatko BE, Benner JS, Quinton TJ, Moran LS, Simcox TG, Van Cott

EM, Wilson GG. Cloning, sequencing and expression of the

TaqI restriction-modification system. Nucleic Acids Res 1987;15:

9781–9796.

[2] Toksoy E, Ozdinler PH, Onsan ZI, Kirdar B. High level secretion of

TaqI restriction endonuclease by recombinant Escherichia coli. Bio-

technol Tech 1999;13:803–8.

[3] Toksoy E, Onsan ZI, Kirdar B. Effect of the co-expression of methyl-

transferase activity on extracellular production of TaqI restriction

endonuclease in recombinant E. coli cells. Process Biochem

2001;37:527–34.

[4] Barany F. A genetic system for isolation and characterization of TaqI

restriction endonuclease mutants. Gene 1987;56:13–27.

[5] Toksoy E, Onsan ZI, Kirdar B. Expression of a thermostable restriction

endonuclease in recombinant Escherichia coli cells and optimization

of fermentation conditions. World J Microbiol Biotechnol 2002;

18:23–7.

[6] Toksoy E, Onsan ZI, Kirdar B. High-level production of TaqI restric-

tion endonuclease by three different expression systems in Eschercihia

coli cells using the T7 phage promoter. Appl Microbiol Biotechnol

2002;59:39–245.

[7] Oh S, Rheem S, Jaehun S, Kim S, Baek Y. Optimizing conditions for

the growth of Lactobacillus casei YIT 9018 in tryptone-yeast extract-

glucose medium by using response surface methodology. Appl

Environ Microbiol 1995;61:3809–14.

[8] Ooijkaas LP, Wilkinson EC, Tramper J, Buitelaar RM. Medium

optimization for spore production of Coniothyrium minitans using

statistically-based experimental designs. Biotechnol Bioeng 1999;64:

92–100.

[9] Wejse PL, Ingvorsen K, Mortensen KK. Xylanase production by a

novel halophilic bacterium increased 20-fold by response surface

methodology. Process Biochem 2003;32:721–7.

[10] Rodrigues RCLB, Felipe MA, Silva JBA, Vitolo M. Response surface

methodology for xylitol production from sugarcane bagasse hemi-

cellulosic hydrolyzate using controlled vacuum evaporation process

variables. Process Biochem 2003;38:1231–7.

[11] Francis F, Abdulhameed S, Nampoothiri KM, Ramachandran S,

Ghosh S, Szakacs G, Padney A. Use of response methodology for

optimizing process parameters for the production of a�amylase by

Aspergillus oryzae. Biochem Eng J 2002;15:107–15.

[12] Kılıc M, Bayraktar E, Ates S, Mehmetoglu Uul.. Investigation of

extractive citric acid fermentation using response surface methodol-

ogy. Process Biochem 2002;37:759–67.

[13] Elibol M, Ozer D. Response surface analysis of lipase production by

freely suspended Rhizopus arrhizus. Process Biochem 2002;38:367–

72.

[14] Niccolai A, Fontani S, Kapat A, Olivieri R. Maximization of recombi-

nant Helicobacter pylori neutrophil activating protein production in

Escherichia coli: improvement of a chemically defined medium using

response surface methodology. FEMS Microbiol Lett 2003;221:257–62.

[15] Montgomery DC, Runger GC. Applied Statistics and Probability for

Engineers. New York: John Wiley & Sons Inc.; 1994.