MI-Ahmad Wibisana Available online at http://jurnal.permi.or.id/index.php/mionline DOI: 10.5454/mi.9.3.4ISSN 1978-3477, eISSN 2087-8575 Vol 9, No 3, September 2015, p 120-128 *Corresponding author; Phone: +62-21- , Fax: 21-75603120, Email: awibisana2000@gmail.com 7560208 +62- Biosurfactants are microbial metabolites that work as surface active agents produced by some microorganisms, including bacteria, yeast and fungi. Interests in biosurfactants are growing due to their broad possible applications such as in the pharmaceuti- cal, cosmetic, agricultural and food industries, environmental protection and crude oil drilling (Banat et al. 2010; Meena et al. 2015). Their important characteristics such as biological activities, emulsifying, foaming, soaping and dispersing acquire them for multiple applications. Based on their chemical structures, biosurfactants are divided into five major classes, i.e. lipopeptides, glycolipids, phospholipids, neutral lipids, and polymeric compounds. One of the most important families of lipopeptide class is surfactin. Surfactin is a cyclic molecules consisting of a fatty acid of variable length (hydrophobic moiety) linked to a short Surfactin is a lipopeptide biosurfactant that show potential biomedical application due to its activities such as antiviral, antibacterial, antifungi, anticancer, and antimycoplasma. Bacillus amyloliquefaciens MD4-12, isolated from oil-contaminated soil, produced promising yield of surfactin in McKeen medium. The production of surfactin was influenced by many fermentation process parameters such as carbon, nitrogen, minerals and also environmental conditions such as pH and agitation. Therefore, to obtain high yield of surfactin by B. amyloliquefaciens MD4-12, optimization of process production was conducted in shake flask fermentation using response surface methodology. McKeen medium composition was used as basal medium. Screening of the best carbon and nitrogen source were selected in preliminary experiments followed by selection of the influencing significant parameters on surfactin production using Plackett-Burman design. Selected parameters were optimized by central composite design and for the data analysis was used response surface methodology. The -1 result showed that the optimum medium composition contained (g L ) 45.0 glucose, 6.33 urea, 1.0 monosodium glutamate, 1.85 MgSO .7H O, 0.4 KCl, 0.5 K HPO , and 0.5 mL trace elements. The surfactin yield at optimal 4 2 2 4 -1 condition was 1.25 g L , increased 2.4 times compared to condition prior to optimization. Key words: lipopeptide, optimization, response surface methodology, surfactin Surfaktin adalah senyawa biosurfaktan lipopeptida yang sangat potensial untuk aplikasi di bidang biomedis karena mempunyai berbagai aktifitas biologi seperti antivirus, antibakteri, antijamur, antikanker, dan antimikoplasma. Bacillus amyloliquefaciens MD4-12, yang diisolasi dari tanah yang tercemar minyak menunjukkan kemampuan yang tinggi dalam menghasilkan surfaktin menggunakan medium dasar McKeen. Produksi surfaktin secara fermentasi dipengaruhi oleh berbagai faktor seperti sumber karbon, sumber nitrogen, mineral serta kondisi lingkungan seperti pH dan agitasi. Dengan menggunakan medium McKeen sebagai medium dasar, maka pada penelitian ini dilakukan optimasi produksi surfaktin menggunakan metode permukaan respon. Pada percobaan awal dilakukan pemilihan sumber karbon dan nitrogen terbaik, dan selanjutnya diikuti dengan pemilihan faktor-faktor yang berpengaruh secara signifikan untuk produksi surfaktin menggunakan rancangan Plackett-Burman. Faktor-faktor yang terpilih selanjutnya dioptimasi dengan menggunakan central composite design dan analisis data dilakukan dengan menggunakan metode permukaan respon. Hasil penelitian -1 menunjukkan bahwa komposisi medium yang optimal terdiriatas (g L ) glukosa 45,0; urea 6,33; monosodium glutamat1,0; MgSO .7H O 1,85; KCl 0,4,; K HPO 0,5 dan mikroelemen 0.5 mL. Pada kondisi optimum 4 2 2 4 -1 perolehan surfaktin adalah 1,25 g L , meningkat 2,4 kali dibandingkan dengan kondisi sebelum optimasi. Kata kunci: lipopeptida, metode permukaan respon, optimasi, surfaktin Optimization of Surfactin Production by Bacillus amyloliquefaciens MD4-12 Using Response Surface Methodology 1 2,3 4 AHMAD WIBISANA *, WAHONO SUMARYONO , TJAHJANI MIRAWATI SUDIRO , AND 4 PRATIWI PUDJILESTARI SUDARMONO 1 Biotech Center-Badan Pengkajian dan Penerapan Teknologi, Gedung 630, Kawasan Puspiptek Serpong, Tangerang Selatan, Indonesia; 2 Center of Pharmaceutical and Medical Technology, Badan Pengkajian dan Penerapan Teknologi, Gedung Laptiab Kawasan Puspiptek Serpong Tangerang Selatan, Indonesia; 3 Faculty of Pharmacy Universitas Pancasila, Jalan Srengseng Sawah Jagakarsa Jakarta Selatan, 12640, Indonesia; 4 Department of Microbiology, Faculty of Medicine, Universitas Indonesia/Cipto Mangunkusumo General Hospital, Jakarta, Jalan Pegangsaan Timur 16 Jakarta, 10320, Indonesia peptide chain (hydrophilic moiety) of seven amino acids. It has ability to reduce surface tension of water -1 -1 from 72 mN m to 27 mN m (Cooper et al. 1981). Several biological activities have been reported for biomedical application, such as antiviral, antibacterial, antifungi, antitumor, antiinflammatory, antiadhesive, antimycoplasma and thrombolytic activity (Huang et al. 2006; Sabate et al.2013; Liu et al. 2012; Kim et al. 2007; Zhang et al. 2015; Zeraik et al. 2010, Lim et al. 2005; Meena et al. 2015). A large variety of Bacillus has been reported to produce surfactin, nevertheless microbial exploration is still needed to obtain high productivity strain. Bacillus amyloliquefaciens MD4-12 isolated from oil- contaminated soil showed promising ability to produce surfactin. According to previous studies, the surfactin production was influenced by several nutritional factors including the nature and concentration of carbon and nitrogen substrate, the concentration of metal ion such as Mg, Fe, Zn, and Mn in the medium and environmental conditions such as temperature, pH, agitation and aeration (Sen 1997; Yi et al. 2013; Sen et al. 1997; Al-Ajlani et al. 2007; Abdel-Mawgoud et al. 2008; Amani et al. 2013; Singh et al. 2014a). To achieve high yield of surfactin production by B. amyloliquefaciens MD4-12, optimization of nutrient supply and environmental condition is very important. Statistical technique, including combination of Plackett-Burman design (PBD), central composite design (CCD) and response surface methodology (RSM) are the most widely method used for optimization of biological processes. The Plackett- Burman experimental design is a two-level factorial design, used to determine the critical physicochemical parameters. Interaction effects among the variables are not considered (Mnif et al. 2012). To screen N variables using PBD, N+1 experiments are needed. The significant variables obtained by PBD can be further optimized using CCD and (RSM). RSM has been used extensively in biological process optimization due to its efficiency and accuracy for building of a model, evaluating the effects of factors and predicting optimum conditions. The objective of the present study was to optimize of surfactin production by B. amyloliquefaciens MD4-12 using response surface methodology. MATERIALS AND METHODS Microorganisms. A surfactin-producing microor- ganism has been isolated from oil-contaminated soil taken from Palembang, Indonesia. Furthermore, it was identified as B. amyloliquefaciens MD4-12 by morphological, biochemical, and 16S rRNA sequence analysis. Inoculum and Culture Conditions. The strain was stored lyophilized and working stock cultures were maintained on nutrient agar slant and stored at 4 °C. For surfactin production, two-stage inoculum were prepared as following: one isolated colony from fresh culture grown onto nutrient agar medium and incubated for 24 h, was dispensed in 5 mL of nutrient broth medium and incubated overnight at 37 °C. The culture (4 mL) was used to inoculate 100 mL nutrient broth medium in 500 mL Erlenmeyer flasks and incubated in a rotatory shaker at 200 rpm and 37 °C (±0.5) for 16 h. At this condition, absorbance around 3 measured spectrophotometrically at 600 nm was reached. For fermentation, 2 mL of culture was inoculated to 250 mL Erlenmeyer flask containing 50 mL of production medium. Modified McKeen medium composition was -1 used as the production medium consist of (g L ) 20.0 glucose, 2.0 monosodium glutamate, 3.0 yeast extract, 1.0 MgSO .7H O, 1.0 KCl with addition of 1 mL trace 4 2 element solution (in 100 mL deionized water : 0.64 g MnSO .4H O, 0.16 g CuSO .5H O, and 0.015 g 4 2 4 2 FeSO .7H O). pH was adjusted at 7.0. Fermentation 4 2 was conducted for 28 h. Experimental Design and Data Analysis. Preliminary experiments were conducted to screen carbon and nitrogen source. The effect of different carbon sources was evaluated by substitution of glucose in McKeen medium composition with sucrose, fructose, maltose, dextrose, mannitol, sorbitol, galactose, xylose, and starch. The best carbon source generated the highest surfactin production was used for further study. Furthermore, the effect of the different nitrogen source addition was studied similarly by substitution of yeast extract with ammonium nitrate, ammonium sulphate, sodium nitrate, soy flour, peptone, casein hydrolysate, and urea. The next step was to screen the significant factors affecting surfactin production by B. amyloliquefaciens MD4-12 using the PBD, as noted in Table 1. All components in the production medium (seven factors) and some environmental conditions (two-factor) were used to determine the factors that influence significantly. This experiment required 12 runs as presented in Table 2. The factors with confidence levels above 95% were considered as significant effect on surfactin production. The selected significant influence factor obtained Volume 9, 2015 Microbiol Indones 121 from PBD experiments was optimized with CCD and RSM as presented in Table 4. Thirty experiments consisted of sixteen factorial points, eight star points and six center points were conducted to optimize four significant factors. For each factor was studied at three level concentration (low, middle and high levels) coded with (-1, 0, +1). All experiments were conducted triplicate and the final result was expressed as the average value. Statistical Analysis. Design Expert (Version 7.0, Stat-Ease Inc., USA) software was used to generate the experimental designs and data analysis. The quality of polynomial model equation was evaluated statistically using analysis of variance (ANOVA) to determine the 2 2 coefficient of determination, R and adjusted R . The statistical significance of the model and the regression coefficient were determined using the F-test. Surfactin Analysis. The gravimetric method as described by Liu et al. (2012) was used for determination of surfactin concentration with minor modification. The broth culture was centrifuged at 10 000 rpm for 10 min to obtain the cell free supernatant (CFS). An aliquot of the CFS was acidified to pH 2.0 using 6 N HCl, left overnight at 4 °C, and then centrifuged at 10 000 rpm for 20 min. The resulting supernatant was discarded and the remaining pellet was collected, and then extracted three times with methanol. The dry pellet was obtained by removal of methanol using a rotary vacuum evaporator. The net weight of pellet was determined and calculated as crude surfactin. Structural characterization of the crude surfactin by mass spectrophotometry revealed that it contained surfactin as the major component (data not shown). RESULTS Selection of Carbon Source and Nitrogen Source. Among the carbon sources used in this experiment, the use of glucose yielded highest -1 surfactin production, i.e. 0.320 g L . Furthermore, from the experiments result to screen the nitrogen source showed that the use of urea yielded the highest -1 surfactin production, i.e. 0.436 g L . Therefore, glucose and urea were used as carbon and nitrogen source for the next study. Plackett-Burman Experimental. The experiments for selection of nine critical factors affected surfactin production by B. amyloliquefaciens MD4-12 namely medium composition (seven factors) and environment conditions (initial medium pH and fermentation temperature) were conducted using PBD. The experimental design and results were presented in Table 2. The ANOVA of the experiments (Table 3) showed that the p-value (Prob>F) of the model was 0.0402, implied that the model was significant. The magnitude of each factor affecting on surfactin production was indicated by p-value. Among the factors screened, the concentration of glucose, urea, MgSO .7H O, and KCl showed 4 2 significantly effecting on surfactin production with p- values, 0.0438, 0.0100, 0.0221, and 0.0262 with the coefficient of the model 0.056, 0.12, -0.080, and 0.074 respectively. The other factors have p-value (Prob>F) > 0.05 that means no significantly affecting on surfactin production. Optimization Using Response Surface Methodo- logy. Optimization of four significant factors on surfactin production was conducted using RSM and the results were presented in Table 4. The other factors were kept at their low level. The experiments result was modeled with a second-order polynomial equation to explain the effect of each factor on surfactin production, as follows: Y = 1.18 - 0.034 X - 4.758E-003 X + 0.069 X - 0.080 1 2 3 X + 0.057 X X + 0.018 X X - 0.11 X X - 4 1 2 1 3 1 4 2.814E-003 X X + 0.047 X X + 8.887E-005 X 2 3 2 4 3 2 2 2 2 X - 0.041 X - 0.077 X - 0.065 X + 0.023 X4 1 2 3 4 Where Y was the estimated surfactin production and X , X X and X were coded value for glucose, urea, 1 2, 3 4 MgSO .7H O and KCl concentration, respectively. The 4 2 results of the second-order response surface model in the form of analysis of variance (ANOVA) are given in Table 5. It can be seen that two linear (X and X ) and 3 4 three quadratic (X , X , and X ) terms were significant, 1 2 3 with the p-values being very small (p<0.05). The quadratic effect of X was not significant (p>0.05). The 4 significant interaction of variables was shown by X .X ; 1 2 X .X , and X .X terms (p<0.05). The other interaction 1 4 2 4 of factors was insignificantly effected on surfactin production. By considering the only significant factors, the model for surfactin production could be written as following equation: Y = +1.18 - 0.069 X - 0.080 X + 0.057 X X - 0.11 3 4 1 2 2 2 X X - + 0.047 X X - 0.041 X - 0.077 X - 1 4 2 4 1 2 2 0.065 X3 According to the equation, the yield of surfactin could be estimated as a linear effect of magnesium sulphate and potassium chloride concentration and squared effect of glucose, urea and magnesium 122 WIBISANA ET AL. Microbiol Indones Variable Code Unit Level -1 +1 Carbon source A -1 g L 15 45 Nitrogen source B 1 9 MSG C 1 4 MgSO .7H O 4 2 D 0.5 2 K HPO 2 4 E 0.5 2.5 KCl F 0.3 1 Trace element G mL 0.5 2 pH H 6 8 Temperature I °C 25 35 -1 g L -1 g L -1 g L -1 g L -1 g L Std Variables Surfactin A B C D E F G H I -1 g L mL °C 1 45 9 1 2 2.5 1 0.5 6 25 0.366 2 15 9 4 0.5 2.5 1 2 6 25 0.413 3 45 1 4 2 0.5 1 2 8 25 0.133 4 15 9 1 2 2.5 0.3 2 8 35 0.048 5 15 1 4 0.5 2.5 1 0.5 8 35 0.207 6 15 1 1 2 0.5 1 2 6 35 0.116 7 45 1 1 0.5 2.5 0.3 2 8 25 0.085 8 45 9 1 0.5 0.5 1 0.5 8 35 0.572 9 45 9 4 0.5 0.5 0.3 2 6 35 0.508 10 15 9 4 2 0.5 0.3 0.5 8 25 0.180 11 45 1 4 2 2.5 0.3 0.5 6 35 0.039 12 15 1 1 0.5 0.5 0.3 0.5 6 25 0.064 -1 g L -1 g L -1 g L -1 g L -1 g L -1 g L Table 1 Plackett-Burman design for screening of significant parameters on surfactin production by Bacillus amyloliquefaciens MD4-12 Table 2 The Plackett-Burman design and the experimental results for the selection of important parameters on the surfactin production Volume 9, 2015 Microbiol Indones 123 sulphate concentration. ANOVA of the model for surfactin production showed that the regression model was highly significant and the lack of fit was insignificant (Table 2 5). The high determination coefficient (R ) showed the goodness of fit of the second order model, and it implied that only 9.58% the total variations were not explained by the model. The high adjusted determination 2 coefficient (adj R ) was also satisfactory to confirm the significance of the model. A lower value of the coefficient of variation (CV) indicates that experiments were precise and reliable (Box et al. 1978). The signal to noise ratio (adequate precision) for the model was higher than 4, indicating a good fit. Visualization of the interaction between the response and experimental levels of each variable and the type of interactions between test variables in order to deduce the optimum conditions provided by the 3D response surface. Fig 1 depicted the 3D response surface showing the interactive effect of the significant variable interaction. The other insignificant variables, i.e. concentration of monosodium glutamate, K HPO , 2 4 -1 and trace element were kept at a minimum level (1 g L , -1 -1 0.5 g L , and 0.5 mL L , respectively) and initial pH of medium and temperature were 7.0 and 30 °C, respectively. Validation of the Optimized Condition. Based on the model, the optimized medium composition consists -1 of (g L ): 45.00 glucose, 6.33 urea, 1 monosodium glutamate, 1.85 MgSO .7H O, 0.40 KCl, 0.5 K HPO , 4 2 2 4 and 0.5 mL trace element. The model predicts the -1 maximum yield of surfactin as 1.35 g L . Under optimized condition, validation experiments were conducted in triplicates. The result of experiments -1 showed that the yield of average surfactin was 1.25 g L , suggesting that experimental and predicted values of surfactin yield were in a good agreement. The usage of optimized medium determined by response surface methodology for surfactin production could increase 2.4-fold over the yield in non-optimized medium. DISCUSSION B. amyloliquefaciens MD4-12 is a potential strain for surfactin production. The selection of potential strain is necessary for future mutation or bioengineering studies. The significant influence factors on surfactin production by B. amyloliquefaciens MD4-12 consist of glucose, urea, MgSO .7H O and KCl selected by PBD 4 2 experiments. B. amyloliquefaciens MD4-12 can use Source Coeficient model Sum of Squares df Mean Square F Value p-value Prob > F Model 0.39 9 0.043 24.26 0.0402* Glucose 0.056 0.038 1 0.038 21.32 0.0438* Urea 0.17 1 0.17 98.32 0.0100* Yeast Extract 0.019 4.376E-003 1 4.376E-003 2.48 0.2563 MgSO .7H O 4 2 -0.080 0.077 1 0.077 43.83 0.0221* K HPO 2 4 -0.035 0.014 1 0.014 8.15 0.1039 KCl 0.074 0.065 1 0.065 36.72 0.0262* Trace element -0.011 1.355E-003 1 1.355E-003 0.77 0.4737 Initial pH -0.024 6.651E-003 1 6.651E-003 3.76 0.1920 Temperature 0.021 5.243E-003 1 5.243E-003 2.97 0.2272 Table 3 ANOVA of the factors affecting on surfactin production using Plackett-Burman design *) significant at the level > 95.0% (for p-value < 0.05). 124 WIBISANA ET AL. Microbiol Indones Table 4 Central composite design and the experimental results for optimization of important parameters on surfactin production Std X :Glucose 1 -1 (g L ) X :Urea 2 X :MgSO .7H O 3 4 2 X :KCl 4 Surfactin 1 30.00 5.00 0.70 0.40 1.088 2 45.00 5.00 0.70 0.40 1.261 3 30.00 9.00 0.70 0.40 0.922 4 45.00 9.00 0.70 0.40 1.064 5 30.00 5.00 2.00 0.40 1.170 6 45.00 5.00 2.00 0.40 1.313 7 30.00 9.00 2.00 0.40 1.033 8 45.00 9.00 2.00 0.40 1.175 9 30.00 5.00 0.70 1.00 1.190 10 45.00 5.00 0.70 1.00 0.556 11 30.00 9.00 0.70 1.00 1.000 12 45.00 9.00 0.70 1.00 0.868 13 30.00 5.00 2.00 1.00 1.180 14 45.00 5.00 2.00 1.00 0.790 15 30.00 9.00 2.00 1.00 1.103 16 45.00 9.00 2.00 1.00 0.988 17 22.50 7.00 1.35 0.70 0.953 18 52.50 7.00 1.35 0.70 1.020 19 37.50 3.00 1.35 0.70 0.783 20 37.50 11.00 1.35 0.70 0.864 21 37.50 7.00 0.05 0.70 0.630 22 37.50 7.00 2.65 0.70 1.008 23 37.50 7.00 1.35 0.10 1.388 24 37.50 7.00 1.35 1.30 1.061 25 37.50 7.00 1.35 0.70 1.169 26 37.50 7.00 1.35 0.70 1.079 27 37.50 7.00 1.35 0.70 1.159 -1 (g L ) -1 (g L ) -1 (g L ) -1 (g L ) Volume 9, 2015 Microbiol Indones 125 Mawgoud et al. 2008; Ghribi et al. 2011). In this study, -1 the usage of glucose concentration higher than 40 g L still showed increasing of surfactin production and at -1 glucose concentration 45 g L reached maximum surfactin yield. Although in this study the concentration of glucose required higher but keep in mind that surfactin produced was also higher than previous reported. The nature of nitrogen source also plays an important role in the production of surface-active several carbon sources for surfactin production. However, the nature and concentration of carbon source influenced on surfactin yield (Abdel-Mawgoud et al. 2008; Yi et al. 2013; Singh et al. 2014b). The best carbon source for surfactin production by B. amyloliquefaciens MD4-12 is glucose since this carbon source is easy to assimilate. The concentration of glucose for maximum surfactin production was -1 reported at 40 g L and at glucose concentration higher -1 than 40 g L , the surfactin yield decreased (Abdel- Source Coeficient Estimates Sum of Squares df Mean Square F Value p-value Prob > F Model 0.91 14 0.065 10.11 < 0.0001* Intercept 1.18 X -Glucose 1 -0.0337 0.027 1 0.027 4.26 0.0567 X -Urea 2 -0.0048 5.433E-004 1 5.433E-004 0.085 0.7747 X -MgSO .7H O 3 4 2 0.06930 0.12 1 0.12 18.02 0.0007* X -KCl 4 -0.0798 0.15 1 0.15 23.89 0.0002* X .X 1 2 0.05693 0.052 1 0.052 8.11 0.0122* X .X 1 3 0.01790 5.127E-003 1 5.127E-003 0.80 0.3847 X .X1 4 -0.1136 0.21 1 0.21 32.29 < 0.0001* X .X 2 3 -0.0028 1.267E-004 1 1.267E-004 0.020 0.8899 X .X 2 4 0.04682 0.035 1 0.035 5.48 0.0334* X .X 3 4 8.89E -15 1.264E-007 1 1.264E-007 1.976E-05 0.9965 2 X 1 -0.0412 0.046 1 0.046 7.26 0.0166* 2 X2 -0.0767 0.16 1 0.16 25.23 0.0002* 2 X 3 -0.0647 0.11 1 0.11 17.97 0.0007* 2 X 4 0.0225 0.014 1 0.014 2.17 0.1610 Residual 0.096 15 6.396E-003 Lack of Fit 0.082 10 8.195E-003 2.93 0.1236 Pure Error 0.014 5 2.798E-003 Table 5 ANOVA for respon surface with quadratic regression model 2 2 *) significant at the level > 95.0% (for p value < 0.05); R = 0.9042; Adj R = 0.8148; Coeficient variation (C.V. %) = 7.63; Signal to noise ratio (adequate precision) = 14.605 126 WIBISANA ET AL. Microbiol Indones optimum condition of MgSO .7H O and KCl were 1.85 4 2 -1 and 0.4 g L , respectively, which is equal to 2+ + concentration of Mg and K of 7.5 and 5.3 mM, respectively. MgSO4 concentration influenced cell growth as well as the surfactin production, while KCl was reported to relate with the permeation pressure by offering a buffer environment in cooperation with KH PO for cell growth (Yi et al. 2013). Maintaining the 2 4 environmental condition is important to support biomass growth and surfactin production as well as inhibit byproduct formation in the fermentation process. compounds by microorganisms (Singh et al. 2014b). In this study the use of urea showed highest surfactin production. The optimum urea concentration was 6.33 -1 g L . The adequate C/N ratio is an important factor for efficiently surfactin synthesis. The other significant factors effecting surfactin production in this study were MgSO .7H O and KCl. 4 2 These results correspond to those reported in literature (Wei et al. 2007). Under optimal condition of the metal 2+ + ion Mg and K (2.4 and 10 mM, respectively), surfactin yield was improved significantly. In this study the S u rf a c ti n X : Urea2 X : KCl4 1.2075 1.065 0.9225 0.78 5.00 6.00 7.00 8.00 9.00 0.40 0.55 0.70 0.85 1.00 S u rf a c ti n 1.35 1.255 1.16 1.065 0.97 30.00 33.75 37.50 41.25 45.00 5.00 6.00 7.00 8.00 9.00 X : Urea2 X : Glucose1 1.35 A S u rf a c ti n X : Glucose1 X : KCl4 1.35 1.2525 1.155 1.0575 0.96 30.00 33.75 37.50 41.25 45.00 0.40 0.55 0.70 0.85 1.00 B C Fig 1 3D respon surface plot for effect combination of (a) glucose and urea (b) glucose and KCl and (c) urea and KCl on surfactin production. Volume 9, 2015 Microbiol Indones 127 doi:10.1016/j/febslet.207.01.059. Lim JH, Park BK, Kim MS, Hwang MH, Rhee MH, Park SC, Yun HI. 2005. 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