Unstructured kinetic model for sophorolipid production by Candida bombicola

Enzyme and Microbial Technology 25 (1999) 613– 621

Unstructured kinetic model for sophorolipid production by Candida bombicola F. Garcı´a-Ochoa*, J.A. Casas Dept. Ingenierı´a Quı´mica, Facultad CC Quı´micas, Universidad Complutense, 28040 Madrid, Spain Received 26 November 1998; received in revised form 8 June 1999; accepted 15 June 1999

Abstract A kinetic model for sophorolipid production by Candida bombicola is proposed. The unstructured nonsegregated kinetic model takes into account three responses: biomass, sugar, and sophorolipid. In the kinetic model, the nitrogen—from yeast extract—is assumed to be the limiting nutrient of the yeast growth, the sugar— glucose—is assumed to be used in both growth and sophorolipid production, and oil is supposed to be spent in product formation and maintenance of biomass is attributed to glycerol eased by the catabolism of the oil. The parameters of the kinetic model are calculated by fitting model to experimental data. These data have been obtained in experiments in which a batch stirred and an aerated tank bioreactor have been used. Fitting has been carried out according to a nonlinear regression algorithm, coupled with a Runge–Kutta method for the interpretation of integral data (evolution of system composition with time). The proposed kinetic model is able to explain both our experimental data and data given by other authors in previous works in literature in which nutrient and product concentrations evolution are given. © 1999 Elsevier Science Inc. All rights reserved. Keywords: Sophorolipid production; Kinetic modeling; Candida bombicola

1. Introduction Biosurfactants are recently attracting attention as natural and promising surfactants, offering several advantages over chemical surfactants, such as lower toxicity, biodegradable nature and ecological acceptability [1]. Sophorolipids are one of those molecules with surfactant properties that are synthesized by the yeast Candida bombicola. These biosurfactants are nongrowth-associated products, thus yeast first grows, and when it reaches the stationary growth phase begins to produce the biosurfactant molecules [2]. Sophorolipids are comprised of one sophorose molecule (␤ 1–2 glucose), hydrophile part, linked to one hidroxyfatty acid, lipophile part, by one or two crosslines [3], as can be seen in Fig. 1. There are several of these species depending on sophorose acetylated grade (can be acetylated on 6⬘ or 6⬙), hidroxyfatty acids lengths, and hydroxyl group position on the fatty acid [4]. All of these species show similar surfactant properties, although cycled structures can inhibit the growth of some microorganisms [5]. Sophorolipids already

are used as a high value skin moisturizer, in the petroleum industry, and in cosmetic and food areas [6]. Several works, many of them very recent, have studied medium composition and methods to produce sophorolipids in high concentration and yield and low cost [1– 4,7–10]. Some of them are studying an optimal medium composition [3,7,11]. Other works are considering different production methods, such as batch [3], fed-batch [2], and resting cells [12]. The development of a kinetic model for scale-up and bioreactor design is necessary for this important bioprocess. Nevertheless, no reference exists in literature on a kinetic model able to describe sophorolipid production. In all works, data are compared by using singular points for yeast growth and production process. A kinetic model must be able to describe subtracts and product evolutions under different operational conditions. The aim of this work is the development and testing of a kinetic model able to fit both our experimental data and data from other authors.

2. Kinetic model * Corresponding author. Tel.: ⫹34-1-394-4176; fax: ⫹34-1-394-4176/14. E-mail address: [email protected] (F. Garcı´a-Ochoa)

The medium usually used for sophorolipid production includes three nutrients: sugar— usually glucose—, oil—

0141-0229/99/$ – see front matter © 1999 Elsevier Science Inc. All rights reserved. PII: S 0 1 4 1 - 0 2 2 9 ( 9 9 ) 0 0 0 8 9 - 7

614

F. Garcı´a-Ochoa, J.A. Casas / Enzyme and Microbial Technology 25 (1999) 613– 621

Fig. 1. Molecular structure of sophorolipids [4].

Nomenclature CC CP CS CX C Xm C X0 Em F G O P r2 ri Rj S t tS X Y PO Y mS Y PS Y XS YE YNB

carbon dioxide concentration (kg/m3) product, sophorolipid concentration (kg/m3) glucose concentration (kg/m3) biomass concentration (kg/m3) maximum biomass concentration (kg/m3) initial biomass concentration (kg/m3) maintenance energy statistical parameter, experimental fitted and tabulated value for Fisher’s F glycerol oil product (sophorolipid) statistical parameter reaction rate (kg i/m3 䡠 h) (i depends on the reaction) production rate (kg j/m3 䡠 h) (j depends on the species) glucose time (h) statistical parameter, experimental fitted and tabulated value for Student’s t biomass oil product yield (kgproduct/kgoil) substrate biomass maintenance yield kgbiomass/kgsubstrate) substrate product yield (kgproduct/kgsubstrate) substrate biomass yield (kgbiomass/kgsubstrate) yeast extract yeast nitrogen base.

Greek symbols

␮ ␮C ␮P

biomass growth specific rate (per h) carbon dioxide specific constant rate (kg0/kgx 䡠 h) product-specific constant rate (kgp 䡠 m3/kgx 䡠 kgs 䡠 h)

many have been employed, sunflower oil being one of those giving best results [11]—and yeast extract. Each one of these nutrients plays a different role in the process: 2.1. Sugar (glucose) This carbon source is consumed in biomass growth [16] and sophorolipid production and perhaps in biomass maintenance. Glucose is assimilated during yeast growth until another essential source, usually the nitrogen source (yeast extract), is depleted [3]. When biomass reaches the stationary growth phase, it begins to produce sophorolipids. This product is synthesized to reduce the interfacial tension oil/ water and as a food stock which few microorganisms will be able to consume [11]. 2.2. Oil (sunflower oil) The yeast employs this carbon source in sophorolipid production. The yeast produces sophorolipid molecules by using the fatty acids obtained from the triglycerides [13,14], while the glycerol released must be used for energy maintenance [11]. 2.3. Yeast extract This extract contains nitrogen, phosphate, and all oligoelements required for yeast growth and for production of sophorolipids. Therefore, yeast extract concentration shows high influence on the biomass and product concentrations reached [3,11]. Sophorolipid production reduces the interfacial tension oil/water and the superficial tension air/water. These effects are proportional to sophorolipid concentration in dissolution until this reaches the critical micelle concentration (CMC, that value of the concentration which corresponds to the moment in which the surfactant molecules begin to form micelles). The critical micelle concentration is reached, in

F. Garcı´a-Ochoa, J.A. Casas / Enzyme and Microbial Technology 25 (1999) 613– 621

the case of the sophorolipid and at pH 4, toward 0.2 kg/m3. This surfactant concentration is reached at the early stages of the production process. Therefore, during all of the production process, it can be considered that the CMC value has been reached and all physical properties are constant for the entire experiment. The medium is considered to be saturated in oil, so constant oil concentration also is assumed. According to the above hypothesis, the following simplified reaction scheme can be assumed, taking into account that the maintenance of the yeast is achieved by using the glycerol (G) released from the oil catabolism, as shown by reaction 3: Y XSS O ¡X

(reaction 1)

Y PSS ⫹ YPOO O ¡P

(reaction 2)

Y mGG O ¡ Em ⫹ CO2

(reaction 3)

冧

冎

(1)

(2)

The kinetic equations for each of the reactions of the simplified scheme of reaction were assumed to be as follows: For reaction 1, as usual for microbial growth:

冋

r1 ⫽ ␮ 䡠 CX 䡠 1 ⫺

CX C Xm

册

(3)

For reaction 2: assuming that sophorolipid production depends on three factors, glucose, oil, and biomass, and considering that sunflower oil has a lipid nature and low solubility in water and, therefore, its concentration in solution can be assumed constant at any time and it can be included in the kinetic constant value (␮P), this kinetic equation can be written according to the following equation: r2 ⫽ ␮P 䡠 CS 䡠 CX

Therefore, the kinetic model proposed can be expressed according to the following set of equations:

冋

册

RX ⫽ r1 ⬖

dCX CX ⫽ ␮ 䡠 CX䡠 1⫺ dt CXm

RP ⫽ r2 ⬖

dCP ⫽ ␮P 䡠 CS 䡠 CX dt

1 1 dCS RS ⫽ ⫺ 䡠r ⫺ 䡠r ⬖ YXS 1 YPS 2 dt 1 dCX 1 dCP ⫽⫺ 䡠 ⫺ 䡠 YXS dt YPS dt

冧

(5)

3. Materials and methods

Obviously, the reaction scheme proposed is simplified, but it takes into account biomass (X) growth (reaction 1) sophorolipids (S) production (reaction 2) from glucose (S), and biomass maintenance from glycerol (G) (reaction 3). In all of these equations, biomass can be considered not as reactive but as a cell factory. After a stoichiometric study, three key compounds must be chosen for analysis and process evolution rate description. Among different possibilities, because glycerol and CO2 were not analyzed (both in this work and in the other works in literature), the key compounds chosen were: biomass (X), sophorolipid (P), and glucose (S). Thus, the production rates of these three key compounds are those given by Eq. (2): RX ⫽ r1 RP ⫽ r2 R S ⫽ ⫺1/Y XS 䡠 r 1 ⫺ 1/Y PS 䡠 r 2

615

(4)

3.1. Microorganism C. bombicola NRRL Y-17069 was used. This yeast was supplied by Microbiologist Culture Collection, Fermentation Laboratory, Department of Agriculture, Peoria, IL, USA. The strain was stored at ⫺25°C in a cryoprotective medium: glycerol 100 g/l, saline solution (NaCl) 0.9% (w/w). 3.2. Growth medium composition C. bombicola grew in a medium with 10 kg/m3 glucose, 7.5 kg/m3 yeast nitrogen base (YNB), and 2 kg/m3 yeast extract (YE). 3.3. Inoculum build-up Culture was carried out in shake flasks, in an orbital shaker, at 210 rev./min and 30°C, by using 250-ml Erlenmeyers. The yeast grew from 0.03 kg/m3 to 9 kg/m3 in 30 h. After this, the culture was used as inoculum for the different trials. 3.4. Production medium composition Production medium was always formed by: 100 kg/m3 glucose, 100 kg/m3 sunflower oil, and 1 kg/m3 yeast extract, optimized in a previous work [11]. 3.5. Experimental set-up and operational conditions The runs were carried out in a bioreactor, Infors ISF-101, with a work volume of 1.5 l. Aeration rate was 1 v/v/min, temperature was 30°C, and stirrer speed was 600 rev./min. In all runs, the culture medium was inoculated with a volume of preculture so that an initial biomass concentration of 0.1 kg/m3 was achieved in all the runs. 3.6. Analytical methods Culture samples (10 ml) were withdrawn and extracted with different reagents. First, oil was extracted using n-

616

F. Garcı´a-Ochoa, J.A. Casas / Enzyme and Microbial Technology 25 (1999) 613– 621

Fig. 2. Experimental data obtained in this work and model prediction.

hexane, and as sophorolipid fraction solubilized in n-hexane is very low, it was considered negligible. Afterwards, sophorolipids were extracted using ethyl-acetate. The different fractions were separated by centrifugation in a Hermle centrifuge, Z 230 model, at 2000 ⫻ g for 15 min and analyzed. Biomass concentration was measured as optical density at 650 nm with a Shimadzu UV-1603 spectrophotometer. Calibration was carried out separating cells from broth by centrifugation at 2000 ⫻ g and drying at 80°C until constant weight. Oil concentration was measured by dry weight after n-hexane removal by evaporation. Sophorolipid concentration also was measured by dry weight after ethyl-acetate removal. Changes in glucose concentration in media were analyzed by high-performance liquid chromography, using a Sedex 45 light-scattering detector. The column was a Nucleosil NH2 (5 ␮m) working with a flow rate of 2 ml/min of acetonitrile/water 75/25 (v/v).

duce sophorolipids from glucose and sunflower oil. As can be seen in Fig. 2, sophorolipids are synthesized with high yield only when biomass has reached stationary growth phase. From this moment, oil and glucose are consumed, although glucose is mainly used in sophorolipid production and only a very small proportion in the yeast growth. The proposed kinetic model must be able to describe this behaviour. Experimental data from sophorolipid production have been fitted employing a nonlinear regression technique [15]. Each response (biomass, glucose, product) has been first separately fitted, and then a multi-response fitting method has been applied. Because the data available were integral data, that is, the change in system composition with time, the kinetic model given by the set of Eq. (5) must be integrated, with the following boundary conditions: t ⫽ 0 ⬖ C X ⫽ C X0 ⬖ C S ⫽ C S0 ⬖ C P ⫽ 0 ⬖ C C ⫽ 0

3.7. Fitting method Experimental data fitting has been accomplished by a nonlinear regression method [15]. When the equation to fit could not be integrated analytically, as in the case of product concentration, Eq. (8), a fourth order Runge–Kutta subroutine was coupled to the nonlinear regression technique.

(6)

After this integration, the kinetic model can be formulated in the integral way. The biomass production rate yields the following equation (the logistic equation): CX ⫽

C X0 䡠 exp共 ␮ 䡠 t兲 C X0 1⫺ 䡠 关1 ⫺ exp共 ␮ 䡠 t兲兴 C Xm

(7)

4. Results and discussion Yeast grows by using glucose until depletion of another nutrient, in this case nitrogen, from yeast extract [11]. When biomass reaches stationary growth phase, it begins to pro-

For the sophorolipid production rate integration, it is necessary to use a numerical integration technique, thus data were fitted coupling a fourth-order Runge–Kutta method to the nonlinear regression algorithm:

F. Garcı´a-Ochoa, J.A. Casas / Enzyme and Microbial Technology 25 (1999) 613– 621

617

Table 1 Parameters calculated by fitting of different experimental dataa Author

Parameter

This work

Asmer et al. [4]

Zhou et al. [17]

Lee and Kim [8]

Lang and Wagner [18] a

␮ CXm ␮P YPS ␮ CXm ␮P YPS ␮ CXm ␮P YPS ␮ CXm ␮P YPS ␮ CXm ␮P YPS

Optimal value

Confidence interval at 95% level Lower limit

Upper limit

0.14 5.98 7.3 䡠 10⫺4 0.59 0.21 30.1 3.6 䡠 10⫺4 0.56 0.15 39.7 6.3 䡠 10⫺4 1.02 0.22 25.4 1.1 䡠 10⫺3 0.65 0.25 22.8 5.6 䡠 10⫺4 0.49

0.13 5.64 6.8 䡠 10⫺4 0.56 0.17 25.8 2.4 䡠 10⫺4 0.51 0.1 27.7 3.2 䡠 10⫺4 0.78 0.20 22.7 6.5 䡠 10⫺4 0.59 0.20 20.8 3.5 䡠 10⫺4 0.44

0.15 6.32 7.9 䡠 10⫺4 0.62 0.25 34.3 4.8 䡠 10⫺4 0.63 0.2 51.8 9.4 䡠 10⫺4 1.27 0.24 28.2 1.5 䡠 10⫺3 0.74 0.30 24.9 7.8 䡠 10⫺4 0.56

ts

23.1 38.0 28.9 52.5 11.6 18.8 7.1 22.7 9.3 10.5 5.3 8.9 28.2 26.6 5.9 8.5 12.3 26.1 5.9 17.6

r2

F

854

0.98

846 17 915 155.1

0.98 0.98 0.97

204 1601 74

0.97 0.98 0.97

143 230 428

0.97 0.97 0.99

111 119 373

0.98 0.97 0.97

206 297

0.92 0.99

Tabulated parameter values at 95%: t S ⫽ 2.10, F ⫽ 3.55.

CP ⫽

冕

t

␮ P 䡠 C S 䡠 C X dt

(8)

0

And the sugar production rate can be integrated yielding: C S ⫽ C S0 ⫺

1 1 䡠 共C X ⫺ C X0兲 ⫺ 䡠C Y XS Y PS P

(9)

When the model proposed was fitted to experimental data, good results were not obtained because of the lack of convergence of the algorithm used, until some parameters were fixed, that is, not allowed to float. Biomass concentration was fitted by using Eq. (7). This equation has two parameters, ␮ and C Xm, and both were let to float. Sophorolipid concentration was fitted to Eq. (8), by using only one

Fig. 3. Experimental data from Asmer et al. [4] and model prediction.

618

F. Garcı´a-Ochoa, J.A. Casas / Enzyme and Microbial Technology 25 (1999) 613– 621

Fig. 4. Experimental data from Zhou et al. [17] and model prediction.

parameter, ␮P. Glucose concentration, given by Eq. (9), is only spent on biomass growth and sophorolipid production because maintenance was assumed to be carried out using the glycerol eased in oil catabolism, while the sugar concentration used in cell maintenance can be considered not significant, at least under the operational conditions here employed. This equation has two parameters, Y PS and Y XS. It was necessary to fix the Y XS value in 0.9 kgbiomass/

kgglucose—according with the results achieved in previous works, in which only the yeast growth was studied [11,16]— otherwise values without physical meaning are obtained. The results of the fitting, such as optimal parameter values, confidence interval at the 95% probability level and statistical information (t S, F, r 2 ) obtained, are given in Table 1. As can be seen in this table, all parameters indicate a very good fitting, without any of the cases including the 0

Fig. 5. Experimental data from Lee and Kim [8] and model prediction.

F. Garcı´a-Ochoa, J.A. Casas / Enzyme and Microbial Technology 25 (1999) 613– 621

619

Fig. 6. Experimental data from Lang and Wagner [18] and model prediction.

value inside the confidence interval at the 95% level (all the statistic parameters of the fitting are much greater than those tabulated for 95%). Fig. 2 shows experimental data and kinetic model prediction for the run considered. As can be observed in this figure, the fitting of the model to the experimental results is very good. Thus, the model is able to reproduce adequately the experimental results. 4.1. Validation of the model: fitting of other authors’ data In the literature, several authors have studied sophorolipid production by C. bombicola, but only some works give enough information about system evolution so that a fitting to experimental data can be tried [4,8,17,18]. Data from these authors have been fitted using the kinetic model above described and using the same calculation method above commented. The results of these fittings are also given in Table 1. As can be seen, the fitting obtained to the proposed model is also good in all the cases. For all the fitted experiments, none of the parameters include the 0 value in the confidence interval. Again the statistic parameters of these fittings, F and t S, are greater than those tabulated at the 95% probability level. As can be seen in Figs. 3– 6, where experimental data and those predicted by the kinetic model are shown, data reproduction obtained from these fittings are reasonable and in some cases very good. In Table 2, the operational conditions used in the sophorolipid production for the different authors above-quoted are given. As can be seen in this table, different composition media and production conditions have been used. The kinetic model proposed is able to fit the data from all these experiments. Parameter values calculated in every trial change, depending on medium composition and/or operational conditions.

Yeast growth specific rate (␮) and maximum biomass concentration (C Xm), both parameters giving the biomass growth rate, change depending on production medium composition, mainly with yeast extract concentration. At low yeast extract concentration (1 kg/m3), biomass scarcely grows, and growth specific rate (␮) and maximum biomass Table 2 Medium composition and operational conditions used by different authors Author This work

Asmer et al. [4]

Zhou et al. [17]

Lee and Kim [8]

Lang and Wagner [18]

Medium composition 3

100 kg/m glucose 100 kg/m3 sunflower oil 1 kg/m3 YE 100 kg/m3 glucose 10 kg/m3 YE 1 kg/m3 urea 36 kg/m3 oleic acid. Continuous Fed 100 kg/m3 glucose 5 kg/m3 MgSO4 䡠 7H2O 10 kg/m3 KH2PO4 0.1 kg/m3 NaCl 3 kg/m3 YE 1 kg/m3 urea 105 kg/m3 safflower oil 110 kg/m3 glucose 5 kg/m3 MgSO4 䡠 7H2O 3.3 kg/m3 (NH4)2SO4 1 kg/m3 KH2PO4 0.1 kg/m3 NaCl 0.1 kg/m3 CaCl2 䡠 2H2O 5 kg/m3 YE 100 kg/m3 soya oil 136 kg/m3 glucose and soya oil 10 kg/m3 YE 1 kg/m3 urea

Conditions 600 rev./min 30°C 1 v/v/min 550 rev./min 30°C 0.6 v/v/min

450 rev./min 30°C 0.7 v/v/min

600 rev./min 25°C 1 v/v/min

550 rev./min 30°C 0.6 v/v/min

620

F. Garcı´a-Ochoa, J.A. Casas / Enzyme and Microbial Technology 25 (1999) 613– 621

Fig. 7. Values of ␮ and C Xm calculated for data obtained with different yeast extact concentration in the production medium.

concentration (C Xm) values are low (␮ varies from 0.14/h to 0.15/h and C Xm ⫽ 5 kg/m3). Zhou et al. [17], by using 3 g/l YE, reached a very high C Xm value; nevertheless, the ␮ value calculated shows a value close to those given by other authors. When media with high yeast extract concentration are used, biomass reaches a higher maximum concentration. Both parameters, C Xm and ␮, increase significantly, as can be seen in Fig. 7, which shows ␮ and C Xm values versus YE concentration in production medium. Production rate (␮P) is the parameter giving the sophorolipid synthesis rate. This parameter (␮P) changes with the operational conditions, especially with aeration rate. As can be seen in Fig. 8, when aeration rate is low (0.6 v/v/min), ␮P has a value between 3.6 ⫻ 10⫺4 and 5.6 ⫻ 10⫺4 kgP 䡠 m3/kgS 䡠 kgX 䡠 h. If the aeration rate is higher, ␮P increases

Fig. 8. Values of ␮P and Y XS calculated for data obtained under different values of aeration rate.

reaching a value of 1.1 ⫻ 10⫺3 kgP 䡠 m3/kgS 䡠 kgX 䡠 h [8], although according to our data this parameter only achieved a value of 7.3 ⫻ 10⫺4 kgP 䡠 m3/kgS 䡠 kgX 䡠 h. It seems like high aeration rate enhances sophorolipids production process, but this influence, as shown in Fig. 8, is not very great. Thus, production of sophorolipids by C. bombicola can be improved if aeration rate is increased. The last parameter considered is YPS, a macroscopic yield which shows sophorolipid production and glucose consume relationship. One sophorolipid molecule is formed by two glucose units (C6H12O6) and one hidroxyfatty acid (e.g. C18H36O3), so if all the glucose was used for sophorolipid synthesis, YPS would have a value of ⬃1.8 kgp/kgs; nevertheless, glucose is also used, at least, in biomass growth (YXS ⫽ 0.9 kgX/kgS), and it also can be used as an energy source, and

F. Garcı´a-Ochoa, J.A. Casas / Enzyme and Microbial Technology 25 (1999) 613– 621

in this case this parameter, YPS, has a very low value. As can be seen in Fig. 8, YPS has a value between 0.5 and 0.6 kgX/kgS in most of the cases, although with data from Zhou et al. [17] a parameter value of YPS ⫽ 1 kgP/kgS is obtained.

5. Conclusions A simplified, unstructured, nonsegregated, kinetic model has been proposed for production of sophorolipids by C. bombicola. The model takes into account three responses, biomass, sophorolipid, and glucose concentrations, according to the production rates given by the set of Eq. 5. This kinetic model assumes that the nitrogen—from yeast extract—is the limiting nutrient of the yeast growth, the sugar— glucose—is dedicated to both growth and sophorolipid production, and the oil is supposed to be only spent in the product formation and maintenance of biomass is attributed to glycerol released in the medium by the catabolism of the oil. The model is able to fit not only the experimental data given in this work but also those data given in other works [4,8,17,18], as shown in Figs. 2– 6. The parameter values of the kinetic model— calculated by fitting of experimental data using a nonlinear regression algorithm, coupled with a Runge–Kutta method, given in Table 1— change with medium composition and with operational conditions. As shown in Fig. 7, the parameter values of the yeast growth rate, ␮ and C Xm, change with the value of YE concentration used in the production medium. Fig. 8 shows a small influence of aeration rate on ␮P, the sophorolipid specific constant rate. Acknowledgment This work has been supported by Comision Interministerial de Ciencia y Tecnologia Contract No. BIO97-0596. References [1] Zhou QH, Kosaric N. Utilization of canola oil and lactose to produce biosurfactant with Candida bombicola. JAOCS 1995;72:67–71.

621

[2] Davila AM, Marchal R, Vandecasteele JP. Kinetics and balance of a fermentation free from product inhibition: sophorose lipid production by Candida bombicola. Appl Microbiol Biotechnol 1992;38:6 –11. [3] Cooper DG, Paddock DA. Production of a biosurfactant from Torulopsis bombicola. Appl Environ Microbiol 1984;47:173– 6. [4] Asmer HJ, Lang S, Wagner F, Wray V. Microbial production, structure elucidation and bioconversion of sophorose lipids. JAOCS 1988; 65:1460 – 6. [5] Ito S, Kinta M, Inoue S. Growth of yeasts on alkanes: inhibition by a lactonic sophorolipid produced by Torulopsis bombicola. Agric Biol Chem 1980;44:2221–23. [6] Klekner V, Kosaric N. Biosurfactant for cosmetic. In: Kosaric N, editor. Biosurfactant: production, properties, applications. New York: Marcel Dekker, 1993. p. 65–99. [7] Klekner V, Kosaric N, Zhou QH. Sophorose lipids produced from sucrose. Biotechnol Lett 1991;137:345– 8. [8] Lee HK, Kim JH. Carbon mass balance analysis of sophorose lipid production by Torulopsis bombicola. In: Furusaki, Endo I, Matsumo R, editors. Biochemical engineering for 2001. Berlin: Springer-Verlag, 1993. p. 228 –30. [9] Lee HK, Kim JH. Distribution of sustrates carbon in sophorose lipid production by Torulopsis bombicola. Biotech Lett 1993;15:263– 66. [10] Rau U, Manzke C, Wagner F. Influence of substrate supply on production of sophorose lipids by Candida bombicola ATCC 22214. Biotech Letts 1996;18:149 –54. [11] Casas JA. Produccio´n de soforolı´pidos por Candida bombicola. Ph.D. thesis. Madrid, Spain: Universidad Complutense, 1996. [12] Go¨bbert U, Lang S, Wagner F. Sophorose lipid formation by restingcells of Torulopsis bombicola. Biotech Letts 1984;6:225–30. [13] Tulloch AP, Spencer JFT. Formation of long chain alcohol ester of hydroxy fatty acid sophoroside by fermentation of fatty alcohol by a Torulopsis specied. J Org Chem 1972;37:2868 –70. [14] Ito S, Inoue S. Sophorolipids from Torulopsis bombicola: possible relation to alkane uptake. Appl Environ Microbiol 1982;43:1278 – 83. [15] Marquardt AW. An algorithm for least-squares estimation of non linear parameters. J Soc Indus Appl Math 1963;11:431– 41. [16] Casas JA, Garcia de Lara S, Garcia–Ochoa F. Optimization of a synthetic medium for Candida bombicola growth using factorial design of experiments. Enz Microb Technol 1997;21:221–9. [17] Zhou QH, Klekner V, Kosaric N. Production of sophorose lipids by Torulopsis bombicola from safflower oil and glucose. JAOCS. 1992; 769:89 –91. [18] Lang S, Wagner F. Bioconversion of oil and sugar. In: Kosaric N, editor. Biosurfactant: production, properties, applications. New York: Marcel Dekker, 1993. p. 251– 87.