Medium factor optimization and fermentation kinetics for phenazine-1-carboxylic acid production by...

ARTICLE

Medium Factor Optimization and FermentationKinetics for Phenazine-1-Carboxylic AcidProduction by Pseudomonas sp. M18G

Li He, Yu-Quan Xu, Xue-Hong Zhang

Key Laboratory of Microbial Metabolism, Ministry of Education, College of Life Sciences and

Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P.R.

China; telephone: 86-21-34204854; fax: 86-21-34204854; e-mail: xuehzhang@sjtu.edu.cn

Received 19 July 2007; revision received 22 October 2007; accepted 26 November 2007

Published online 13 December 2007 in Wiley InterScience (www.interscience.wiley.c

ABSTRACT: We investigated the production of biofungicidephenazine-1-carboxlic (PCA) by Pseudomonas sp. M18G, agacA-deficient mutant of M18 for PCA high-production.Glucose was chosen as the optimal carbon source and soypeptone as the nitrogen source. A Plackett–Burman designrevealed that glucose, soy peptone and NaCl were the mostsignificant factors in PCA fermentation. Response surfacemethodology (RSM) and artificial neural network (ANN)models involving the significant factors were developedusing common data. The prediction accuracy of ANNwas slightly higher compared to RSM. The genetic algorithm(GA) was used to search the optimal input space of thetrained ANN model and find the corresponding PCA yield.The optimum composition was found to be: glucose34.3 g L�1, soy peptone 43.2 g L�1, NaCl 5.7 g L�1, andthe predictive maximum PCA yield reached 980.1 mg mL�1.The optimized medium allowed PCA yield to be increasedfrom 673.3 to 966.7 mg mL�1 after verification experimenttests. Additionally, PCA fermentation kinetics was investi-gated. Kinetic models based on the modified Logistic andLuedeking–Piret equations were developed, providing agood description of temporal variations of biomass (X),product ( P), and substrate (S) in PCA fermentation.

Biotechnol. Bioeng. 2008;100: 250–259.

� 2007 Wiley Periodicals, Inc.

KEYWORDS: phenazine-1-carboxylic acid (PCA); responsesurface methodology (RSM); artificial neural network(ANN); genetic algorithm (GA); kinetic models; optimiza-tion

Introduction

Some strains of fluorescent pseudomonads can protectplants against a range of agricultural fungal disease as plantgrowth-promoting rhizobacteria (PGPR) by colonizing the

Correspondence to: X.-H. Zhang

Contract grant sponsor: 863 Programs of China

Contract grant sponsor: Shanghai Leading Academic Discipline Project

250 Biotechnology and Bioengineering, Vol. 100, No. 2, June 1, 2008

roots (Loper, 1988; Thomashow et al., 1990; Weller andCook, 1983). Secondary metabolites, including phenazines,phloroglucinols, pyoluteorin, pyrrolnitrin, cyclic lipopep-tides, and hydrogen cyanide, have been shown to play asignificant role in disease suppression by pseudomonads(Haas and Defago, 2005).

It is reported that Phenazine-1-carboxylic acid (PCA) isactive against several soil-borne fungal pathogens includingGaeumannomyces graminis (Gurusiddaiah et al., 1986),Phytophthora capsici and Colletotrichum orbiculare (Leeet al., 2003). Shenqinmycin (the commercial name of PCA),has high efficiency low toxicity, good environmentalcompatibility and is the original pesticide developedindependently in China. The product has gained PesticideRegistration Certification as issued by the Ministry ofAgriculture (code LS20031381). It has been proven that theprevention and cure effect of Shenqinmycin for withering ofwatermelon sprout (Fusarium oxysporum) and pimientoepidemic disease (P. capsici) can reach over 70%, plus a goodcure effect for melon gummy stem blight (Mycosphaerallamelonis) can be achieved.

In order to make PCA available as a biofungicide inagriculture, the fermentation process of PCA needs to bestudied in order to improve the yield and lower theproduction cost. Although some liquid-culture technologieson PCA production by Pseudomonas fluorescens 2–79 havebeen reported (Slininger and Jackson, 1992; Slininger andShea-Wilbur, 1995; Slininger et al., 1996), the factors in PCAfermentation have not yet been synthetically studied, norany reports made of the kinetic model which describes PCAproduction.

Pseudomonas sp. strain M18 is a root-colonizingbiocontrol agent isolated from the rhizosphere of sweetmelon (Hu et al., 2005), and is the only strain reported todate which produces both PCA and pyoluteorin. M18G, agacA-deficient mutant of M18, is used in current work. Itwas found that in M18G pyoluteorin production wasinhibited completely, but PCA production was increased by

� 2007 Wiley Periodicals, Inc.

up to 30-fold above that in the wild-type strain (Ge et al.,2004). In order to optimize PCA production by M18G,factors in fermentation were investigated, including: carbonsource, nitrogen source, inorganic salts and pH, etc. Toscreen for the significant factors amongst a large number ofvariables, the Plackett–Burman design was used.

In the field of modeling and optimization, the traditionalmethod has shortcomings in describing the relationshipsbetween the combinations of factors (Kalil et al., 2000).Response surface methodology (RSM), employing a fullsecond-order polynomial to depict the relationships, is afrequently used optimization technique in many areas ofbiotechnology (Adinarayana and Ellaiah, 2002; Gheshlaghiet al., 2005; Ribeiro et al., 2003). The artificial neuralnetwork (ANN), which is different to the linear RSM model,has advantages in constructing multi-variate non-linearmodels, and is appropriate to depict the combined effect offactors in complicated bioprocess. In recent years, ANN hasbeen successfully applied to optimize reaction conditions(Huang et al., 2007), estimate cell growth models (Garcıa-Gimeno et al., 2005), and realize the on-line adaptive controlin fed-batch fermentation (Duan et al., 2006). Geneticalgorithm (GA), based on principles of evolution, is anefficient tool used to search the optimum inputs predictedby the ANN model (Erenturk and Erenturk, 2007; Izadifarand Jahromi, 2007). In this work, the RSM and ANN modelsfor PCA yield were constructed using common data toenhance PCA yield; the prediction accuracy of both modelswas compared. The optimum condition for PCA fermenta-tion was found by using a coupled ANN model and GA.

The development of kinetic models is necessary forunderstanding, controlling, and optimizing fermentationprocesses. In this work, the course of PCA fermentation wasseparated into three stages by different characters in cellgrowth, product yield, and substrate consumption. Classicalkinetic models were picked, combined, and simplifiedaccording to the features of the different stages. Kineticmodels for PCA fermentation were developed, describingtemporal variations of biomass (X), product ( P), andsubstrate (S).

Materials and Methods

Strain and Chemicals

The strain used in this study was Pseudomonas sp. strainM18G. It was the insertional gacA mutant strain of M18,which was isolated from the rhizosphere of sweet melon.Aggregated bacteria were suspended in glycerol (20%, byvolume) and stored at �708C. All chemicals used werepurchased from Sinopharm Chemical Reagent Co. Ltd(Shanghai, China).

Media and Culture Conditions

The strain was cultured on KB plates (Sambrook et al., 1989)containing (L�1): peptone 20 g; glycerol 15 mL; K2HPO4

H

0.392 g; MgSO4 0.732 g; agar 15 g; pH 7.5. The plates wereincubated at 288C for 12 h.

For seed preparation, a loop of cells was inoculated into50 mL of KB medium in 250-mL Erlenmeyer flasks andincubated for 10.5 h at 288C with shaking at 166 rpm on arotary shaker.

The fermentation medium contained: carbon source,nitrogen source, NaCl, MgSO4, K2HPO4, all as required inthe experimental designs. For batch fermentation, the seedculture was inoculated at 5% (by volume) into 100 mL of thefermentation medium in 250-mL flasks. The inoculatedflasks were kept on a rotary shaker at 166 rpm at 288C.

Analytical Methods

For biomass detection, 250 mL of cultures were centrifugedat 10,000g for 10 min. The cell pellet was suspended with3 mL distilled water. Optical density was measured by UVspectrometer (model UV-2000; UNICO, Dayton, NJ) at600 nm; the path-length of a cuvette was 1 cm. Dry cellweight was quantified with sample cultures dried in a hot-airoven at 608C for 24 h.

DCW ¼ 8:16ðOD600Þ þ 0:89 (1)

where DCW is dry cell weight, g L�1; OD600 is optical densityat 600 nm.

PCA extraction and detection was done according toreference (Ge et al., 2004).

For glucose concentration estimation, 100 mL cultureswere diluted to 2 mL with distilled water, added with 1.5 mL3,5-dinitrosalicylic acid reagent (Miller, 1959) and thentreated by the boiling water for 5 min. Samples were dilutedto 15 mL and then detected by UV spectrometer at 540 nm.

Cglucose ¼ 10:53ðOD540Þ þ 0:02 (2)

where Cglucose is glucose concentration, g L�1; OD540 isoptical density at 540 nm.

Plackett–Burman Design

The purpose of the Plackett–Burman experiment was toidentify the factors which have a significant effect on PCAproduction. Table I shows the Plackett–Burman design(Plackett and Burman, 1946) used in this study. Fourdummy variables, with levels which do not change in thedesign, were introduced to estimate the population standarddeviation. Analysis for the Plackett–Burman experiment wascarried out as follows:

First, the effect of all variables, including dummies, wascalculated.

EVi ¼P

RViðþÞ �P

RVið�ÞN=2

(3)

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Biotechnology and Bioengineering

Table I. Plackett–Burman designs.a

Run

no.

Variables/levelsPCA yield

(mg mL�1)A Bb C D Eb F G Hb I J Kb

1 1 �1 1 �1 �1 �1 1 1 1 �1 1 711.9� 79.8

2 1 1 �1 1 �1 �1 �1 1 1 1 �1 421.1� 27.1

3 �1 1 1 �1 1 �1 �1 �1 1 1 1 555.9� 28.8

4 1 �1 1 1 �1 1 �1 �1 �1 1 1 826.5� 70.2

5 1 1 �1 1 1 �1 1 �1 �1 �1 1 421.1� 34.9

6 1 1 1 �1 1 1 �1 1 �1 �1 �1 711.9� 52.6

7 �1 1 1 1 �1 1 1 �1 1 �1 �1 667.2� 85.4

8 �1 �1 1 1 1 �1 1 1 �1 1 �1 527.2� 33.8

9 �1 �1 �1 1 1 1 �1 1 1 �1 1 354.9� 40.2

10 1 �1 �1 �1 1 1 1 �1 1 1 �1 296.4� 40.6

11 �1 1 �1 �1 �1 1 1 1 �1 1 1 203.5� 15.7

12 �1 �1 �1 �1 �1 �1 �1 �1 �1 �1 �1 243.5� 39.0

aEach experimental data point represents the mean� SD from threeindependent samples.

bRepresents a dummy variable; �1 and 1 represent the low and highlevels, respectively.

Table IIA. Independent variables and experimental design levels for CCD.

Independent

variables

Coded

symbols

Level

�1.68 �1 0 1 1.68

Glucose, g L�1 X1 13.2 20.0 30.0 40.0 46.8

Soy peptone, g L�1 X2 13.2 20.0 30.0 40.0 46.8

NaCl, g L�1 X3 0.0 2.0 5.0 8.0 10.0

Table IIB. Experimental and predicted data of PCA yield for constructing

the RSM and ANN models, construction set.a

Run

no. Glucose

Soybean

peptone NaCl

PCA (mg mL�1)

OBS RSM ANN

1 1 1 1 730.2� 76.6 739.3 720.2

2 1 1 �1 780.5� 90.1 741.5 769.6

3 1 �1 1 266.7� 29.6 207.2 265.7

4 1 �1 �1 224.5� 30.4 209.4 224.3

5 �1 1 1 783.1� 96.9 791.2 772.1

6 �1 1 �1 837.5� 86.2 793.4 825.5

7 �1 �1 1 622.6� 51.5 539.0 614.7

8 �1 �1 �1 538.3� 63.2 541.2 532.0

9 �1.68 0 0 536.2� 29.7 579.0 530.0

10 1.68 0 0 221.2� 19.0 256.7 221.1

11 0 �1.68 0 214.2� 14.8 280.0 214.2

12 0 1.68 0 926.2� 26.4 938.8 926.2

13 0 0 �1.68 702.4� 52.8 732.9 693.0

where EVi is the effect of variable i, mg mL�1; RVi(þ) andRVi(�) represent the response parameter (PCA yield) of anassembly in the screening design which contains the highand low levels of variable i, respectively, mg mL�1; N isnumber of assemblies (N¼ 12).

After determining the effect of each variable, the standarddeviation (SD) of dummies, which serves as the populationstandard deviation in the Student’s t-test, was calculated.

SD ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPðEdÞ2

n

s(4)

where Ed is the effect of dummy variables; n is number ofdummy variables (n¼ 4).

Finally, a Student’s t-test was performed to identify thesignificant factors.

tVi ¼EVi

SD(5)

The variable which has no effect will give a t-value of 0.The larger the absolute value of the t-value, the moresignificant is the variable.

14 0 0 1.68 680.1� 80.7 729.2 671.1

15b 0 0 0 868.5� 16.8 828.8 819.4

16b 0 0 0 788.3� 77.4 828.8 819.4

17b 0 0 0 856.5� 98.3 828.8 819.4

18b 0 0 0 853.4� 92.0 828.8 819.4

19b 0 0 0 772.6� 66.4 828.8 819.4

20b 0 0 0 848.4� 97.8 828.8 819.4

RMSE 40.89 20.12

SEP 6.27% 3.08%

aEach experimental data point represents the mean� SD from threeindependent samples.

bRepresents center point conditions; OBS, experimental yield, mg mL�1;RSM, predictive yield by RSM model, mg mL�1; ANN, predictive yield byANN model, mg mL�1.

Response Surface Methodology

RSM is a frequently used technique of building models anddetermining the optimal process conditions. The relation-ship amongst the significant variables identified by thePlackett–Burman experiment was expressed in a second-order equation:

Y ¼ A0 þX

AiXi þX

AiiX2i þ

Xi 6¼j

AijXiXj (6)

252 Biotechnology and Bioengineering, Vol. 100, No. 2, June 1, 2008

where Y is the predictive PCA yield, mg mL�1; A0 is constant;Ai, Aii, Aij are regression coefficients of the model; Xi and Xj

represent the independent variables; XiXj represents theinteraction terms.

A central composite design (CCD) for three factors usingfive coded levels was employed (Table IIB). The extremelevel of axial points was chosen to be 1.68 (Table IIA) inorder to make this design rotatable (Montgomery, 2001).The fermentation medium in this experiment contained(L�1): glucose, soy peptone, NaCl (all above as required inCCD), MgSO4 2 g, K2HPO4 0.5 g, pH 7.5, and liquid volume80 mL.

Artificial Neural Network

A feed forward network with error back propagation (BP)algorithm was used; the tangent sigmoid function

[f(x)¼ tanh(x)] was used as the transfer function in thenetwork. All factors which have significant influence wereincluded as input variables, and PCA yield as outputvariable. The network architecture was determined asreference (Huang et al., 2007). All the variables (inputand output ones) were scaled in the rank [0.1, 0.9] due totheir different measurement ranges to avoid saturationproblems in the sigmoidal function in the network (Haykin,1994). The new scaled variables were obtained as follows:

X� ¼ 0:8X � Xmin

Xmax � Xmin

þ 0:1 (7)

These values were de-scaled by:

X ¼ ðXmax � XminÞðX� � 0:1Þ0:8

þ Xmin (8)

To evaluate the fitting and prediction accuracy of RSMand ANN models, root mean squares error (RMSE) andstandard error of prediction (SEP; Hervas et al., 2001) wereemployed:

RMSE ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPðYi;e � Yi;pÞ2

n

s(9)

SEP ¼ RMSE

Ye

100% (10)

where Yi,e is the experimental data; Yi,p is the correspondingpredictive data; Ye is the mean value of experimental data; nis the number of the experimental data.

Training an ANN network, aimed at minimizing theRMSE and SEP, was accomplished by utilizing MATLABsoftware (version 7.1; The MathWorks, Natick, MA).

Genetic Algorithm

GA is an optimization technique based on principles ofevolution and can be applied to optimize non-linearproblems with multi-variables (Goldberg, 1989). GA canbe simply described as follows (Izadifar and Jahromi, 2007).First, a population of individuals (genes) is randomlygenerated. A fitness value is assigned to each individual byspecific fitness function. Individuals with higher fitnessvalues are selected and undergo genetic operation such ascrossover and mutation. The newly generated childpopulation serves as the parent population for the nextgeneration and is treated with the same evolutional processcontinuously until the stop criterion has been satisfied.

The trained ANN model is used as the fitness function ofGA in this type of work. The individuals in GA are numberscontaining full information of the condition for PCAfermentation. The MATLAB Genetic Algorithm Toolbox

H

was used to search the optimal input space of the model andfind the corresponding optimum output.

Kinetic Models

The kinetic models for PCA fermentation include temporalvariations of biomass (X, dry cell weight, g L�1), product ( P,PCA yield, g L�1), and substrate (S, glucose, g L�1).

Microbial Growth: The Modified Logistic Equation

Some features of PCA fermentation process could beconcluded from previous work (data not shown).

First, similar biomass values were detected in thestationary phase of batch fermentations with differentinitial glucose concentrations, which indicated that theM18G fermentation process does not follow the classicalkinetic model of substrate-limited biomass growth proposedby Monod (1949). Therefore, the substrate-independentLogistic equation is used to describe the biomass growth.

Second, the substrate inhibition to the specific growthrate was detected in fermentations with different initialglucose concentrations. The Andrews model (Andrews,1968), describing this inhibition effect, was employed tomodify the Logistic equation. The modified Logisticequation can be described as follows:

dX

dt¼ mm 1 � X

Xm

� �1

1 þ S=Ki

� �X (11)

where mm is the maximum specific growth rate, h�1; Xm isthe maximum attainable biomass concentration, g L�1; Ki isthe substrate inhibition constant, g L�1.

Production Formation: The ModifiedLuedeking–Piret Equation

The kinetics of product formation was based on theLuedeking–Piret’s equation (Luedeking and Piret, 1959).The Bajpai model (Bajpai and Reuss, 1980), a non-competitive inhibition model once used in penicillinfermentation, was introduced to describe the effect ofsubstrate concentration on the specific product formationrate. The modified Luedeking–Piret equation can bedescribed as follows:

dP

dt¼ K1

dX

dtþ K2

S

S þ Ksp

� �1

1 þ S=Kip

� �X (12)

where K1 and K2 (h�1) are the product formation constants;Ksp and Kip are the substrate inhibition constants, g L�1.The larger the K1 value, the more growth-associated isthe fermentation process, while the K2 value represents thenon-growth-associated process.

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Biotechnology and Bioengineering

Glucose Uptake: The Luedeking–Piret Equation

There is a classical kinetic model of substrate uptakeproposed by Luedeking and Piret (1959).

� dS

dt¼ 1

YX=S

dX

dtþ 1

YP=S

dP

dtþ mX (13)

where YX/S is the cell yield coefficient for glucose, g g�1; YP/S

is the product yield coefficient for glucose, g g�1; m is themaintenance coefficient, h�1.

Figure 1. Effects of different carbon sources on PCA production and cell

growth.

Model Fit and Computational Technique

To evaluate the fitting accuracy of the kinetic models, therelative error sum of squares (RESS) was employed:

RESS ¼Xn

i¼1

Xi;e � Xi;p

Xi;e

� �2

þ Pi;e � Pi;p

Pi;e

� �2

þ Si;e � Si;p

Si;e

� �2" #

(14)

where Xi,e, Pi,e, and Si,e are the observed value of biomass,PCA products, and substrate concentration, respectively;Xi,p, Pi,p, Si,p are the corresponding fitted value by kineticmodels; n is the number of experimental data. RESS wasemployed because the statistic is not dependent on themagnitude of the measurements.

Estimates for model parameters were obtained byminimizing RESS. Fourth-order Runge-Kutta method wasused to solve the differential equation. Simplex method wasused to search the parameter value. All the numericalcalculations were performed by the MATLAB software.

The performance of the models was evaluated by usingcorrelation coefficients (r):

r ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1 �

Pn

i¼1

ðYi;e � Yi;pÞ2

Pn

i¼1

ðYi;e � YeÞ2

vuuuuut (15)

where Yi,e is the experimental data; Yi,p is the correspondingfitted value by kinetic model; Ye is the mean value ofexperimental data; n is number of experimental data. Thecloseness to a value of 1 is an effective and practical measureof the validity of model prediction (Wang et al., 2006).

Figure 2. Effects of different nitrogen sources on PCA production and cell

growth. S.Peptone, soy peptone; B.Extract, beef extract; Y.Extract, yeast extract.

Results and Discussion

Effects of Different Carbon Sources andNitrogen Sources

The growth of cells and the yield of PCA were investigatedusing seven different carbon sources: glucose, fructose,lactose, sucrose, maltose, glycerol, and ethanol. The fermen-tation medium contained (L�1): carbon source 8 g (by

254 Biotechnology and Bioengineering, Vol. 100, No. 2, June 1, 2008

carbon element), peptone 22 g, NaCl 3 g, MgSO4 0.5 g,K2HPO4 0.5 g, pH 7.5, and cultivation was carried outfor 48 h. As shown in Figure 1, the maximum cell growth(4.462 g L�1) and the synthesis of PCA (455.0 mg mL�1)were observed when glucose served as the carbon source.The reductive monosaccharide, involving glucose andfructose, was more efficient than the other carbon sources.

The similar nitrogen source choice was conducted using:peptone, soy peptone, beef extract, yeast extract, (NH4)2SO4,CO(NH2)2, and NH4Cl. The fermentation medium con-tained (L�1): nitrogen source 2.8 g (by nitrogen element),glucose 20 g, NaCl 3 g, MgSO4 0.5 g, K2HPO4 0.5 g, pH 7.5.As shown in Figure 2, the maximum cell growth(6.320 g L�1) was achieved by using yeast extract, whilethe optimal nitrogen source for PCA production

Table IV. RSM model coefficients estimated by multiple linear regression.

Term Coefficients estimated t-value P-value

X1 57.55 5.26 0.0003

X2 50.07 4.58 0.0007

X3 72.35 2.26 0.0250

X21 �1.46 �10.16 1.6E-06

X2 �0.78 �5.42 0.0002

(673.3 mg mL�1) was soy peptone. The result was differentto the report (Slininger and Shea-Wilbur, 1995) in that thesimpler inorganic sources of ammonia were not good for cellgrowth and PCA production in Pseudomonas fluorescens 2–79. Also, PCA production by M18G was not significantlypH-dependent (data not show), so pH-control was notintroduced in fermentation.

2

X23 �3.91 �2.42 0.0192

X1X2 0.70 3.64 0.0027

X1X3 �0.16 �0.25 0.4051

X2X3 �0.96 �1.50 0.0834

Table III shows the Plackett–Burman design and analysis.The SD, which was calculated using dummy variables, wasnot equal to 0 (Table III), suggesting the existence ofinteractions amongst factors in the fermentation (Ahujaet al., 2004). P-values less than 0.05 indicate factors whichare significant at the probability level of 95%. The P-value of0.0004 for soy peptone showed it had the most significantpositive effect on PCA production. Also, increasing the levelof glucose and NaCl enhanced the PCA productionmarkedly, where as MgSO4, K2HPO4, initial pH and theliquid volume did not significantly influence PCA produc-tion within the levels tested.

Glucose is the main source of phosphoenolpyruvate anderythrose 4-phosphate, which are transferred to chorismatein metabolism. Chorismate is the important precursor ofphenazine compounds. Soy peptone and NaCl, maintainingcell growth and appropriate conditions for PCA formation,are the other two significant factors. These three factors wereoptimized in the next step.

RSM Model

The relationship amongst the three above-mentionedsignificant factors was investigated using RSM. A CCDwas performed, and the levels of glucose (X1), soy peptone(X2), and NaCl (X3) were optimized. In accordance with theexperimental data of CCD (Table IIB), regression coeffi-cients, Student’s t-test values and P-values for the RSM

Table III. Screening of significant factors for PCA production.a

Variable LevelsEffect

(mg mL�1)

Relative

Significance

Code Term Low(�) High(þ) t-test P-value

A Glucose (g L�1) 15 30 139.5 5.52 0.0059

B Dummy . . . . . . 3.4 0.13 0.4510

C Soy peptone (g L�1) 15 30 343.4 13.58 0.0004

D NaCl (g L�1) 1 5 82.5 3.26 0.0235

E Dummy . . . . . . �34.4 �1.36 0.1335

F MgSO4 (g L�1) 0.5 2 30.0 1.18 0.1607

G K2HPO4 (g L�1) 0.5 2 �47.8 �1.89 0.0777

H Dummy . . . . . . �13.4 �0.53 0.3170

I Initial pH 6.5 7.5 12.3 0.49 0.3302

J Liquid volume (mL) 80 100 �46.7 �1.85 0.0811

K Dummy . . . . . . 34.4 1.36 0.1333

aStandard deviation (SD)¼ 25.3 mg mL�1.

H

model of PCA yield were presented in Table IV. Also, P-values less than 0.05 indicate factors which are significant atthe probability level of 95%. The non-significant terms, thatis, X1X3 and X2X3, were excluded from the model. The fittedequation of RSM model was obtained considering onlysignificant terms to predict Y.

Y ¼ �947:46 þ 56:76X1 þ 45:25X2 þ 38:70X3

� 1:46X21 � 0:78X2

2 � 3:91X23 þ 0:70X1X2 (16)

The F-test of RSM model was as follows: Fregression ¼43.43> F0.05(8,11)¼ 2.95, so the regression of RSM model issignificant. The determination coefficient (R2) of 0.969indicated 96.9% of the variability in the response could beexplained by the model.

ANN Model

The ANN architecture consists of three neurons (glucose,soy peptone, and NaCl) in the input layer, six neurons in thehidden layer, and one neuron (PCA) in the output layer(topology 3-6-1). The optimal number of neurons inthe hidden layer of ANN was investigated as the reference(Huang et al., 2007). The network with six neurons in thehidden layer gave the lowest error. Common data used inconstructing the RSM model were selected for training theANN model. Training was accomplished by MATLABNeural Network Toolbox. The performance of the ANNmodel was compared to the RSM model as follows:

Two L4(23) orthogonal tables were employed to build twovalidation sets for testing the models. As recommended bythe RSM model, the optimal conditions for PCA productionwere glucose 29.7 g L�1, soy peptone 42.5 g L�1, NaCl 5.0g L�1, and the predictive maximum PCA yield reached 952.5mg mL�1. The recommended component levels were set asthe higher levels in both validation sets, the lower levels invalidation sets 1 and 2 were chosen to be 80% and 60% oftheir higher levels, respectively. Validation set 1 (Table VA)showed the fitting accuracy near the crest of the responsesurface (the predictive higher-PCA yield area); whilevalidation set 2 (Table VB) represented the models’

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Biotechnology and Bioengineering

Table VA. Experimental and predicted data of PCA yield for validating the RSM and ANN models, validation set 1.a

Run no. Glucose (g L�1) Soybean peptone (g L�1) NaCl (g L�1)

PCA (mg mL�1)

OBS RSM ANN

1 29.7 42.5 5.0 930.5� 64.1 952.5 935.4

2 29.7 34.0 4.0 847.7� 26.7 893.1 877.7

3 23.8 42.5 4.0 825.7� 52.7 898.1 851.2

4 23.8 34.0 5.0 750.7� 45.5 880.9 780.7

RMSE 78.66 36.59

SEP 9.38% 4.36%

aIn this validation set, the lower levels of components were chosen to be 80% of their higher levels. Each experimental data point represents the mean� SDfrom three independent samples.

performance in the periphery of the crest (the predictivelower-PCA yield area).

As shown in Table IIB (construction set), the RMSE forPCA yield given by ANN for the training data is 20.12, whileRSM gives 40.89. The results demonstrated that the ANNmodel gave a better fit to the training data compared to theRSM model.

In the case shown in Table VA, SEP for PCA yield by ANNis 4.36%, while RSM gives 9.38%. It indicated that the ANNmodel performed a better prediction near the high-PCAyield area than the RSM model. To search the optimumconditions for PCA fermentation, an accurate prediction inthe area of the maximum is welcome. The large SEP valuesgiven by both models (Table VB) indicated that neithermodel was able to give a satisfying prediction when levels ofcomponents were chosen from the periphery of the high-PCA yield area.

In terms of the global view (Table VB), the ANN modelgives total RMSE equal to 44.41 and total SEP equal to 6.50%while the RSM gives 54.77 and 8.02%. Both models providedsimilar quality predictions for the three independentvariables in terms of PCA yield, while the ANN modeldid slightly better.

Optimization of PCA Yield by GA

The trained ANN model was used with GA to identify theoptimal set of input conditions which yield a maximum

Table VB. Experimental and predicted data of PCA yield for validating the R

Run no. Glucose (g L�1) Soybean peptone (g L�1)

1 29.7 42.5

2 29.7 25.5

3 17.8 42.5

4 17.8 25.5

RMSE

SEP

tRMSE

tSEP

aIn this validation set, the lower levels of components were chosen to be 60including the construction set (Table IIB), validation set 1 (Table VA) and validatfrom three independent samples.

256 Biotechnology and Bioengineering, Vol. 100, No. 2, June 1, 2008

output. The computations for GA were performed using theMATLAB Genetic Algorithm Toolbox. As shown in Figure 3,after 216 generations, the population with the best fitnessvalues was generated. The optimum conditions were found asglucose 34.3 g L�1, soy peptone 43.2 g L�1, NaCl 5.7 g L�1, andthe predictive maximum PCA yield reached 980.1 mg mL�1.The average value of three experimental PCA yields under theabove conditions was 966.7� 54.6 mg mL�1. Compared withthe PCA yield of 930.5� 64.1 mg mL�1 under the conditionsrecommended by the RSM model, the ANN model performedbetter in the optimization work.

Batch Fermentation and Kinetic Models Analysis

Under the conditions of initial glucose 20.0 g L�1, soy pep-tone 40.0 g L�1, NaCl 5.0 g L�1, MgSO4 0.5 g, K2HPO4 0.5 g,initial pH 7.5, liquid volume 100 mL, batch fermentationwas conducted. As shown in Figure 4, the PCA fermentationprocess could be separated into three stages, as follows:

Stage I: 0–8 hAn 8-h exponential phase with high specific growth rate

was observed; in this phase, PCA could accumulate onlyminimally.Stage II: 8–36 h

After the initial biomass accumulation, the cell began togrow with a linear decreasing specific growth rate (Fig. 5)after 8 h. Evident accumulation of PCA began after 8 h and

SM and ANN models, validation set 2.a

NaCl (g L�1)

PCA (mg mL�1)

OBS RSM ANN

5.0 930.5� 64.1 952.5 935.4

3.0 545.7� 39.0 713.6 696.5

3.0 756.7� 40.4 731.2 822.4

5.0 490.7� 27.4 663.5 581.9

121.66 102.20

17.87% 15.01%

54.77 44.41

8.02% 6.50%

% of their higher levels; tRMSE, tSEP: RMSE and SEP for total data sets,ion set 2 (Table VB). Each experimental data point represents the mean� SD

Figure 3. Evolution of best fitness by genetic algorithm.

Figure 5. Specific growth rate versus dry cell weight. Symbols: D, stage I, 0–8 h,

dry cell weight increased to 2.4 g L�1; &, stage II, 8–36 h, dry cell weight increased to

6.2 g L�1. The solid line represents the linear regression result in stage II, R¼ 0.87.

yielded continuously till 34 h. Substrate was consumedsharply during this stage. Cell growth came into thestationary phase after 28 h. The maximum PCA concentra-tion (870.3 mg mL�1) was observed at 36 h. The residualglucose concentration of the fermentation broth decreasedto 1.5 g L�1 at 36 h.Stage III: 36–44 h

Cell growth and substrate consumption stopped. LittlePCA was formed in this stage, so the data points after 36 hwere excluded from the kinetic models.

Considering the different character of the first two stages,the model describing the microbial growth (Eq. 11) could besimplified to two equations. In stage I, the inhibition to thespecific growth rate is mainly contributed by substrate (S)for the relatively low initial cell concentration (X) duringthis stage. A simplified substrate inhibition model (Eq. 17)could be used, and the inhibition effect of the prospectivemaximum biomass (Xm) neglected.

dX

dt¼ mm;1

1

1 þ S=Ki;1

� �X (17)

Figure 4. Time course of batch fermentation of M18G. Symbols: &, dry cell

weight; *, PCA; D, glucose. The solid lines represent model predictions.

H

Stage I is the non-PCA yield phase; the product formationand substrate uptake models could also be simplified.

dP

dt¼ 0 (18)

� dS

dt¼ 1

YX=S;1

dX

dtþ m1X (19)

In stage II, the substrate inhibition effect on the specificgrowth rate is not significant, which is supported by thelinear relationship between dry cell weight (X) and specificgrowth rate ((dX/dt)/X), as shown in Figure 5. Equation(11) could be simplified to a classical Logistic equation, withthe substrate inhibition neglected.

dX

dt¼ mm;2 1 � X

Xm;2

� �X (20)

The simplified models and model parameters of the twostages are shown in Tables VIA and VIB. The parameter m1

equal to 0 represents that all the glucose consumption is forthe cell growth in stage I. YX/S,1 is 0.737 g g�1 and confirmsthe classical cell yield coefficient approximately, which is0.95 g g�1 when Pseudomonas fluorescens utilize glucose(Nagai, 1979).

In stage II, the growth-associated constant K1,2 is 0.001,the non-growth-associated constant K2,2 is 0.043 h�1. Incomparison, K2,2 is more than 40 times as K1,2, whichindicated that the PCA formation was a weak growth-associated fermentation process. In accord with the

e et al.: Medium Optimization and Fermentation Kinetics for PCA 257

Biotechnology and Bioengineering

Table VIA. Kinetic models and estimation of model parameters of stage I.

Model Parameter Value

dXdt ¼ mm;1

11þS=Ki;1

�X mm,1, h�1 0.323

Ki,1, g L�1 10.826

dPdt ¼ 0

� dSdt ¼ 1

YX=S;1

dXdt þ m1X YX/S,1, g g�1 0.737

m1, h�1 0.000

traditional antibiotics fermentation process, biosynthesisdoes not start simultaneously with growth; PCA will notaccumulate until the cell density meets a certain level. Cellyield coefficient for glucose YX/S,2 is 5.709 g g�1, muchdifferent to the classical value (0.95 g g�1), which indicatedthat glucose was not the sole carbon source in stage II. Cellgrowth here might significantly be due to absorbing thecarbon skeleton of amino acids provided by soy peptone.

In PCA batch fermentation the above kinetic modelsbased on the Logistic and Luedeking–Piret equationsprovide a good description of cell growth, PCA formation,and substrate consumption (Fig. 4). The correlationcoefficients of cell growth, PCA formation, and substrateconsumption were 0.993, 0.997, and 0.970, respectively,which demonstrated there was good correspondencebetween the experimental results and model predictions.

A Supposed Fed-Batch Fermentation Strategy

Equation (17) indicated that a relatively low glucoseconcentration enhanced the specific growth rate in theinitial exponential phase (stage I). In practice, a modest C/Nratio appropriate for cell growth should be determinedaccording to the conditions of bioreactor.

In the PCA-yield phase (stage II), the glucose concentra-tion should be controlled at a particular level to maximizethe specific product formation rate (Eq. 12). As shown inEquation (20), the cell growth was independent from thesubstrate concentration in stage II, so X and dX/dt inEquation (12) could be treated as constants. The optimalsubstrate concentration in stage II was found by searching

Table VIB. Kinetic models and estimation of model parameters

of stage II.

Model Parameter Value

dXdt ¼ mm;2 1 � X

Xm;2

�X mm,2, h�1 0.107

Xm,2, g L�1 6.511

dPdt ¼ K1;2

dXdt þ K2;2

SSþKsp;2

�1

1þS=Kip;2

�X K1,2 0.001

K2,2, h�1 0.043

Ksp,2, g L�1 19.020

Kip,2, g L�1 11.648

� dSdt ¼ 1

YX=S;2

dXdt þ 1

YP=S;2

dPdt þ m2X YX/S,2, g g�1 5.709

YP/S,2, g g�1 0.287

m2, h�1 0.104

258 Biotechnology and Bioengineering, Vol. 100, No. 2, June 1, 2008

the maximum value of the following function:

f ðSÞ ¼ S

S þ Ksp

� �1

1 þ S=Kip

� �

¼ Kip

S þ ðKsp Kip=SÞ þ Ksp þ Kip

(21)

when S¼ (KspKip)/S, the function reaches the maximum.Under the conditions of this experiment, the optimalsubstrate concentration is:

Soptional ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiKsp Kip

p¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi19:020 11:648

p¼ 14:9 g L�1

(22)

The fed-batch strategy can be realized by following anoptimal feeding rate function determined by Pontryagin’smaximum principle, Green’s theorem, or GA. It can also beaccomplished by a PID or On-off feedback controller withthe cooperation of an on-line glucose electrode. A flaskexperiment using pulse feeding to approximate this strategywas done and higher PCA production achieved (data notshown). The further validation and optimization infermentors are to be studied.

Conclusions

Compared to chemical methods, the biosynthesis of PCA ismore attractive due to inexpensive materials, mild reactionconditions, and environmental compatibility. In this study,PCA fermentation based on a simple medium byPseudomonas sp. strain M18G was investigated. Glucosewas selected as the optimum carbon source, which confirmsother reports (Slininger and Shea-Wilbur, 1995), and soypeptone as the nitrogen source. The significant factors forPCA fermentation, that is, glucose, soy peptone and NaCl,were identified by the Plackett–Burman experiment. Thenext step was to build and test the RSM and ANN modelsinvolving the above three factors. The fitting and predictionaccuracy of the two models were compared. The resultdemonstrated that the ANN model (combined with GA)performs better in predicting and optimizing the multi-factor bioprocess. PCA yield in batch fermentation increasedfrom 673.3 to 966.7 mg mL�1 under the conditionsrecommended by the ANN model, which is glucose 34.3g L�1, soy peptone 43.2 g L�1, and NaCl 5.7 g L�1. Finally,the kinetic models depicting the time course of batchfermentation were developed. Cell growth, PCA formation,and substrate consumption were described satisfactorilyusing differential equations. The kinetic informationobtained here and the supposed fed-batch strategy wouldbe helpful to the industrial bioprocess to improveproductivity and efficiency.

This study was supported by the 863 Programs of China

(2006AA10A209) and Shanghai Leading Academic Discipline Project

(project number: B203).

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