Real encoded genetic algorithm and response surfacemethodology to optimize production of an indolizidinealkaloid, swainsonine, from Metarhizium anisopliae

Digar Singh & Gurvinder Kaur

Received: 17 July 2012 /Accepted: 20 December 2012 /Published online: 12 January 2013# Institute of Microbiology, Academy of Sciences of the Czech Republic, v.v.i. 2013

Abstract Response surface methodology (RSM) and artifi-cial neural network-real encoded genetic algorithm (ANN-REGA) were employed to develop a process for fermentativeswainsonine production from Metarhizium anisopliae(ARSEF 1724). The effect of finally screened process varia-bles viz. inoculum size, oatmeal extract, glucose, and CaCl2were investigated through central composite design and werefurther utilized for training sets inANNwith training and testRvalues of 0.99 and 0.94, respectively. ANN-REGAwas finallyemployed to simulate the predictive swainsonine productionwith best evolved media composition. ANN-REGA predicteda more precise fermentation model with 103 % (shake flask)increase in alkaloid production compared to 75.62 % (shakeflask) obtained with RSM model upon validation.

AbbreviationsCDW Cell dry weightRSM Response surface methodologyCCD Central composite designPB Plackett–BurmanOFAT One factor at a timeGA Genetic algorithmANN Artificial neural networkANN-REGA Artificial neural network-real

encoded genetic algorithm

Introduction

In the past few decades, polyhydroxylated indolizidine andpyrrolizidine alkaloids such as swainsonine, castanospermine,

australine, and their analogs have engendered considerable in-terest in the field of synthetic and medicinal chemistry. Studieshave proved their potent inhibitory activities against certainclasses of glycosidase enzymes (Elbein et al. 1987).Swainsonine molecule is composed of a fused piperidine andpyrrolidine ring system (Colegate et al. 1979). It is produced bycertain plants, as a secondary metabolite by cultures ofRhizoctonia leguminicola (Schneider et al. 1983), Metarhiziumanisopliae (Patrick et al. 1995), and more recently reported infungal endophytes of locoweed (Braun et al. 2003). Chemicalsynthesis of swainsonine is a low-yielding, expensive, and mul-tistep process due to four chiral centers (Nemr 2000). Microbialbiosynthesis of this alkaloid through fermentativemeans is beinginvestigated as an alternative to the chemical process.

Conventional OFAT approach to optimize a fermentativeyield has certain drawbacks, such as a large number of experi-ments and the uncertainty in results due to lack of interactionsbetween factors (Hao et al. 2006; Leardi 2009). RSM is themost preferred and practiced worldwide for fermentative me-dia optimization. In RSM, statistical modeling is done tomaximize the required response by optimizing different vari-ables. Therefore, RSM is a collection of statistical techniquesfor designing experiments, building models, evaluating theeffect of factors, and searching for the optimum conditions(Tabandeh et al. 2008). It also includes studying the effects ofseveral factors by varying them simultaneously under limitednumber of experiments (Mundra et al. 2007). In the last twodecades, ANN-GA has evolved as a more efficient method forempirical modeling, especially in case of nonlinear biologicalsystems. ANNs can learn from examples and are fault-tolerantin the sense that they are able to handle noisy, incomplete, andnonlinear data, and can perform prediction and generalizationat high speed (Haykin 2008). In the recent years, hybridapproaches namely ANN-GA have been successfully usedfor the optimization of various bioprocess systems (Desai etal. 2008; Zhang et al. 2010; Caldeira et al. 2011).

D. Singh :G. Kaur (*)Department of Biotechnology, Indian Institute of TechnologyGuwahati, Guwahati 781 039 Assam, Indiae-mail: gurvinder@iitg.ernet.in

Folia Microbiol (2013) 58:393–401DOI 10.1007/s12223-012-0220-8

GA is inspired by natural selection and genetic program-ming. Recently, many studies have been performed on GAwith real encoding, which outperforms conventional binarycoded representation (Yoon and Kim 2012). REGA empha-sizing on the coding of the chromosomes with floating pointrepresentations is proven to have significant improvementson the computation speed and precision. At the same time,much effort was imposed to improve computation perfor-mance of GA and to avoid premature convergence of sol-utions. REGA is capable of exploring large input variablespace through the search operators, viz. selection, crossingover, and mutation (Gen and Cheng 2000; Goldberg 1991).

This study is aimed at the optimization of the mediacomponents for enhancing the swainsonine production withquadratic RSM and nonlinear ANN-REGA models.

Materials and methods

Microorganism and cultivation conditions

M. anisopliae (ARSEF 1724) was procured from ARSCollection of Entomopathogenic Fungal Cultures (ARSEF)USDA, Ithaca, NY. The culture was maintained inSabouraud dextrose agar (SDA) slants at 4 °C. Ten-day-old SDA slants were used for the preparation of conidialsuspension at 4×108 spores per mL. Basal oatmeal media(6 %, w/v) supplemented with 2 % (w/v) glucose at 28 °Cand 180 rpm was used for the production of swainsonine.The culture broth was centrifuged at 16,000×g for 10 min at4 °C to separate the cells. The cell-free extract was analyzedfor swainsonine production.

Chemicals

The ingredients required for maintenance and swainsonineproduction media were from Hi-Media, India. Swainsoninestandard from M. anisopliae, α-D-mannosidase from jackbean, and α-L-fucosidase from bovine kidney, their respec-tive substrates p-nitrophenyl-α-D-mannopyranoside, p-nitrophenyl-α-D-fucopyrannoside, and L-glutathione re-duced were all obtained from Sigma-Aldrich.

Swainsonine assay

Swainsonine production in culture supernatants was deter-mined using the α-mannosidase inhibition assay (Sim andPerry 1995).

Saccharide assay

Total saccharide assay was performed using the phenol-sulfuric acid method (Fox and Robyt 1991).

Growth rate

Growth rates were estimated as a function of their CDW.The culture broth was filtered and dried on a weighed filterpaper in an oven, and their CDW was measured as the indexof biomass.

PB screening of factors affecting swainsonine production

Seven factors affecting swainsonine production werescreened using PB design (Plackett and Burman 1946) inthe Design-Expert software DX7. These factors with theirhigh and low range values were selected based upon theone-factor-at-a-time experiment (Singh and Kaur 2012).These were glucose (A), lysine (B), oatmeal (C),MgSo4 7H2O (D), CaCl2 (E), pH (F), and inoculum size(G). The level of significance for each variable was deter-mined using the lack-of-fit-test (F test) with P<0.05. ThePB design with seven variables as well as the levels of eachfactor and the response (swainsonine production) is shownin Table 1. All the experiments were done in triplicates.

Central composite design

The levels of four independent variables, viz. glucose (A),oatmeal (B), CaCl2 (C), and inoculum size (D), were finallyconsidered. The coded and real values for each of thevariables are shown in Table 3. Each factor in the CCDwas studied at five different levels (−2, −1, 0, +1, +2). Allthe factors were taken at their central coded values consid-ered as zero. A 24 full factorial CCD was used to determinethe optimum concentrations of these four variables in 30 runorders (2k+2k+6) in duplicate at the optimum vicinity.

Response surface methodology

The relationships among the variables were determined byfitting the second-order polynomial equation to the experi-mental data:

Y ¼ bo þXk

i¼1

biXiþXk

i¼1

biiX2i þ

X

i

X

j

bijXiXj ð1Þ

where Y = predicted response (swainsonine production),k = number of factor variables, βo = model constant, βi =linear coefficient, βii = quadratic coefficient, βij = interactioncoefficient, and Xj variable in its coded form.

The statistical software package Design-Expert®7.0(StatEase, Inc., Minneapolis, USA) was used to analyzethe experimental design and to evaluate the analysis ofvariance (ANOVA).

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Shake flask evaluation of swainsonine productionon RSM-optimized medium

In order to confirm and validate the RSM optimization,fermentative evaluation in triplicate shake flask (250 mL)was performed. The parameters, swainsonine production,CDW, total saccharide content, and pH were monitored afterevery 24-h interval using the standard protocols.

Modeling and optimization using ANN-REGA

ANN is a predictive model loosely based on the working ofbiological neurons. A feed foreword neural network withfour neurons (glucose, oatmeal, CaCl2, and inoculum size)in the input and six neurons in the hidden and one in theoutput layer (swainsonine production) was created inMATLAB version 7.6.0 (Mathworks Inc. 2008, Natick,USA). Levenberg–Marquardt back propagation algorithm

was employed in network training. Neurons were associatedwith the scalar functions known as weights which determinethe learning process of the network.

Sum ¼X

ni¼1xiwiþ θ ð2Þ

Here, xi (i=1, n) is the connection weight, wi is the inputparameter, and θ=bias. The neurons in the hidden andoutput layer process these input data by multiplying withtheir corresponding weights using a transfer function repre-sented as S-shaped sigmoid curve.

f ðxÞ ¼ 1

1þ exp�xð3Þ

The output generated in the hidden layer and func-tioned as input to the output layer. The learning of thenetwork was carried out by adjusting the weights andcontinuous iteration and minimizing the error betweenthe experimental and ANN-predicted response (Goh

Table 1 PB design matrix in real values along with the observed (mean ± SD) and predicted swainsonine production

Run order Variables with their real values Swainsonine production (μg/mL)

Glucose(%, w/v)

Lysine(%, w/v)

Oatmeal(%, w/v)

MgSo4∙7H2O(mmol/L)

CaCl2(mmol/L)

pH Inoculum size(%, v/v)

Observed Predicted

1 2.5 0.1 10 0.5 0.1 6.5 0.5 0.55±0.11 0.58

2 2.5 1 10 0.1 0.5 6.5 0.5 1.06±0.11 1.08

3 0.5 1 2 0.1 0.1 6.5 2.5 0.79±0.21 0.82

4 0.5 1 10 0.5 0.1 6.5 2.5 0.89±0.08 0.86

5 2.5 1 2 0.5 0.1 4.5 0.5 0.40±0.16 0.39

6 0.5 0.1 2 0.1 0.1 4.5 0.5 0.99±0.19 1.00

7 2.5 0.1 10 0.1 0.1 4.5 2.5 0.54±0.19 0.52

8 2.5 0.1 2 0.1 0.5 6.5 2.5 0.77±0.16 0.75

9 0.5 0.1 10 0.5 0.5 4.5 2.5 1.21±0.14 1.24

10 2.5 1 2 0.5 0.5 4.5 2.5 0.58±0.18 0.59

11 0.5 0.1 2 0.5 0.5 6.5 0.5 1.29±0.24 1.27

12 0.5 1 10 0.1 0.5 4.5 0.5 1.55±0.16 1.53

Table 2 Statistical analysis ofPB design matrix

R = 0.99; R (adj) = 0.98aMost significant at P>F<0.05bLeast significant at P>F >0.05cNon significant at P<F>0.05

Source Sum of squares df Mean square F value P value P>F

Model 1.32 7 0.18 119.90 0.0002 (significant)

Glucose 0.66 1 0.66 418.54 <0.01e−2 a

Lysine 5.33e-004 1 5.33e-004 0.33 0.59c

Oatmeal 0.08 1 0.08 50.54 0.21e−2 a

MgSO4∙7H2O 0.05 1 0.05 32.01 0.48e−2 b

CaCl2 0.44 1 0.44 278.42 <0.01e−2 a

pH 5.33e-004 1 5.33e-004 0.33 0.59c

Inoculum size 0.09 1 0.09 59.13 0.15e−2 a

Residual 6.33e-003 4 1.58e-003

Core total 1.33 11

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1995). Genetic algorithm (GA), on the other hand, is aglobalized optimization technique that optimizes a givenfunction over a particular range and is based on theevolutionary methods of natural selection of the bestindividuals in a population (Imandi et al. 2008).

Operating parameters for REGA

Generation of population Initial population of experi-ments was created using the ANN trained and validatedwith the experimental sets of RSM experiment(Table 3). A population size of 1,000 chromosomes(experimental sets) was finally created and reproducedup to ten generations. Here, REGA was used in place ofthe traditional binary encoded GA (Herrera et al. 1998).Hence, each chromosome is represented by one exper-imental set with four media components (glucose,oatmeal, CaCl2, and inoculum size) at various concen-trations, each of which represented a single gene. Thus,in a population of 1,000 experimental sets, there were atotal of 4,000 genes.

Selection of best fit individuals Best fitted individuals wereselected from the current population using the selectionoperator.

Genetic crossover Simple heuristic crossover at the rateof less than 0.07 was applied to build the newchromosomes.

Mutation Randomly selected uniform genes were mutatedfrom the domain of 4,000 genes in the population. Themutation rate was set low at 0.1 %. The whole processwas continued until a suitable result is achieved that cansatisfy the condition.

Model validation

Model validation was performed in triplicate shake flask(250 mL) at its REGA-optimized levels. The average pro-duction ± SD was considered as the experimental response.

Results

Statistical optimization for swainsonine production

PB design

The swainsonine production from M. anisopliae underunoptimized condition was 1.60 μg per mL (Singh andKaur 2012). Using PB design, four components out ofseven, viz. glucose, oatmeal, CaCl2, and inoculum size,were actually found to be the most significant. The produc-tion range was varied from 0.55 to 1.55 μg per mL; thisfurther confirmed the need for optimization. ANOVA pro-vided the regression equation for the model with significantvariables at (P<0.05):

Y ¼ 0:885� 0:235Aþ 0:081Cþ 0:191E� 0:088G ð4Þ

where A = glucose (in percentage, w/v), C = oatmeal (inpercentage, w/v), E = CaCl2 (in millimolar), and G = inoc-ulum size (in percentage, v/v).

High model F value (119.90) and low P value (Prob > F)(0.0002) indicated that the model is significant (Table 2).The Pareto chart showed the ranking of these variables inaccordance to their absolute values of standardized effects(Fig. 1).

CCD and RSM

Based on the full factorial 24 CCD matrices (Table 3), aquadratic polynomial relation was established betweenswainsonine production and the medium components. Theresulting RSM model equation was as follows:

Y ¼ 1:62� 0:18� Aþ 0:26� Bþ 0:03� Cþ 0:08�Dþ 0:03� A2�0:14� B2 � 0:003� C2 � 0:14�D2 � 0:01� A� Bþ 0:08� A� Cþ0:24� A�D� 0:27� B� C� 0:23� B� Dþ 0:04� C� D

ð5Þ

where A = glucose, B = oatmeal, C = CaCl2, and D =inoculum size.

The results were analyzed using the ANOVA to theexperimental design used (Table 4). A model is consid-ered to be significant if its P value is lower than 0.05.

Fig. 1 Pareto chart of the standardized effects of the factors onswainsonine production from M. anisopliae, (A) glucose, (B)lysine, (C) oatmeal, (D) MgSO4 7H2O, (E) CaCl2, (F) pH, and(G) inoculum size

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The “adequate precision” of the statistical data wasindexed by “signal-to-noise” ratio, which should begreater than 4. Here, this ratio was 63.49 which con-firmed the adequacy of signal. Based on the quadraticEq. (5), the 3-D response surface curves (Fig. 2) wereplotted between the swainsonine production (Z-axis) andany two independent variables, maintaining the rest ofthe variables at their optimum levels. The effect ofglucose and oatmeal on swainsonine production at afixed concentration of CaCl2 (0.3 mmol/L) and inocu-lum size (1.75 %, v/v) is shown in Fig. 2a. It was

observed that the alkaloid production increased signifi-cantly between oatmeal concentration ranges of 5.5–7 %(w/v). However, with increasing glucose concentration,the production attained saturation. This result inferredthe lesser degree of interaction between glucose andoatmeal (P=0.19). The effect of oatmeal and CaCl2together had prominent interaction level (P=0.0001).Swainsonine production enhanced continuously with el-evation in oatmeal and CaCl2 concentrations andachieved a maximum level at 6 % (w/v) and 0.4 mmolper L, respectively (Fig. 2b). The alkaloid production as

Table 3 Full factorial 24 CCD matrix of the four variables in real and coded values along with experimentally observed (mean±SD) regression-predicted and ANN-predicted response values

Glucose (%, w/v) Oatmeal (%, w/v) CaCl2 (mmol/L) Inoculum size (%, v/v) Swainsonine production (μg/mL)

Experimental Regression ANN

Data sets used for ANN model training

1.37 (−1 α) 5 (−1 α) 0.20 (−1 α) 1.37 (−1 α) 1.04±0.07 1.00 1.11

2.12 (+1 α) 5 (−1 α) 0.20 (−1 α) 1.37 (−1 α) 0.02±0.01 0.02 0.01

1.37 (−1 α) 7 (+1 α) 0.20 (−1 α) 1.37 (−1 α) 2.59±0.45 2.59 2.51

2.12 (+1 α) 7 (+1 α) 0.20 (−1 α) 1.37 (−1 α) 1.61±0.19 1.53 1.65

1.37 (−1 α) 5 (−1 α) 0.40 (+1 α) 1.37 (−1 α) 1.35±0.39 1.36 1.40

2.12 (+1 α) 5 (−1 α) 0.40 (+1 α) 1.37 (−1 α) 0.72±0.16 0.73 0.68

1.37 (−1 α) 7 (+1 α) 0.40 (+1 α) 1.37 (−1 α) 1.88±0.28 1.84 1.72

2.12 (+1 α) 7 (+1 α) 0.40 (+1 α) 1.37 (−1 α) 1.08±0.32 1.14 1.04

1.37 (−1 α) 5 (−1 α) 0.20 (−1 α) 2.12 (+1 α) 1.12±0.43 1.05 1.16

2.12 (+1 α) 5 (−1 α) 0.20 (−1 α) 2.12 (+1 α) 1.01±0.25 1.04 0.98

2.12 (+1 α) 7 (+1 α) 0.40 (+1 α) 2.12 (+1 α) 1.38±0.01 1.41 1.32

1.00 (−2 α) 6 (0) 0.30 (0) 1.75 (0) 2.08±0.43 2.11 1.98

2.50 (+2 α) 6 (0) 0.30 (0) 1.75 (0) 1.41±0.42 1.39 1.25

1.75 (0) 4 (−2 α) 0.30 (0) 1.75 (0) 0.52±0.34 0.50 0.53

1.75 (0) 6 (0) 0.30 (0) 1.75 (0) 1.62±0.36 1.62 1.64

1.75 (0) 6 (0) 0.30 (0) 1.75 (0) 1.55±0.06 1.62 1.64

1.75 (0) 6 (0) 0.30 (0) 1.75 (0) 1.59±0.33 1.62 1.64

1.75 (0) 6 (0) 0.30 (0) 1.75 (0) 1.68±0.29 1.62 1.64

1.75 (0) 6 (0) 0.30 (0) 1.75 (0) 1.64±0.28 1.62 1.64

1.75 (0) 6 (0) 0.30 (0) 1.75 (0) 1.69±0.15 1.62 1.64

Data sets used for ANN model testing

1.37 (−1 α) 7 (+1 α) 0.20 (−1 α) 2.12 (+1 α) 1.73±0.15 1.71 1.84

2.12 (+1 α) 7 (+1 α) 0.20 (−1 α) 2.12 (+1 α) 1.65±0.45 1.67 1.71

1.37 (−1 α) 5 (−1 α) 0.40 (+1 α) 2.12 (+1 α) 1.53±0.22 1.60 1.47

2.12 (+1 α) 5 (−1 α) 0.40 (+1 α) 2.12 (+1 α) 1.95±0.04 1.93 1.85

1.37 (−1 α) 7 (+1 α) 0.40 (+1 α) 2.12 (+1 α) 1.18±0.19 1.15 1.21

1.75 (0) 6 (0) 0.10 (−2 α) 1.75 (0) 1.47±0.28 1.54 1.52

1.75 (0) 6 (0) 0.50 (+2 α) 1.75 (0) 1.75±0.29 1.68 1.71

1.75 (0) 6 (0) 0.30 (0) 1.00 (−2 α) 0.86±0.40 0.87 0.92

1.75 (0) 6 (0) 0.30 (0) 2.50 (+2 α) 1.21±0.05 1.20 1.34

1.75 (0) 8 (+2 α) 0.30 (0) 1.75 (0) 1.55±0.26 1.57 1.68

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a function of oatmeal and inoculum size showed a veryprominent interaction (P=0.0001) with a steep rise in itsconcentration. The highest production was predicted atnearly 6–6.5 % (w/v) of oatmeal concentration andinoculum size of 2 % (v/v) as shown in Fig. 2c.Product concentration was also influenced by CaCl2

and inoculum size (Fig. 2d). It was also observed thatthe inoculum size has a significant effect with promi-nent interaction (P=0.005), whereas CaCl2 had a lessereffect. The maximization of the regression Eq. (4) wascarried out using iterative methods to obtain the opti-mum levels of variables (medium components). The

Table 4 ANOVA for quadraticmodel and its coefficients esti-mated by linear multiple linearregression

A, glucose; B, oatmeal; C,CaCl2; D, inoculum size. R =0.99; R (adj) = 0.98

SS sum of squares, df degrees offreedom, MS mean square

Source SS df MS F value Pro. (P)>F Significance

Model 7.04 14 0.50 153.88 <0.0001 Significant

A 0.78 1 0.78 239.11 < 0.01 e−2

B 1.72 1 1.72 526.89 < 0.01 e−2

C 0.03 1 0.03 9.62 0.73 e−2

D 0.15 1 0.15 48.52 < 0.01 e−2

A2 0.02 1 0.02 7.91 1.31 e−2

B2 0.59 1 0.59 180.79 < 0.01 e−2

C2 0.02 e−2 1 0.02 e−2 0.07 7.85 e−1

D2 0.59 1 0.59 180.79 <0.01 e−2

AB 0.06 e−1 1 0.06 e−1 1.84 19.39 e−2

AC 0.12 1 0.12 36.89 <0.01 e−2

AD 0.92 1 0.92 283.57 <0.01 e−2

BC 1.21 1 1.21 372.05 <0.01 e−2

BD 0.85 1 0.85 263.05 <0.01 e−2

CD 0.03 1 0.03 10.78 0.05 e−1

Residual error 0.04 15 0.003

Lack-of-fit 0.03 10 0.003 1.21 43.89 e−2 Nonsignificant

Pure error 0.01 5 0.002

Total 7.09 29

Fig. 2 3-D response surfaceplots for swainsonineproduction a glucose andoatmeal, b oatmeal and CaCl2, coatmeal and inoculum size, andd CaCl2 and inoculum size

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maximum predictable response (swainsonine production)was estimated by substituting these optimum levels ofvariables into the regression Eq. (5). Hence, the opti-mum levels of each variable were determined as fol-lows: 1.38 % (w/v) glucose, 6.96 % (w/v) oatmeal,0.2 mmol per L of CaCl2, and 1.38 % (v/v) of inoculumsize. With optimized levels, the average swainsonineproduction was found to be 2.81±0.17 μg per mL,which was in accordance to the model predicted valueof 2.77 μg per mL. An overall 75.62 % increase in thealkaloid production was achieved after statistical medi-um optimization.

Shake flask evaluation of swainsonine productionas a function of CDW, saccharide concentration, and pH

Swainsonine production in the optimized medium wasfinally evaluated as the function of parameters pH, totalsaccharides, and CDW on swainsonine production(Fig. 3). Production reached a maximum level with2.67±0.41 μg per mL after 72 h of incubation andthereafter decreased. Depletion of saccharide content inthe media was followed by reduction in swainsonineproduction sharply after 96 h. Initial medium pH of5.8 was slightly lowered and then maintained at 5.3 orup to 72 h and reduced to pH4.8 after 144 h followedby rapid decrease in the alkaloid production.

Optimization of media components using ANN-REGA

ANN model was trained and validated based upon theobtained experimental results of CCD. Twenty CCDexperimental data sets were used to adjust the weightsof the model for training and the remaining ten fortesting the network’s performance after every iteration.The learning rate was adjusted to 0.06 through trial anderror. The model was found to be highly significantwith the R value of 0.99 for the training data and0.94 for the test data (Fig. 4a, b). The results showedthat ANN prediction was closer to the experimentallyvalidated values for the CCD experiments as well as theregression prediction (Fig. 4c). Using the trained ANNas a fitness function, the medium optimization wascarried out with REGA. The algorithm was run fivetimes, and the results at the tenth generation for eachrun were reported along with the experimentally vali-dated value for swainsonine production (Table 5). Theaverage of the best runs of the algorithm was consid-ered for experimental validation at shake flask level.The evolution of healthy individuals achieved stagnationby the end of tenth generation and remained constantthereafter (Fig. 5). Swainsonine production predicted byANN-REGA was 3.32 μg per mL with medium compo-sition of 2.90 % (w/v) glucose, 7.89 % (w/v) oatmeal,0.21 mmol/L of calcium chloride, and 1.22 % (v/v) ofinoculum size. Verification of REGA-optimized resultsat shake flask level resulted in a swainsonine concen-tration of 3.25 μg per mL, which is significantly closeto the GA emulated production (Table 5).

Discussion

The previously optimized oat meal-based production medi-um (Singh and Kaur 2012) was further used for maximiza-tion of swainsonine production with statistical and

Fig. 3 Shake flask evaluation of swainsonine production (blacksquare) as a function of CDW (black triangle), saccharide concentra-tion (white triangle), and pH (white square) on RSM-optimized media

Fig. 4 Regression plots for ANN model a training, b test, and c modelpredicted (ANN, white triangle; RSM, black diamond) versus experi-mental swainsonine production from M. anisopliae

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evolutionary algorithm-based methods. PB screeningshowed that glucose and CaCl2 were the most effectiveparameters, followed by oatmeal and inoculum size at theirhigh confidence level of ≥95 %. The subsequent optimiza-tion of the screened variables and their interaction in theprocess was studied through RSM. The 3-D response sur-face plots and ANOVA of CCD further established oatmealand glucose at their optimum levels as sole productionmedia components, which are in agreement with earlierreports (Singh and Kaur 2012). However, CaCl2 was alsosignificantly affecting the production, as was evident fromthe PB screening and its higher degree of interaction withthe rest of the variables, in CCD experiment (P=0.0001).CaCl2 might have an influence over nutrient uptake byaffecting the transport channels across the fungal cell mem-brane. Apart from this, calcium is a structural component ofthe cell wall, cofactor of many enzymes, and has prominentrole in cell growth, cell division, and hyphal growth of manymycorrhiza (Jackson and Heath 1993). Under shake flaskevaluation of RSM-optimized results, alkaloid productionfell rapidly after 72–96 h, followed by decrease in glucoseand pH of the fermentation media. This indicated some“diauxic-like” behavior of the fungal strain (ARSEF1724), which can be hypothesized to consume swainsonine(including other metabolites) with the decrease in saccharide

concentration below a critical level in the media. This issimultaneously accompanied by the reduction in pH of themedia from 5.5 to ~4.5. This reduction in pH can be attrib-uted to the production of some organic acids (Tamerler andKeshavarz 1999). However, indolizidine and pyrrolidineclass of alkaloids are more stable at low pH (Fellows andFleet 1989); hence, the probability of low pH-related deg-radation of the alkaloid is not considerable.

The nonlinear relationship between the process variables(oatmeal, glucose, CaCl2, and inoculum size) with swainso-nine production was further modeled by ANN-REGA. Thehigh regression values for training (0.99) and validation(0.94) of ANN were in good agreement with the experimen-tal RSM data. Thus, a model was established which couldmimic the actual system and was not just a mathematicalfitting of experimental data. The trained network generated(N=1,000) set of experiments or populations, which wereallowed to reproduce five times for ten generations underthe environment of fixed genetic operator. For swainsonineproduction, we had observed that the medium componentswere converged to a single or narrow multiple levels, whichindicated its fit into the neural network trained in the previ-ous generation as reported by Bhatti et al. (2011). ANN-REGA resulted in optimized conditions with nearly 12.63 %higher production compared to RSM model. The predictedoptimal solutions were finally lab-validated under shakeflask conditions with an overall increase of 103 % in alka-loid production compared to the unoptimized model previ-ously reported (Singh and Kaur 2012). The interactionbetween the medium components cannot be exactly de-scribed by GA, but it can be assumed that there is a lackof interaction between these components if they converge toan optimum value (Haider et al. 2008). Hence, further scale-up studies for swainsonine production could provide themore appreciable results at bioreactor levels with controlledagitation, aeration, and pH conditions.

Acknowledgments We thank Indian Institute of TechnologyGuwahati, for providing the experimental facilities, and the Councilof Scientific and Industrial research, PUSA, New Delhi, Governmentof India, for providing the research fellowship.

Table 5 ANN-REGA-opti-mized concentration of mediumcomponents along with theirpredicted and experimentalswainsonine production

ANN-REGA run Glucose(%, w/v)

Oatmeal(%, w/v)

CaCl2(mmol/L)

Inoculum size(%, v/v)

Swainsonine production(μg/mL)

1 2.92 7.91 0.20 1.11 3.06

2 2.82 7.97 0.23 1.31 3.20

3 2.96 7.91 0.20 1.15 3.06

4 2.92 7.95 0.21 1.16 3.15

5 2.89 7.73 0.25 1.40 3.14

Average model predicted 2.90 7.89 0.21 1.22 3.12

Experimental 2.90 7.89 0.21 1.22 3.25±0.39

Fig. 5 Evolution of best (gray bar) and average fitness (black bar)values for swainsonine production (in microgram per milliliter) overten generations in genetic algorithm

400 Folia Microbiol (2013) 58:393–401

Conflict of interest None

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