IJEB 51(4) 322-335.pdf

Indian Journal of Experimental Biology

Vol. 51, April 2013, pp. 322-335

Statistical and evolutionary optimization for enhanced production of an anti-

leukemic enzyme, L-asparaginase, in a protease-deficient Bacillus aryabhattai

ITBHU02 isolated from the soil contaminated with hospital waste

Yogendra Singh & S K Srivastava*

School of Biochemical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi 221005, India

Received 17 August 2012; revised 19 December 2012

Over the past few decades, L-asparaginase has emerged as an excellent anti-neoplastic agent. In present study, a new

strain ITBHU02, isolated from soil site near degrading hospital waste, was investigated for the production of extracellular

L-asparaginase. Further, it was renamed as Bacillus aryabhattai ITBHU02 based on its phenotypical features, biochemical

characteristics, fatty acid methyl ester (FAME) profile and phylogenetic similarity of 16S rDNA sequences. The strain was

found protease-deficient and its optimal growth occurred at 37 °C and pH 7.5. The strain was capable of producing enzyme

L-asparaginase with maximum specific activity of 3.02±0.3 Umg-1 protein, when grown in un-optimized medium

composition and physical parameters. In order to improve the production of L-asparaginase by the isolate, response surface

methodology (RSM) and genetic algorithm (GA) based techniques were implemented. The data achieved through the

statistical design matrix were used for regression analysis and analysis of variance studies. Furthermore, GA was

implemented utilizing polynomial regression equation as a fitness function. Maximum average L-asparaginase productivity

of 6.35 Umg-1 was found at GA optimized concentrations of 4.07, 0.82, 4.91, and 5.2 gL-1 for KH2PO4, MgSO4.7H2O,

L-asparagine, and glucose respectively. The GA optimized yield of the enzyme was 7.8% higher in comparison to the yield

obtained through RSM based optimization.

Keywords: 16S rRNA gene, Bacillus aryabhattai ITBHU02, FAME analysis, L-asparaginase, Protease-deficient

In recent years, L-asparaginase (L-asparagine

amidohydrolase EC 3.5.1.1) has emerged as an

important enzyme in rapidly growing enzyme

industry, owing its potential use in certain kinds of

lymphoblastic malignant therapies, mainly in acute

lymphoblastic leukemia (ALL) and lymphosarcoma1,2

and in food industries to prevent acrylamide

formation in fried food at high temperature3.

L-asparaginase catalyzes the hydrolysis of amide

group of the side chain in L-asparagine to yield

L-aspartate and ammonia. The selective cytotoxicity

for leukemic cells without affecting normal cells,

by treatment of L-asparaginase, occurs due to

effective depletion in L-asparagine level at circulating

plasma pools in the body, resulting in inhibition of

protein synthesis and finally inhibition of DNA

and RNA synthesis, causing apoptotic cell death

of leukemic cells4. Since some leukemic cells,

completely differing from normal cells, are not

capable of synthesizing asparagine synthetase

enzyme, these are totally dependent on the exogenous

supply of L-asparagine. Currently, L-asparaginase

purified from two microbial sources viz. Escherichia

coli and Erwinia carotovora is extensively used in

clinical treatment of leukemia, but their prolonged

administration induces immunogenic side effects

like allergic reactions, anaphylaxis, pancreatitis and

neurological seizures and anti-asparaginase antibodies

so formed, inactivate the enzyme2,5,6

. To overcome the

toxicity associated with the clinical preparations of

asparaginases from current sources, a new serologically

different enzyme, having same therapeutic effect, is

required. To obtain a better and alternative source of

L-asparaginase, there is an ongoing interest to screen

new organisms from different biodiversities.

The most important steps in microbial based

metabolite production systems are modeling and

optimization to maximize the efficacy of the system7.

There is a broad range of modeling and optimization

methodologies, which vary from simple one factor

at a time (OFAT) method8,9

to complex statistical

———————— *Correspondent author

Telephone: +91 542 6702886;

Fax: +91 542 2368428

E-mail: [email protected] (S. K. Srivastava);

[email protected]

SINGH & SRIVASTAVA: L-ASPARAGINASE PRODUCTION FROM BACILLUS ARYABHATTAI ITBHU02

323

methods such as Plackett-Burman design technique

(PBD), central composite design (CCD) and Box-

Behnken design (BBD)10,11

. Implementation of

response surface methodology (RSM) to a biological

process does not only save the labour and time of

study but also establish a relation between the

component factors12,13

. RSM is a set of statistical

techniques for designing experiments, constructing

empirical models, evaluating the impacts of component

factors and searching for the optimum conditions.

Evolutionary computing based methods, such as

genetic algorithm (GA), have been well implemented

for controlling and optimizing the bioprocesses

for past decade11,14,15

. GA based optimization

processes necessitate a fitness function, which was

polynomial regression equation generated by CCD in

the present study.

Soil beneath the degrading hospital waste site

may be a good source of a variety of bacteria.

In the present study, an efficient protease-deficient

L-asparaginase producing strain was screened

successfully, among different soil isolates. The strain

was further identified as Bacillus aryabhatti based on

its physiological, biochemical characteristics, fatty

acid methyl ester (FAME) profile as well as 16S

rDNA sequence homology analysis. Additionally, the

yield of L-asparaginase production was investigated

under different media composition and fermentation

conditions (i.e. nutrients, temperature, pH, etc.) by

means of one factor at a time (OFAT) method. The

productivity of the enzyme was optimized employing

response surface methodology (RSM) and genetic

algorithm (GA) based modeling technique. The

fitness and prediction accuracy of the model was

evaluated heuristically.

Materials and Methods

Isolation and screening of L-asparaginase producing

organisms—Soil samples collected from two different

spots, located near degrading hospital wastes at Sir

Sundar Lal Hospital, B.H.U., Varanasi, India, were

suspended in sterile distilled water to make a 10% soil

suspension and serially diluted up to 10-8

dilutions.

0.2 mL from different dilutions was uniformly

spreaded on nutrient agar plates and incubated at

30 °C for 24-48 h. From the plates, 15 morphologically

different colonies were chosen and further purified by

repetitive streaking method. The L-asparaginase

producing strain was screened by rapid plate assay

method, based on its capability to form a pink zone

around colonies on agar plates of modified M-9

medium incorporated with a pH indicator16

. Among

5 colonies producing L-asparaginase, one (designated

as ITBHU02) showing maximum ratio of pink zone

diameter to colony diameter was selected for

subsequent experiments. The promising isolate was

maintained in nutrient agar (NA) slant. The slant was

incubated at 30 °C for 24 h and stored at 4 ±1 °C.

Stock culture was transferred to fresh NA medium

every 3-4 weeks.

Characterization of strain ITBHU02—Taxonomic

characterization of ITBHU02 was done based

on cultural, morphological, biochemical characteristics,

FAME analysis and 16S rRNA gene sequencing.

Phenotypic characterization of the isolate was done

by Gram staining, oxidase, motility, fermentation,

nitrate/nitrite reduction and other biochemical profile

tests. FAME analysis was performed by Royal

Life Sciences Pvt. Ltd., Secunderabad, India;

a MIDI Sherlock, USA based Laboratory. Further,

amplification of 16S rRNA gene of ITBHU02

strain was done by PCR using two universal

eubacterial oligonucleotide primers, 16SF

5′- AGAGTTTGATCCTGGCTCAG -3′ and 16SR

5′- AAGGAGGTGATCCAGCC -3′17

. Purified PCR

product was sequenced by using an automated

sequencer (3730XL DNA Analyzer, Applied

Biosystem, HITACHI, USA). The homology search

of resulting 16S rRNA gene of the strain was done by

BLASTN method using EzTaxon online server

version 2.118

and finally deposited in NCBI GenBank

database (Accession #: JQ673559). Preliminary

multiple sequence alignments and a phylogenetic tree

construction were performed using Clustal W and

MEGA software version 4.119

.

Production of L-asparaginase—The production

studies of L-asparaginase were performed in a basal

modified M9 medium (BMM) containing : 3.0 gL-1

glucose, 6.0 gL-1

Na2HPO4.2H2O, 3.0 gL-1

KH2PO4,

0.5 gL-1

MgSO4.7H2O, 0.5 gL-1

NaCl, 0.015 gL-1

CaCl2.2H2O and 3.0 gL-1

L-asparagine at pH 7.016

.

The seed culture was prepared by addition of a loop

of cells from the fully grown slants into 50 mL of

above sterile medium in 250 mL Erlenmeyer flasks

and incubated at 30 °C in a rotary shaking incubator

(160 rpm). The production medium was then

inoculated with inoculum (2% v/v) from 24 h

grown seed culture and allowed to grow at 30 °C

with shaking at 160 rpm. All the experiments were

conducted in triplicate and average value of enzyme

INDIAN J EXP BIOL, APRIL 2013

324

production was utilized for the compatibility analysis.

Dry cell weight for each experiment was also quantified.

Optimization of L-asparaginase production medium

Optimization using one- factor-at-a-time method—

As a primary step in the optimization of L-asparaginase

production, the component factors were tested as a

single variable (one factor at a time method; OFAT).

The effect of incubation time (0-96 h), different

incubation temperature (20, 25, 30, 37, 42, 50

and 60 °C) and pH (5.0-10.0) of the medium was

investigated. The production of L-asparaginase after

substituting glucose (0.3% w/v) with fructose,

galactose, starch, sucrose, maltose, malt extract,

lactose, tri-sodium citrate, sodium pyruvate

independently at concentration 0.3% (w/v), as well as,

addition of various organic nitrogen sources (yeast

extract, peptone, tryptone, beef extract, tryptose,

casein, gelatin and urea; 0.2% w/v) and inorganic

nitrogen sources (NH4Cl, (NH4)2SO4, [NH4NO3,

NH4COOCH3, tri-ammonium citrate and ammonium

oxalate; level maintained at 0.032 g/100 mL] to basal

medium was studied. Effect of supplementation of

L-aspartate, L-glutamine, L-glutamate and histidine

(0.3% w/v) after substituting L-asparagine from

BMM was also evaluated.

Optimization through statistical design and analysis

Screening of significant variables by using

Plackett-Burman design—The Plackett-Burman

design (PBD) was applied to screen the significant

medium components with respect to their main

consequences on enzyme production. This design

(PBD) is a fraction of two-level factorial design, in

which each factor is investigated at two widely

spaced levels, a high (+1) and a low (-1) level20

.

A total of eight variables considered for the

experimental design were beef extract, Na2HPO4.2H2O,

KH2PO4, MgSO4.7H2O, NaCl, CaCl2.2H2O, glucose

and L-asparagine. The responses from 12 individual

experiments were utilized for generating regression

coefficient values. The details of experimental design

for the screening of variables are shown in Table 2.

The Plackett-Burman design is based on the first-

order polynomial model:

… (1)

where, Y denotes the response (L-asparaginase

activity), β0 is model intercept, βi is the factor

estimates, and Xi is the level of the independent

variable. From regression analysis, the variables

showing P-values below 5% level (P<0.05) were

considered to have greater impact on L-asparaginase

production and used further for central composite

design (CCD).

Response surface methodology (RSM)—Response

surface methodology, an empirical combination of

mathematical and statistical techniques, is a quite

powerful tool for modeling, improving and optimizing

the processes. The significant medium components

screened through Plackett-Burman design technique

were subjected to central composite design (CCD), a

popular second-order experimental design for

developing sequential experimentation and predicting

the level of factors, to get an optimal response

through regression analysis21

. The effect of four

independent variables, viz., KH2PO4, MgSO4.7H2O,

glucose and L-asparagine on the production of

L-asparaginase was studied at five different levels

(-α, -1, 0, +1, and +α), where α = 2n/4

, here n denotes

the number of variables used for the study. A

full factorial central composite design was performed

to build a total of 30 experiments, having 24= 16

cube points plus 6 centre points (4 in cube and 2 in

axial positions) and (4 × 2 = 8) star points. The

experimental design and statistical analysis of the data

were done by using statistical software Minitab

version 15.1.0.0, USA. The second-degree

polynomial equation was used to calculate the

relationship between the independent variables and

the response. Considering all the linear terms, square

terms and by linear interaction terms, the quadratic

regression model can be illustrated as:

… (2)

where, β0 is the constant, n denotes the number of

variables, βi the slope or linear effect of the input

variable Xi and βii the quadratic effect of input

factor Xi and βij is the linear by linear interaction

effect between the input variable Xi and Xj. The

contour plots were obtained for determining the

optimum levels of factor variables for maximum

L-asparaginase production.

Optimization through genetic algorithm—Genetic

algorithms (GA) follow the theory of “survival of the

fittest” or “natural evolution” proposed by Sir Charles

Darwin to solve search and optimization processes22

.

GA has been successfully applied to resolve non-


325

linear, non-differentiable functions efficiently such as

regression equations formulated in biological systems

on optimizing media components15,23,24

. To facilitate

a solution for an optimization problem (regression

equation), GA creates an initial population of randomly

generated individual chromosomes, generally represented

as strings of binary digits. During successive iterations

(generations), the evolved chromosomes acquire

better fitness value by reproduction among individuals

of the previous generation. In order to create new

generations three genetic operators are applied: selection,

crossover and mutation. The descendants evolved at

each generation, were subjected to evaluation for their

fitness value using the fitness function (regression

equation). At each step, the genetic algorithm

selects the individuals at random, from the current

population, to be parents and uses them to produce the

offspring for the next generation. Crossover operator

combines two parents to form children for upcoming

generations. However, mutation rules are concerned

with introducing new diversities among individual

parents producing children. Point mutations are the

most commonly occurring mutations, which are used

to avoid any convergence to local maxima. This

iterative process continues until a satisfactory solution

according to the need of fitness function was

achieved. The MATLAB (Version 7.0, Mathworks,

Inc., MA, USA) was used to perform genetic

algorithm based modeling studies.

Analytical methods

Assay of L-asparaginase activity—L-asparaginase

activities were assayed at 37 °C with using L-aspartic

acid β-hydroxamate (AHA) as the substrate25

. Reaction

mixture containing 0.3 mL AHA solution (0.01 M

solution in 0.05 M HEPES buffer, pH 7.0) and 0.1 mL

cell-free broth was incubated for exactly 30 min at

37 °C. The reaction was stopped by addition of

2.4 mL stopping reagent (1 M Na2CO3 solution:

1% (w/v) 8-hydroxyquinoline in ethanol: 1% (w/v)

NaIO4 solution; 8:1:0.2). The green colour, developed

after keeping the mixture in boiling water bath for

1-2 min, was measured at 705 nm. One unit (U)

of asparaginase activity is defined as the amount

of enzyme that liberates 1.0 µmol of NH2OH from

AHA per min at 37 °C. The specific activity of

L-asparaginase was expressed as the activity of enzyme

in terms of units per milligram of protein (Umg-1

).

Cell counting and growth of culture—Viable cell

counting was done by spread plate method using a

suitable dilution of culture. Each colony that can be

counted is called as colony forming unit (CFU).

Culture growth was monitored by measuring the

optical density of suspension culture at 600 nm. Dry

cell weight measurement was done by centrifuging

the culture sample at 8,000 rpm for 10 min, and the

supernatant was used for product analysis. The pellet

of cells was washed twice with distilled water and

kept in an oven at 80 °C until it dried. The mass of

dried cells was taken as dry cell mass.

Quantification of protein content—The total

protein content in the supernatant was estimated using

bovine serum albumin as a standard26

.

Determination of protease activity—Plates containing

skimmed milk agar (SM) were used for the qualitative

analysis of proteolytic activity present among isolated

strains, whereas quantification of proteases in cell-

free broth was measured as described by Tang et al.27

using 0.05 M Tris-HCl buffer containing 2.0% casein

(w/w) at pH 7.5, 37 °C for 20 min.

Results Characterization of L-asparaginase producing

strain—The potential L-asparaginase producing strain

ITBHU02, screened through rapid plate assay (Fig.1)

was seen to be rod–shaped, Gram staining positive,

motile and spore bearing bacteria. Colonies were

circular, creamy white coloured, translucent, convex

on NA medium at 30 °C for 24 h. SEM image

depicted the physical cellular size of range

1.0-3.0 µM (Fig.2). One unit in terms of O.D. at 600

nm of bacterial suspension was corresponding to

approximately 4×108 CFU mL

-1 or 0.917 mg dry

cell weight mL-1

. Biochemical test studies of the

bacterium showed that it was an oxidase negative;

catalase positive; starch hydrolyzing; glucose,

maltose, sucrose fermenting; haemolysing; non-

mannitol fermenting and nitrate reducing strain.

Fig. 1—L-asparaginase activity plate assay, (a) Control plate and

(b) plate having pink zone showing degradation of L-asparagine

present in media due to activity of L-asparaginase of strain ITBHU02


326

The fatty acid methyl ester (FAME) analysis of

ITBHU02 was conducted using MIDI Sherlock®

Microbial Identification System software. The major

cellular fatty acid contents of the isolate were anteiso-

C15:0 (45.03%), iso-C15:0 (23.69%), iso-C14:0 (9.82%),

C16:0 (5.38%), C16:1ω11C (3.04%), C14:0 (2.80%),

anteiso-C17:0 (2.15%), iso-C16:0 (2.06%). The

RTSBA6.0 database matches from Sherlock®

software showed the Similarity Index (SI) value of

0.519 with Bacillus megaterium-GC subgroup

A. Similarity Index (SI) value suggests the extent of

closeness of the cellular fatty acid composition of an

unknown sample in comparison to the mean cellular

fatty acid composition of the strains used to construct

the library entry listed as its match. SI value,

1.000 shows an exact match of cellular fatty acid

make-up of the unknown sample to the mean of

library entry results28

.

Based on blast analysis of 16S rRNA gene (1521

nucleotides), the isolate showed 99.93% similarity

with Bacillus aryabhattai B8W22 (accession #:

EF114313) rather than 99.52% similarity with

Bacillus megaterium strain IAM 16418 (#: D16273).

So, the strain was considered as more closure relative

to Bacillus aryabhattai. The phylogenetic dendrogram

(Fig. 3) constructed by the neighbor-joining method

indicated that the isolate ITBHU02 was a discrete

strain in the Bacillus aryabhattai cluster. Bootstrap

values, which derived from 500 replicates, were

represented in form of a numerical value at branch

point in phylogenetic dendrogram whereas 0.005

Jukes-cantor distance (i.e. 0.005 nucleotide substitutions

per position) in form of scale bar at the base.

Optimization using one-factor-at-a-time (OFAT) method

Time course study and effect of temperature and

pH on L-asparaginase production—Time course

study of the enzyme production from the strain

ITBHU02 depicted that L-asparaginase production

was started at 7-8 h and maximum level of 3.02 ± 0.1

U mg-1

was found at 24-25 h. However, maximum

bacterial biomass (dry cell weight) was 2.254 ± 0.018

gL-1

was observed at 28 h (Fig. 4a). Effects of

temperature and pH on enzyme productivity were

shown in the Fig. 4b; c. Incubation at 37 °C was

found optimum with maximum specific activity

2.58±0.16 U mg-1

whereas the optimum pH was found

slightly alkaline at 7.5 with maximum 2.64±0.10 U

mg-1

activity. Highest growth of the bacteria was

shown at 37 °C and pH 7.5. Isolate could not grow

beyond the 45 °C and pH higher than 9.0.

Effect of carbon and nitrogen sources on

L-asparaginase production—Carbon and nitrogen

sources have strong impact on biomass production

and L-asparaginase yield of B. aryabhattai ITBHU02,

(Table 1). Growth and enzyme production profiles

were varying in each case of carbon and nitrogen

Fig. 2—SEM image of the strain ITBHU02 showing morphological

characteristics

Fig. 3—Phylogenetic dendrogram constructed from sequence

alignment of 16S rRNA genes for Bacillus aryabhattai strain

ITBHU02 and different related strains (GenBank sequence

accession numbers given in parentheses)


327

sources. Among carbon sources, glucose was found to

enhance the enzyme production to a maximum

specific activity of 3.44±0.02 U mg-1

. Galactose was

found to be in proximity with the glucose with

considerable amount of specific activity 2.96±0.02 U

mg-1

. Beef extract as a nitrogen source, among used

different organic and inorganic sources, was greatly

preferred by the strain for production of L-asparaginase

enzyme (max. sp. activity 3.62±0.24U mg-1

).

Effect of supplementation of inducer—Production

of most of the industrial enzymes from different

microbial sources is found to be induced with

the addition of several related compounds which

act as inducer. Therefore, the effect of inducers on

L-asparaginase production was examined using

L-asparagine, L-aspartate, L-glutamine, L-glutamate and

histidine. Table 1 represents that L-asparagine, when

supplemented as inducer, has given the maximum

productivity of 3.23±0.24U mg-1

, which is quite

higher as compared to rest of the compounds used.

Optimization of process parameters through mathematical

design

Identification of significant factors using Plackett-

Burman design—Table 2 shows 12 sets of

experiments designed by PBD technique to study the

main impact of eight factors on the L-asparaginase

production. Coded level of each real value is given in

parentheses. Corresponding observed and predicted

responses in terms of enzyme specific activity (U mg-1

)

are shown as well. Cell biomass was observed in

unison with the level of L-asparaginase production,

which confirms the growth-associated production of

the enzyme. On the basis of analysis of variance

(ANOVA) and values of coefficient for significance

(P<0.05), four factors out of the eight, viz. KH2PO4,

MgSO4.7H2O, glucose and L-asparagine were found

to have significant effect. The following regression

equation was obtained:

Y = 2.625 – 0.135X3 + 0.339X4 – 0.41x7 – 0.137X8 …(3)

where Y represents the predicted response variable,

L-asparaginase activity (U mg-1

) and X3, X4, X7 and X8

are the values of KH2PO4, MgSO4.7H2O, glucose and

L-asparagine respectively.

The statistical analysis consisting main effects,

value of coefficients, standard error of coefficients, t

and P values of the experimental design, generated

by software has been shown in the Table 3. The

main effect of each factor can be concluded as the

difference between both the averages of measurements

made at higher (+1) and lower (-1) levels of the

corresponding factor. It is simple to evaluate the

significance of each variable based on their respective

values of absolute effect. Positive value of main effect

for a factor denotes that higher yield of the enzyme

would result at the factor’s higher-level concentration

than its lower-level; whereas a factor with negative

sign denotes that its lower-level concentration

would provoke the higher yield of the enzyme13

. A

coefficient close to zero value means that a factor has

little or no impact on the yield. The t-values were

calculated by dividing each coefficient by its standard

error. The goodness of the fit of the regression model

was represented by coefficient of determination (R2).

For a good statistic model, R2

should be closure to

one. In the presented model, R2 was 98.83%, which

indicated that up to 98.83% variability in dependent

Fig. 4—Effect of culture conditions on the production of

L-asparaginase ( ) and cell mass growth ( ) (a) Profile of

incubation time (b) profile of incubation temperature and

(c) profile of medium pH


328

Table 1—Effect of carbon, nitrogen sources and inducer compounds on L-asparaginase production by Bacillus aryabhattai strain

ITBHU02 at pH 7.5 and 37°C during 24 h incubation

C-sources

(0.3% w/v)

Enzyme

(Umg-1 protein)

Dry cell weight

(gL-1)

N-sources Enzyme

(Umg-1 protein)

Dry cell weight

(gL-1)

Organic source

Control* 1.24±N.D. 1.462±0.021 Control** 2.11±0.21 2.054±0.025

Fructose 2.12±0.02 1.986±0.007 Peptone 2.14±0.10 2.281±0.041

Glucose 3.44±0.20 2.211±0.018 Beef extract 3.62±0.24 2.118±0.026

Galactose 2.97±0.11 1.893±0.034 Yeast extract 3.04±0.14 1.992±0.017

Starch 1.94±0.02 1.761±0.024 Tryptone 2.36±0.12 2.210±0.052

Sucrose 1.61±0.12 1.435±0.012 Gelatin 2.22±0.04 1.645±0.023

Maltose 2.44±0.10 1.457±0.010 Casein 2.07±0.30 1.867±0.018

Malt extract 1.44±N.D. 1.361±0.041 Urea 2.12±0.11 1.524±0.034

Lactose 1.79±0.05 1.622±0.023

Tri-sodium citrate 1.37±0.02 1.086±0.008 Inorganic source

Sodium pyruvate 1.44±0.02 1.176±0.027 Ammonium sulphate 2.11±N.D. 1.431±0.010

Ammonium chloride 1.97±0.03 1.084±0.034

Inducer compounds Ammo. nitrate 2.45±0.12 1.894±0.020

(0.3% w/v) Ammo. acetate 2.23±0.20 1.221±0.006

Control*** 1.11±0.15 1.687±0.022 Ammo. oxalate 1.96±0.04 1.243±0.051

L-asparagine 3.23±0.20 2.118±0.048 Tri-ammo. citrate 1.68±0.02 2.012±0.014

L-asparate 1.26±0.11 1.886±0.012 Potassium nitrate 1.87±0.10 1.043±0.041

L-glutamine 2.47±0.10 1.732±0.008

L-glutamate 1.64±0.30 2.078±0.014

Histidine 1.17±0.05 1.776±0.021

N.D. = not detected,

*, **, ***= controls; basal modified medium (BMM) containing no C-source, N-sources or inducers respectively

Table 2—PBD matrix in real and coded values (in parenthesis) of independent variables and the predicted and experimentally

achieved L-asparaginase yield

Media concentration (gL-1) Enzyme specific activity

(Umg-1)

Dry cell

weight (gL-1)

Trials Beef

extract

(X1)

Na2HPO4.

2H2O

(X2)

KH2

PO4

(X3)

MgSO4.

7H2O

(X4)

NaCl

(X5)

CaCl2.

2H2O

(X6)

Glucose

(X7)

L-asparagine

(X8) Observed Predicted

1 4.0 (+1) 1.0 (-1) 5.0 (+1) 0.1(-1) 0.1 (-1) 0.01 (-1) 5.0 (+1) 5.0 (+1) 2.14 ± 0.023 2.155 1.881 ± 0.018

2 4.0 (+1) 10.0 (+1) 1.0 (-1) 1.0 (+1) 1.0 (+1) 0.01 (-1) 1.0 (-1) 5.0 (+1) 1.89 ± 0.013 1.794 1.486 ± 0.036

3 1.0 (-1) 1.0 (-1) 5.0 (+1) 1.0 (+1) 1.0 (+1) 0.01 (-1) 5.0 (+1) 5.0 (+1) 1.58 ± 0.034 1.564 1.447 ± 0.044

4 4.0 (+1) 1.0 (-1) 1.0 (-1) 0.1 (-1) 1.0 (+1) 0.10 (+1) 1.0 (-1) 5.0 (+1) 2.63 ± 0.015 2.670 1.761 ± 0.028

5 1.0 (-1) 10.0 (+1) 5.0 (+1) 1.0 (+1) 0.1 (-1) 0.10 (+1) 1.0 (-1) 5.0 (+1) 2.18 ± 0.021 2.250 1.531 ± 0.032

6 4.0 (+1) 1.0 (-1) 5.0 (+1) 1.0 (+1) 0.1 (-1) 0.10 (+1) 1.0 (-1) 1.0 (-1) 3.45 ± 0.032 3.354 2.245 ± 0.013

7 1.0 (-1) 10.0 (+1) 1.0 (-1) 0.1 (-1) 0.1 (-1) 0.10 (+1) 5.0 (+1) 5.0 (+1) 3.28 ± 0.017 3.264 1.908 ± 0.007

8 1.0 (-1) 1.0 (-1) 1.0 (-1) 0.1 (-1) 0.1 (-1) 0.01 (-1) 1.0 (-1) 1.0 (-1) 2.68 ± 0.020 2.720 1.512 ± 0.043

9 4.0 (+1) 10.0 (+1) 1.0 (-1) 1.0 (+1) 0.1 (-1) 0.01 (-1) 5.0 (+1) 1.0 (-1) 3.13 ± 0.041 3.059 2.117 ± 0.016

10 1.0 (-1) 10.0 (+1) 5.0 (+1) 0.1 (-1) 1.0 (+1) 0.01 (-1) 1.0 (-1) 1.0 (-1) 2.76 ± 0.011 2.719 1.687 ± 0.010

11 4.0 (+1) 10.0 (+1) 5.0 (+1) 0.1 (-1) 1.0 (+1) 0.10 (+1) 5.0 (+1) 1.0 (-1) 2.38 ± 0.015 2.475 1.735 ± 0.005

12 1.0 (-1) 1.0 (-1) 1.0 (-1) 1.0 (+1) 1.0 (+1) 0.10 (+1) 5.0 (+1) 1.0 (-1) 3.41 ± 0.034 3.480 2.135 ± 0.026


329

variable (L-asparaginase specific activity) could be

calculated. L-asparaginase production, obtained from

PBD, showed a broad variation ranging between

1.58 – 3.45 U mg-1

of protein, revealed the necessity

of further optimization of media components. The

other entire insignificant variables (beef extract,

Na2HPO4.2H2O, NaCl and CaCl2.2H2O) were not

considered for further optimization experiments, but

instead were included at their average level (center

value) for each trials as well as next experimentation.

Optimization of concentration levels of the

screened components—The experimental trials were

performed based on the CCD (Table 4) in order to get

the optimal concentration level of entire significant

parameters for maximum L-asparaginase production.

A second-order polynomial equation relating

L-asparaginase productivity (Y) with the independent

factors, viz. KH2PO4 (X3), MgSO4.7H2O (X4),

L-asparagine (X7) and glucose (X8) is shown below:

Y = –10.645 + 3.403X3 +7.138X4 + 1.521X7 + 1.835X8 – 230.502X – 2

46.115X – 240.234X – 2

80.31X + 0.14X3X4 +

0.004X3X7 + 0.076X3X8 + 0.332 X 4X 7 + 0.349X4X8 +

0.037X7X8 … (4)

In order to analyze the results, the ANOVA as appropriate to the design matrix was employed

(Table 5). The R2 value (multiple correlation

coefficient) for the regression model was 0.9749 which implies that sample variation of 97.49% of the

enzyme production was congruous with media components and only 2.51% of the variations

were not explained by the model. The Fisher’s F test (Fmodel = 38.15) and a very low probability value

(Pmodel < 0.0001) indicated that the model was highly

significant. The P value for “lack of fit test” (0.486)

indicates the quadratic model adequately fits the data. The P value of the coefficients for all linear as well as

quadratic relationship suggests they have high significance in the production of L-asparaginase

enzyme (P < 0.0001), while the interactions between

KH2PO4, MgSO4.7H2O, glucose and L-asparaginase concentration were found to be less significant as

their higher P values for interactive terms (Table 6). The root mean squared error (RMSE) and standard

error of prediction (SEP) between the experimental

and RSM predicted results were evaluated to be 0.3 and 4.11% respectively. The maximum error of

prediction was 9.98%. This proved that RSM model for L-asparaginase production had ample accuracy

and was related. The two-dimensional contour plots were constructed

to achieve the main goal of optimization of fermentation

parameters for maximum L-asparaginase production by B. aryabhattai ITBHU02 (Fig. 5a-f). Each contour

plot illustrates the effects of two parameters on the targeted response, keeping other two parameters

constant at their middle value. The predictive yield of

the enzyme for a particular set of fermentative parameters was numerically represented inside the

plot. Further “crosshairs” tool of MINITAB 15 software can be utilized to explore the predictive

response at any particular point. From the analysis of contour plots (Fig. 5a-f), the level of L-asparaginase

production increases to a maximum value with the

increasing level of KH2PO4 to 3.7-3.8 gL-1

, MgSO4.7H2O to 0.82-0.84 gL

-1, glucose to 4.03-4.05

gL-1

and L-asparagine to 3.98-4.05 gL-1

. Further increment of the independent variables beyond the

aforesaid level shows inhibitory effects on the enzyme

production. However, the interaction between any two of the parameters is not prominent. The optimum

combination of parameters for the maximum production of L-asparaginase found as follows:

KH2PO4 3.773 gL-1, MgSO4.7H2O 0.852 gL

-1,

L-asparagine 4.136 gL-1

and glucose 4.136 gL-1whereas,

optimum response was found to be 5.98 U mg-1 protein.

Genetic algorithm based optimization—To facilitate an optimum solution, genetic algorithm has been employed on the same data sets used for the RSM technique. The polynomial regression equation (4), generated by central composite design analysis, was executed as a fitness function by GA for maximum production of L-asparaginase enzyme. All the four parameters of the model have been represented in terms of chromosomes for GA based optimization technique with the followings constraints:

Table 3—Statistical analysis through PBD showing coefficients

and effects for each factor on L-asparaginase yield

Variables Effect Coefficient t-value P-value

Constant 2.625 71.64 0.000

(X1): Beef extract 0.2083 0.104 2.84 0.066**

(X2)Na2HPO4.2H2O 0.1483 0.074 2.02 0.136**

(X3): KH2PO4 -0.2717 -0.135 -3.71 0.034*

(X4): MgSO4.7H2O 0.6783 0.339 9.25 0.003*

(X5): NaCl 0.1217 0.060 1.66 0.196**

(X6): CaCl2.2H2O 0.0183 0.009 0.25 0.819**

(X7): Glucose -0.2750 -0.137 -3.75 0.033*

(X8): L-asparagine -0.8183 -0.409 -11.16 0.002*

R2=98.83%, Adj R2 = 95.70%, Pred. R2= 81.26% * Significant at 95% confidence level (P<0.05) ** Insignificant at 95% confidence level (P>0.05)


330

0.5 (gL-1

) ≤ KH2PO4 ≤ 6.5 (gL-1

)

0.1 (gL-1

) ≤ MgSO4.7H2O ≤ 1.3 (gL-1

)

0.5 (gL-1

) ≤ L-asparagine ≤ 6.5 (gL-1

)

0.5 (gL-1

) ≤ glucose ≤ 6.5 (gL-1

)

The genetic algorithm parameters in the MATLAB

software for the optimization of L-asparaginase activities

in the culture were set as following: population

type: double vector; original population size: 100;

cross over probability: 0.8; elite count: 20; crossover

function: @crossoversinglepoint; migration direction:

forward; selection function: @selectionroulette; mutation

function: @mutationgaussian; total generations: 100.

Since genetic algorithm based optimization procedure

frequently does not declare the global optimum

solution, the process of optimization was repeated

several times by varying the different input space

parameters15,23

. These reiterations at different GA

input conditions ascertained that the whole searching

space was explored thoroughly to achieve a global

optimum solution. Accomplishment of alike optimal

solutions for most of the input conditions confirmed

that it is a global optimal solution. The best fitness

plot (Fig. 6) achieved during the iterations of GA over

generations describes the gradual convergence of

results towards the optimal solution. The validation of

Table 4—CCD matrix for four significant variables in real and coded values (in parenthesis) and the predicted and

experimentally achieved L-asparaginase yield

Media concentration(gL-1) Specific activity

(Umg-1) Trials KH2PO4

(X3)

MgSO4.7H2O

(X4)

Glucose

(X7)

L-asparagine

(X8) Observed Predicted

Dry cell weight

(gL-1)

1 6.5 (+α) 0.7 (0) 3.5 (0) 3.5 (0) 1.89 ± 0.033 1.746 1.412 ± 0.014

2 3.5 (0) 0.7 (0) 3.5 (0) 0.5 (-α) 2.73 ± 0.014 2.749 1.511 ± 0.004

3 3.5 (0) 1.3 (+α) 3.5 (0) 3.5 (0) 4.83 ± 0.041 4.228 2.141 ± 0.010

4 3.5 (0) 0.7 (0) 6.5 (+α) 3.5 (0) 3.87 ± 0.024 3.698 1.648 ± 0.021

5 3.5 (0) 0.7 (0) 3.5 (0) 6.5 (+α) 4.68 ± 0.031 4.165 2.031 ± 0.041

6 0.5 (-α) 0.7 (0) 3.5 (0) 3.5 (0) 0.68 ± 0.047 0.328 0.891 ± 0.007

7 3.5 (0) 0.1 (-α) 3.5 (0) 3.5 (0) 2.38 ± 0.011 2.486 1.517 ± 0.023

8 3.5 (0) 0.7 (0) 0.5 (-α) 3.5 (0) 2.17 ± 0.022 1.846 1.445 ± 0.005

9 3.5 (0) 0.7 (0) 3.5 (0) 3.5 (0) 5.86 ± 0.062 5.558 2.114 ± 0.016

10 3.5 (0) 0.7 (0) 3.5 (0) 3.5 (0) 5.37 ± 0.041 5.558 2.004 ± 0.044

11 5.0 (+1) 1.0 (+1) 2.0 (-1) 5.0 (+1) 2.51 ± 0.047 3.128 1.531 ± 0.002

12 5.0 (+1) 0.4 (-1) 2.0 (-1) 2.0 (-1) 1.83 ± 0.023 1.924 1.451 ± 0.012

13 5.0 (+1) 0.4 (-1) 5.0 (+1) 5.0 (+1) 2.77 ± 0.070 3.268 1.629 ± 0.046

14 3.5 (0) 0.7 (0) 3.5 (0) 3.5 (0) 5.67 ± 0.051 5.558 2.156 ± 0.030

15 3.5 (0) 0.7 (0) 3.5 (0) 3.5 (0) 5.71 ± 0.038 5.558 2.123 ± 0.014

16 2.0 (-1) 0.4 (-1) 5.0 (+1) 5.0 (+1) 2.34 ± 0.061 2.362 1.622 ± 0.028

17 5.0 (+1) 1.0 (+1) 2.0 (-1) 2.0 (-1) 2.01 ± 0.008 2.309 1.502 ± 0.012

18 3.5 (0) 0.7 (0) 3.5 (0) 3.5 (0) 5.30 ± 0.034 5.558 2.084 ± 0.032

19 2.0 (-1) 1.0 (+1) 5.0 (+1) 2.0 (-1) 2.08 ± 0.049 2.526 1.520 ± 0.008

20 5.0 (+1) 0.4 (-1) 5.0 (+1) 2.0 (-1) 2.72 ± 0.063 2.709 1.523 ± 0.041

21 5.0 (+1) 1.0 (+1) 5.0 (+1) 5.0 (+1) 4.79 ± 0.052 4.877 1.941 ± 0.026

22 5.0 (+1) 1.0 (+1) 5.0 (+1) 2.0 (-1) 3.56 ± 0.043 3.721 1.842 ± 0.022

23 2.0 (-1) 0.4 (-1) 2.0 (-1) 5.0 (+1) 1.49 ± 0.057 1.923 1.238 ± 0.024

24 2.0 (-1) 1.0 (+1) 5.0 (+1) 5.0 (+1) 3.22 ± 0.029 3.720 1.704 ± 0.004

25 2.0 (-1) 1.0 (+1) 2.0 (-1) 2.0 (-1) 1.70 ± 0.031 1.796 1.311 ± 0.016

26 5.0 (+1) 0.4 (-1) 2.0 (-1) 5.0 (+1) 2.27 ± 0.040 2.145 1.384 ± 0.051

27 3.5 (0) 0.7 (0) 3.5 (0) 3.5 (0) 5.02 ± 0.081 5.558 2.241 ± 0.038

28 2.0 (-1) 0.4 (-1) 2.0 (-1) 2.0 (-1) 1.43 ± 0.045 1.664 1.104 ± 0.018

29 2.0 (-1) 0.4 (-1) 5.0 (+1) 2.0 (-1) 1.79 ± 0.064 1.766 1.642 ± 0.022

30 2.0 (-1) 1.0 (+1) 2.0 (-1) 5.0 (+1) 2.32 ± 0.034 2.652 1.645 ±0.016


331

optimal solutions was carried out by independent

experiments using the same conditions.

Protease activity in the strain ITBHU02—Among

15 soil isolates, two strains, interestingly one

ITBHU02, were shown to have scant growth level on

SM plate indicating negligible protease activity

present in isolate Bacillus aryabhattai ITBHU02.

Further experimentation with the fermented broth was

illustrated maximum protease activity (1.96±0.3 U mL-1)

at 41-42 h in strain ITBHU02. The characteristic of

protease deficiency of the isolate qualifies it for cost

effective production of enzyme L-asparaginase with

high turnover.

Validation of experimental designs—In order to

validate the optimal results generated by RSM and

GA models, independent experiments were performed

using the optimum levels of significant factors and

at middle level of other media components at same

physical conditions. Table 7 shows the different

optimized media compositions designed through

RSM and GA models along with their respective

predicted results. Experimentation with RSM data, the

mean specific activity was observed 5.74 U mg-1

,

which is in good accordance with model predicted

value, 5.89 U mg-1

. However, independently

experimented results for the different sample spaces

of GA based model have shown considerable

agreement with the predicted results. A maximum

average productivity of L-asparaginase enzyme

obtained after implementing GA based technique

was 6.35 U mg-1

which was rather higher as compared

to the RSM based result.

Table 5—ANOVA study for L-asparaginase production

Source Degree of freedom Sequential sum

of square

Adjusted sum

of square

Adjusted mean

of square

F value P value

Regression 14 65.0825 65.0825 4.6487 38.15 0.000

Linear 4 15.7140 15.8037 3.9509 32.42 0.000

Square 4 47.9729 47.9729 11.9932 98.42 0.000

Interaction 6 1.3956 1.3956 0.2326 1.91 0.150

Residual Error 14 1.7060 1.7060 0.1219

Lack-of-Fit 10 1.2651 1.2651 0.1265 1.15 0.486

Pure Error 4 0.4410 0.4410 0.1102

Total 29 67.9617

S = 0.349085 PRESS = 9.10775

R-Sq = 97.49%, R-Sq (pred.) = 86.60%, R-Sq (adj.) = 94.80%

Table 6—Regression coefficients for response through RSM for L-asparaginase production

Model term Coefficient Standard error coefficient t-value P-value

Constant -10.6448 1.42033 -7.495 0.000

(X3): KH2PO4 3.4039 0.31709 10.735 0.000

(X4): MgSO4.7H2O 7.1389 1.58546 4.503 0.000

(X7): Glucose 1.8350 0.31709 5.787 0.000

(X8): L-asparagine 1.5211 0.31709 4.797 0.000

(X3.X3): KH2PO4 x KH2PO4 -0.5024 0.02962 -16.958 0.000

(X4.X4): MgSO4.7H2O x MgSO4.7H2O -6.1146 0.74060 -8.256 0.000

(X7.X7): Glucose x glucose -0.3096 0.02962 -10.450 0.000

(X8.X8): L-asparagine x L-asparagine -0.2335 0.02962 -7.881 0.000

(X3.X4): KH2PO4 x MgSO4.7H2O 0.1403 0.19394 0.723 0.481

(X3.X7): KH2PO4 x glucose 0.0758 0.03879 1.955 0.071

(X3.X8): KH2PO4 x L-asparagine -0.0042 0.03879 -0.107 0.916

(X4.X7): MgSO4.7H2O x glucose 0.3486 0.19394 1.798 0.094

(X4.X8): MgSO4.7H2O x L-asparagine 0.3319 0.19394 1.712 0.109

(X7.X8): Glucose x L-asparagine 0.0375 0.03879 0.967 0.350


332

Discussion

In the present study, a novel bacterial strain

ITBHU02, producing extracellular L-asparaginase

enzyme, was isolated from soil and identified as

Bacillus aryabhattai strain ITBHU02 based on its

biochemical analysis and 99.93% of 16S rDNA

sequence homology with Bacillus aryabhattai

B8W22. Fatty acid methyl ester (FAME) profile

suggested very close phylogeny to the aforesaid

species. Being a gram positive bacteria and lacking

an outer membrane, the strain ITBHU02 is benefited

over currently used sources strains for the production

of therapeutic L-asparaginase (viz. E. coli and

Erwinia carotovora) as the gram positive bacteria do

not have a periplasmic space and therefore, periplasmic

proteins. Rather, the gram positive bacteria secrete

several enzymes into surrounding medium (generally

called as exoenzymes) that ordinarily would be

periplasmic in gram-negative bacteria29

. Additionally,

the protease deficient property of the strain imparts

economical values to enzyme L-asparaginase, which

makes the strain cost effective. The presence of

protease activity in fermented medium might cause

the degradation of different proteins of interest and so

the enzyme. The characteristics of protease deficiency

of a strain may improve the production profile of

enzyme L-asparaginase.

Fig. 6—Best fitness plot showing the progressive performance (L-

asparaginase production) of GA over generations till the

achievement of optimum solution. Variable (1) KH2PO4 (2)

MgSO4.7H2O (3) L-asparagine and (4) glucose

Fig. 5—Contour plot for L-asparaginase production showing the synergistic effects of (a) MgSO4.7H2O and KH2PO4, (b) L-asparagine and

MgSO4.7H2O, (c) L-asparagine and glucose, (d) glucose and KH2PO4, (e) MgSO4.7H2O and glucose, and (f) L-asparagine and KH2PO4


333

Although L-asparaginase production from the

isolate ITBHU02 started from the 7–8 h and reached

to a maximum on 24-25 h (3.02±0.1 Umg-1

protein),

its further expression was decreased abruptly and after

80 h enzyme level reached to minimum. In Serratia

marcescens ATCC 60 expression of L-asparaginase

began at the early–exponential phase (after 8 h of

cultivation) whereas maximum accumulation of the

enzyme was attended at 34 h and suddenly decreased

further due to increasing of medium pH5.

Bacillus aryabhattai ITBHU02 could grow well at 25, 30 and 37 °C (optimum growth temperature),

whereas the optimum temperature for maximum

L-asparaginase production was found at 37 °C (Fig. 4b) and at slightly alkaline pH 7.5 (Fig. 4c).

Other workers also reported L-asparaginase production in different microorganisms in a modest

alkaline range (i.e. pH 7-8)30,32

. The isolate was capable of utilizing a wide variety

of carbon sources. However, glucose followed by

galactose was the best carbon source in the

present study (Table 1). Boeck et al36

. demonstrated

the ability of glucose to increase the level of

L-asparaginase in E. coli, whereas, in Pectobacterium

carotovorum it was the best carbon source supplied

synergistically with L-asparagine33

. In contrast, the

glucose was reported as a catabolite repressor for

production of L-asparaginase in bacteria32

. The use

of sodium citrate along with glucose at lower

concentration (0.5% w/v of each) was found to

counterbalance the repressive effect of glucose and

gives a higher yield of the enzyme31

.

Supplementation of a nitrogen source along with

BMM promoted better growth of biomass as well as

L-asparaginase synthesis. Organic sources were

utilized more efficiently as compared to inorganic

sources for the enzyme production. Beef extract gave

relatively better result amongst different organic

sources studied. Production of enzyme in the presence

of yeast extract was quite comparable to that of beef

extract. Verma et al.2 reported the importance of yeast

extract at low concentration for cell growth and

L-asparaginase synthesis.

The presence of L-asparagine in the medium

improved the enzyme productivity approximately

3 fold better (3.23±0.2 U/mg-1

protein) as compare to

the control. However, rest of all the inducers viz.

L-aspartate, L-glutamine, L-glutamate and histidine

were also found to improve the enzyme level.

This confirmed that L-asparaginase synthesized in

Bacillus aryabhattai ITBHU02 was an inducible

enzyme. Multisubstrate induction of enzyme

asparaginase within different microbes has also

been reported34,35

. Dunlop and Roon37

introduced

the presence of both constitutive and inducible

asparaginases in S. cerevisae, at intracellular and

extracellular localization respectively.

Nowadays, RSM and artificial intelligence based

techniques such as GA are popular data analysis tools

used for a wide range of processes including

optimization of biological processes13

. In the present

study, RSM and GA based model were built, and

fitness and prediction capability was evaluated

heuristically. The R2

value by RSM based model has

shown 0.97 for the enzyme production, while RMSE

and SEP were 0.3 and 4.11 % respectively with a

maximum error of prediction 9.98%. These values

had shown that RSM-built model for the productivity

of L-asparaginase had a superior applicability.

Maximum enzyme yield of 5.74±0.08 Umg-1

was

predicted at RSM optimized concentration of glucose

4.14 gL-1

; KH2PO4 3.77 gL-1

; MgSO4.7H2O 0.85 gL-1

;

Table 7—Summary of comparative results obtained for maximum L-asparaginase production in initial and different optimization phases

Media concentration (gL-1) Specific activity

(Umg-1) Type of medium KH2PO4

(gL-1)

MgSO4.7H2O

(gL-1)

Glucose

(gL-1)

L-Asparagine

(gL-1) Model predicted Experimental

Dry cell weight

(gL-1)

BMM - - - - - 3.02±0.10 2.181±0.012

OFAT optimized - - - - - 3.62±0.24 2.118±0.026

RSM optimized 3.77 0.852 4.14 4.14 5.89 5.74±0.08 2.251±0.018

GA optimized

Sample No. 1 4.43 0.81 5.50 5.73 6.557 5.98±0.18 2.231±0.006

Sample No. 2 3.71 0.94 5.08 4.18 6.082 5.84±0.21 2.214±0.017

Sample No. 3 4.19 0.72 4.85 5.60 6.403 6.18±0.11 2.247±0.012

Sample No. 4 4.07 0.82 4.91 5.20 6.382 6.35±0.10 2.238±0.010


334

and L-asparagine 4.14 gL-1

. After application of GA,

the maximum yield of L-asparaginase enzyme by

Bacillus arybhattai strain ITBHU02 was improved to

6.35 Umg-1

. However, the optimum concentration for

KH2PO4, MgSO4.7H2O, L-asparagine, and glucose

obtained through GA based model was 4.07, 0.82,

4.91, and 5.2 gL-1

respectively. The yield of

L-asparaginase obtained by employing GA was 7.8%

higher than the yield obtained after RSM-built model.

The analysis and results from the current study

have approved the applicability of both RSM and GA

to be applied in cohesion to the complex bioprocess

systems. Similar optimization process may be used

to improve the productivity of biocatalytic

metabolites from potent microorganisms without

adding any extra cost.

Acknowledgement Thanks are due to the University Grants

Commission, New Delhi and the School of

Biochemical Engineering, I.I.T., B.H.U., Varanasi,

India for financial support and Dr. Vivek Kumar

Singh (Department of Computer Science, Banaras

Hindu University, Varanasi) for artificial intelligence

based computing.

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