Indian Journal of Experimental Biology
Vol. 51, April 2013, pp. 322-335
Statistical and evolutionary optimization for enhanced production of an antileukemic 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,
————————
*
Correspondent author
Telephone: +91 542 6702886;
Fax: +91 542 2368428
E-mail: [email protected] (S. K. Srivastava);
[email protected]
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
SINGH & SRIVASTAVA: L-ASPARAGINASE PRODUCTION FROM BACILLUS ARYABHATTAI ITBHU02
methods such as Plackett-Burman design technique
(PBD), central composite design (CCD) and BoxBehnken 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
323
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
324
INDIAN J EXP BIOL, APRIL 2013
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 firstorder 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-
SINGH & SRIVASTAVA: L-ASPARAGINASE PRODUCTION FROM BACILLUS ARYABHATTAI ITBHU02
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
325
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 cellfree 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; nonmannitol 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
INDIAN J EXP BIOL, APRIL 2013
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 anteisoC15:0 (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.
Fig. 2—SEM image of the strain ITBHU02 showing morphological
characteristics
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. 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)
SINGH & SRIVASTAVA: L-ASPARAGINASE PRODUCTION FROM BACILLUS ARYABHATTAI ITBHU02
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
327
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 PlackettBurman 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)
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
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
INDIAN J EXP BIOL, APRIL 2013
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)
Control*
Fructose
Glucose
Galactose
Starch
Sucrose
Maltose
Malt extract
Lactose
Tri-sodium citrate
Sodium pyruvate
1.24±N.D.
2.12±0.02
3.44±0.20
2.97±0.11
1.94±0.02
1.61±0.12
2.44±0.10
1.44±N.D.
1.79±0.05
1.37±0.02
1.44±0.02
1.462±0.021
1.986±0.007
2.211±0.018
1.893±0.034
1.761±0.024
1.435±0.012
1.457±0.010
1.361±0.041
1.622±0.023
1.086±0.008
1.176±0.027
Inducer compounds
(0.3% w/v)
Control***
L-asparagine
L-asparate
L-glutamine
L-glutamate
Histidine
1.11±0.15
3.23±0.20
1.26±0.11
2.47±0.10
1.64±0.30
1.17±0.05
1.687±0.022
2.118±0.048
1.886±0.012
1.732±0.008
2.078±0.014
1.776±0.021
N-sources
Enzyme
(Umg-1 protein)
Dry cell weight
(gL-1)
Organic source
Control**
Peptone
Beef extract
Yeast extract
Tryptone
Gelatin
Casein
Urea
2.11±0.21
2.14±0.10
3.62±0.24
3.04±0.14
2.36±0.12
2.22±0.04
2.07±0.30
2.12±0.11
2.054±0.025
2.281±0.041
2.118±0.026
1.992±0.017
2.210±0.052
1.645±0.023
1.867±0.018
1.524±0.034
Inorganic source
Ammonium sulphate
Ammonium chloride
Ammo. nitrate
Ammo. acetate
Ammo. oxalate
Tri-ammo. citrate
Potassium nitrate
2.11±N.D.
1.97±0.03
2.45±0.12
2.23±0.20
1.96±0.04
1.68±0.02
1.87±0.10
1.431±0.010
1.084±0.034
1.894±0.020
1.221±0.006
1.243±0.051
2.012±0.014
1.043±0.041
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)
Trials
Beef
extract
(X1)
Na2HPO4.
2H2O
(X2)
KH2
PO4
(X3)
MgSO4.
7H2O
(X4)
NaCl
(X5)
CaCl2.
2H2O
(X6)
1
4.0 (+1)
1.0 (-1)
5.0 (+1)
0.1(-1)
0.1 (-1)
2
4.0 (+1)
10.0 (+1)
1.0 (-1)
1.0 (+1)
1.0 (+1) 0.01 (-1)
3
1.0 (-1)
1.0 (-1)
5.0 (+1)
4
4.0 (+1)
1.0 (-1)
5
1.0 (-1)
6
4.0 (+1)
7
Enzyme specific activity Dry cell
(Umg-1)
weight (gL-1)
Glucose L-asparagine
(X7)
(X8)
Observed
0.01 (-1) 5.0 (+1)
Predicted
5.0 (+1)
2.14 ± 0.023
2.155
1.881 ± 0.018
1.0 (-1)
5.0 (+1)
1.89 ± 0.013
1.794
1.486 ± 0.036
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
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
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
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
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
SINGH & SRIVASTAVA: L-ASPARAGINASE PRODUCTION FROM BACILLUS ARYABHATTAI ITBHU02
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 –
0.502X 32 – 6.115X 42 – 0.234X 42 – 0.31X 82 + 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)
Table 3—Statistical analysis through PBD showing coefficients
and effects for each factor on L-asparaginase yield
Variables
Constant
(X1): Beef extract
(X2)Na2HPO4.2H2O
(X3): KH2PO4
(X4): MgSO4.7H2O
(X5): NaCl
(X6): CaCl2.2H2O
(X7): Glucose
(X8): L-asparagine
Effect
0.2083
0.1483
-0.2717
0.6783
0.1217
0.0183
-0.2750
-0.8183
Coefficient t-value
2.625
0.104
0.074
-0.135
0.339
0.060
0.009
-0.137
-0.409
71.64
2.84
2.02
-3.71
9.25
1.66
0.25
-3.75
-11.16
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)
*
P-value
0.000
0.066**
0.136**
0.034*
0.003*
0.196**
0.819**
0.033*
0.002*
329
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:
INDIAN J EXP BIOL, APRIL 2013
330
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)
Trials
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Specific activity
(Umg-1)
KH2PO4
(X3)
MgSO4.7H2O
(X4)
Glucose
(X7)
L-asparagine
(X8)
Observed
Predicted
6.5 (+α)
3.5 (0)
3.5 (0)
3.5 (0)
3.5 (0)
0.5 (-α)
3.5 (0)
3.5 (0)
3.5 (0)
3.5 (0)
5.0 (+1)
5.0 (+1)
5.0 (+1)
3.5 (0)
3.5 (0)
2.0 (-1)
5.0 (+1)
3.5 (0)
2.0 (-1)
5.0 (+1)
5.0 (+1)
5.0 (+1)
2.0 (-1)
2.0 (-1)
2.0 (-1)
5.0 (+1)
3.5 (0)
2.0 (-1)
2.0 (-1)
2.0 (-1)
0.7 (0)
0.7 (0)
1.3 (+α)
0.7 (0)
0.7 (0)
0.7 (0)
0.1 (-α)
0.7 (0)
0.7 (0)
0.7 (0)
1.0 (+1)
0.4 (-1)
0.4 (-1)
0.7 (0)
0.7 (0)
0.4 (-1)
1.0 (+1)
0.7 (0)
1.0 (+1)
0.4 (-1)
1.0 (+1)
1.0 (+1)
0.4 (-1)
1.0 (+1)
1.0 (+1)
0.4 (-1)
0.7 (0)
0.4 (-1)
0.4 (-1)
1.0 (+1)
3.5 (0)
3.5 (0)
3.5 (0)
6.5 (+α)
3.5 (0)
3.5 (0)
3.5 (0)
0.5 (-α)
3.5 (0)
3.5 (0)
2.0 (-1)
2.0 (-1)
5.0 (+1)
3.5 (0)
3.5 (0)
5.0 (+1)
2.0 (-1)
3.5 (0)
5.0 (+1)
5.0 (+1)
5.0 (+1)
5.0 (+1)
2.0 (-1)
5.0 (+1)
2.0 (-1)
2.0 (-1)
3.5 (0)
2.0 (-1)
5.0 (+1)
2.0 (-1)
3.5 (0)
0.5 (-α)
3.5 (0)
3.5 (0)
6.5 (+α)
3.5 (0)
3.5 (0)
3.5 (0)
3.5 (0)
3.5 (0)
5.0 (+1)
2.0 (-1)
5.0 (+1)
3.5 (0)
3.5 (0)
5.0 (+1)
2.0 (-1)
3.5 (0)
2.0 (-1)
2.0 (-1)
5.0 (+1)
2.0 (-1)
5.0 (+1)
5.0 (+1)
2.0 (-1)
5.0 (+1)
3.5 (0)
2.0 (-1)
2.0 (-1)
5.0 (+1)
1.89 ± 0.033
2.73 ± 0.014
4.83 ± 0.041
3.87 ± 0.024
4.68 ± 0.031
0.68 ± 0.047
2.38 ± 0.011
2.17 ± 0.022
5.86 ± 0.062
5.37 ± 0.041
2.51 ± 0.047
1.83 ± 0.023
2.77 ± 0.070
5.67 ± 0.051
5.71 ± 0.038
2.34 ± 0.061
2.01 ± 0.008
5.30 ± 0.034
2.08 ± 0.049
2.72 ± 0.063
4.79 ± 0.052
3.56 ± 0.043
1.49 ± 0.057
3.22 ± 0.029
1.70 ± 0.031
2.27 ± 0.040
5.02 ± 0.081
1.43 ± 0.045
1.79 ± 0.064
2.32 ± 0.034
1.746
2.749
4.228
3.698
4.165
0.328
2.486
1.846
5.558
5.558
3.128
1.924
3.268
5.558
5.558
2.362
2.309
5.558
2.526
2.709
4.877
3.721
1.923
3.720
1.796
2.145
5.558
1.664
1.766
2.652
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.
Dry cell weight
(gL-1)
1.412 ± 0.014
1.511 ± 0.004
2.141 ± 0.010
1.648 ± 0.021
2.031 ± 0.041
0.891 ± 0.007
1.517 ± 0.023
1.445 ± 0.005
2.114 ± 0.016
2.004 ± 0.044
1.531 ± 0.002
1.451 ± 0.012
1.629 ± 0.046
2.156 ± 0.030
2.123 ± 0.014
1.622 ± 0.028
1.502 ± 0.012
2.084 ± 0.032
1.520 ± 0.008
1.523 ± 0.041
1.941 ± 0.026
1.842 ± 0.022
1.238 ± 0.024
1.704 ± 0.004
1.311 ± 0.016
1.384 ± 0.051
2.241 ± 0.038
1.104 ± 0.018
1.642 ± 0.022
1.645 ±0.016
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
SINGH & SRIVASTAVA: L-ASPARAGINASE PRODUCTION FROM BACILLUS ARYABHATTAI ITBHU02
331
Table 5—ANOVA study for L-asparaginase production
Source
Regression
Linear
Square
Interaction
Residual Error
Lack-of-Fit
Pure Error
Total
Degree of freedom
Sequential sum
of square
Adjusted sum
of square
Adjusted mean
of square
F value
P value
14
4
4
6
14
10
4
29
65.0825
15.7140
47.9729
1.3956
1.7060
1.2651
0.4410
67.9617
65.0825
15.8037
47.9729
1.3956
1.7060
1.2651
0.4410
4.6487
3.9509
11.9932
0.2326
0.1219
0.1265
0.1102
38.15
32.42
98.42
1.91
0.000
0.000
0.000
0.150
1.15
0.486
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
Constant
(X3): KH2PO4
(X4): MgSO4.7H2O
(X7): Glucose
(X8): L-asparagine
(X3.X3): KH2PO4 x KH2PO4
(X4.X4): MgSO4.7H2O x MgSO4.7H2O
(X7.X7): Glucose x glucose
(X8.X8): L-asparagine x L-asparagine
(X3.X4): KH2PO4 x MgSO4.7H2O
(X3.X7): KH2PO4 x glucose
(X3.X8): KH2PO4 x L-asparagine
(X4.X7): MgSO4.7H2O x glucose
(X4.X8): MgSO4.7H2O x L-asparagine
(X7.X8): Glucose x L-asparagine
Coefficient
Standard error coefficient
t-value
P-value
-10.6448
3.4039
7.1389
1.8350
1.5211
-0.5024
-6.1146
-0.3096
-0.2335
0.1403
0.0758
-0.0042
0.3486
0.3319
0.0375
1.42033
0.31709
1.58546
0.31709
0.31709
0.02962
0.74060
0.02962
0.02962
0.19394
0.03879
0.03879
0.19394
0.19394
0.03879
-7.495
10.735
4.503
5.787
4.797
-16.958
-8.256
-10.450
-7.881
0.723
1.955
-0.107
1.798
1.712
0.967
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.481
0.071
0.916
0.094
0.109
0.350
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.
332
INDIAN J EXP BIOL, APRIL 2013
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
Fig. 6—Best fitness plot showing the progressive performance (Lasparaginase production) of GA over generations till the
achievement of optimum solution. Variable (1) KH2PO4 (2)
MgSO4.7H2O (3) L-asparagine and (4) glucose
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.
SINGH & SRIVASTAVA: L-ASPARAGINASE PRODUCTION FROM BACILLUS ARYABHATTAI ITBHU02
333
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)
Dry cell weight
(gL-1)
Type of medium
KH2PO4
(gL-1)
MgSO4.7H2O
(gL-1)
Glucose
(gL-1)
L-Asparagine
(gL-1)
BMM
OFAT optimized
RSM optimized
GA optimized
Sample No. 1
Sample No. 2
Sample No. 3
Sample No. 4
3.77
0.852
4.14
4.14
5.89
3.02±0.10
3.62±0.24
5.74±0.08
2.181±0.012
2.118±0.026
2.251±0.018
4.43
3.71
4.19
4.07
0.81
0.94
0.72
0.82
5.50
5.08
4.85
4.91
5.73
4.18
5.60
5.20
6.557
6.082
6.403
6.382
5.98±0.18
5.84±0.21
6.18±0.11
6.35±0.10
2.231±0.006
2.214±0.017
2.247±0.012
2.238±0.010
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
Model predicted Experimental
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;
INDIAN J EXP BIOL, APRIL 2013
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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