Zaliha Raja Abd. Rahman*^{1} · Nor Hafizah Ahmad Kamarudin^{1} · Mahiran Basri^{2} · Abu Bakar Salleh^{1}
Nowadays, thermostable organic solvents tolerant bacterial lipases (EC 3.1.1.3) gain wide industrial and biotechnological interest due to their novel, multifold applications and resistance to harsh industrially conditions. They are capable of catalyzing an abroad range of novel and important reactions in both aqueous and nonaqueous media. Thereby these lipases present an attractive field for future research and create interest to isolate and study of novel thermostable organic solvents tolerant lipase producing bacteria and the factors affecting their lipase production (Sharma et al. 2001; Svendsen, 2004; Hasan et al. 2006). Predictive models have been accepted as informative tools for rapid and cost effective study of microbial growth, their products development, risk assessment and scientific purposes (Ross, 1999). Response surface methodology (RSM) is a mathematical modelling system, which assesses the relationships between the response(s) and the independent variables (Manohar and Divakar, 2004), and defines the importance of the independent variables, alone or in combination, in the model. Although RSM is the most frequently used techniques for modelling the biological processes, it is not applicable to all modelling studies (Baş and Boyaci, 2007b). Artificial neural networks (ANNs) occupy a place of prominence, in the past decade, among modeling systems in biological studies (Dutta et al. 2004). Indeed, an artificial neural network is an adaptive data processing paradigm that is inspired by the way biological nervous systems process information. ANNs learn by examples via a parallel processing style, improve their performance and, therefore, produce reasonable response(s). In the present
investigation, the lipase production behaviour of a newly isolated thermophilic
bacterium; The bacterial strain used
in this study was a novel thermostable, organic solvent tolerant lipase
producer, isolated from cooking oil contaminated soil from Selangor, Malaysia.
It was identified as
The inoculum was prepared by transferring a loopfull of bacterial cells from a fresh pure culture into 10 ml TSB and incubated at 55ºC, overnight, under 150 rpm agitation. The cells were harvested by centrifugation at 12,000 x g and 4ºC for 10 min. The bacterial pellet was resuspended in sterile normal saline solution (8.5 g/l NaCl) to give an absorbance of 0.5 at 600 nm.
In order to select the
best lipase production medium, eight different media
were tested. The compositions of the media were (% w/v): M1: peptone (3), yeast
extract (1), NaCl (0.5), olive oil (1% v/v) (Hun et al. 2003); A1 (modified
M1): M1 + CaCl The media were sterilized for 15 min at 121ºC after pH adjustment to 7.0. Bacterial inoculum (2%) was then inoculated into 50 ml production medium and incubated by agitation under 150 rpm, for 48 hrs at 60ºC. The cell free supernatant was obtained by centrifugation at 12,000 x g, 4ºC for 15 min prior to lipase assay.
Lipase activity was
assayed according to Kwon and Rhee (1986) method using olive oil as substrate.
The reaction mixture, consisting of 1 ml crude enzyme (culture filtrate), 2.5 ml
olive oil emulsion (properly mixed of an equal volume olive oil with sodium
phosphate buffer, 50 mM, pH 7.0), and 0.02 ml of 20 mM CaCl Protein concentration was determined according to the Bradford method using the Bio-Rad assay reagent (catalogue number 500-0006) and bovine serum albumin as standard, according to the manufacturer's instructions. In order to determine the biomass, 10 ml sample of culture was filtered (cellulose acetate filter, pore size of 0.22 µm, Sartorius) and washed properly with acetone followed by distilled water. The filter was dried at 80ºC to a constant weight. Cell growth was monitored by turbidity measurement at 600 nm (Garcia-Gimeno et al. 2005). Each sample (5 ml) was centrifuged (12,000 x g and 4ºC for 10 min) and the precipitate was washed twice by resuspending in distilled water after removing the supernatant and repeating the centrifugation process. Optical density was measured at 600 nm after resuspending the precipitate.
Response surface method (RSM) offers a large amount of information from a small number of experiments because of using special designs those help the appropriate model be fitted to the response(s). We chose a central composite rotatable design (CCRD), as the most popular RSM design, for the experiments due to its obvious advantages of rotability and the ability to analyse the interaction effects. A central composite design (CCD) includes three groups of design points. Factorial points that consists of all possible combinations of the +1 and -1 levels of the factors; star or axial points that have all of the factors set to 0, the midpoint, except one factor, which has the value +/- Alpha; and center points, which are points with all levels set to coded level 0, the midpoint of each factor range. Typically, the center point of the design is repeated, often four or more times to get a good estimate of experimental error (pure error variance). This gives an adequate estimate of the variation of the response and provides the number of degrees of freedom needed for an adequate statistical test of the model. To summarize, central composite rotatable designs require 5 levels of each factor: -Alpha, -1, 0, 1, and +Alpha. Rotatable designs provide the desirable property of constant prediction variance at all points that are equidistant from the design center, thus improving the quality of the prediction (Montgomery, 2004; Baş and Boyaci, 2007a; Hill and Lewicki, 2007). Although, CCRD is appropriate for calibration the full quadratic models, a complete description of the process behaviour might require a cubic or higher order model. Design-Expert allows creating the higher order models (up to the fifth order) by adding the appropriate terms. On the other hand, rarely all of the terms of the full models are needed in an application. Model reduction (modification) consists of eliminating those terms that are not desired (Montgomery, 2004; Baş and Boyaci, 2007a; Hill and Lewicki, 2007). A five-levels-six-factors central composite rotary design (CCRD) was employed throughout the study, requiring 33 experiments (Cochran and Cox, 1992). The fractional factorial design consisted of 16 factorial points, 12 axial points and 5 center points. The following variables and levels were incorporated: growth temperature (45-65ºC); medium volume (50-200 ml); inoculum size (1-5%); agitation rate (0-200 rpm); incubation period (24-72 hrs) and initial pH (5-9) (Table 1). The experimental data [40 points include CCRD design (Table 1) and optimization data (Table 2)] were divided into three sets: training set, testing set and validating set. Each of different combinations was tested in triplicate.
The CCRD design experimental data were analyzed using Design Expert version 6.06 (Stat Ease Inc. Minneapolis, USA) and then interpreted. The predicted values obtained from RSM models were compared with actual values for testing the models (Table 3) and predicted optimal conditions (Table 2) were used as validating sets.
A commercial ANN software, NeuralPower version 2.5 (CPC-X Software) was used throughout the study. Multilayer normal feedforward and multilayer full feedforward neural networks were used to predict the responses. The networks were trained by different learning algorithms (incremental back propagation, IBP; batch back propagation, BBP; quickprob, QP; genetic algorithm, GA; and Levenberg-Marquardt algorithm, LM). In order to determine the optimal network topology, only one hidden layer was used and the number of neurons in this layer and the transfer functions of hidden and output layers (sigmoid, hyperbolic tangent function, Gaussian, linear, threshold linear and bipolar linear) were iteratively determined by developing different networks. Each network was trained until the network root of mean square error (RMSE) was lower than 0.0001, and average correlation coefficient (R) and average determination coefficient (DC) were equal to 1. At the start of the training, weights were initialized with random values and adjusted through a training process in order to minimize network error. Since the replicates at center point do not improve the prediction capability of the network (Baş and Boyaci, 2007a), the models were improved by using mean of center points. The experimental data (Table 1 and Table 3) were divided into two sets: Training set and testing set, and experimental values of predicted optimal conditions (Table 2) were used as validating set.
In order to test the
estimation capabilities of the techniques, the predicted responses obtained
from RSM and ANNs were compared with the actual responses. The coefficient of
determination (R where _{,exp} and y_{i}_{,cal} are the experimental and calculated responses,
respectively, and p is the number of the experimental run.where R Bivariate correlation was used to test the correlation between lipase production and bacterial growth using SPSS software (Version 18).
The production of lipases is mostly inducer-dependent (Lotti et al. 1998). Different media have different stimulation effects on lipase production (He and Tan, 2006) based on the physiological and biochemical pathways of the bacterium. In order to select the best lipase production medium, the ability of bacterium to produce lipase was tested in eight different liquid media (Figure 1) those were appropriate for the bacterium growth. These media could be categorized in four groups. The first group included
M1, A1 and A2 media. The media of this group were established based on the M1
basal medium, which was composed of peptone and yeast extract as organic
nitrogen sources, olive oil as oil carbon source and Na The second group includes
TYEM and its modified form, MTYEM. This group, similar to first group was
composed of organic nitrogen sources, tryptone and yeast extract, and oil carbon
source, olive oil. These media had Ca The bacterium in the last
3 tested media produced very low amount of the lipase. M3 and M5 media as the
third group composed of peptone, meat extract and yeast extract as organic
nitrogen sources, olive oil (triolein) as oil carbon source and Na As a result, A1 production medium was chosen as the medium to be used in the further study.
In this research we tried to analyse, model and interpret the experimental data using two completely different processing views; response surface methodology (RSM) as a mathematical modeling system, and artificial neural network (ANN) as an adaptative data processing method based on learning and interpretation. RSM is a collection of statistical and mathematical techniques and shows several advantages over the ANNs (Manohar and Divakar, 2004). This method offers a large amount of information from a small number of experiments and provides mathematical model and equation for system studied. In addition, RSM has been furnished by ANOVA analysis, which can help to statistically analyse the whole model produced, every single parameters involved and their interactions. Although, RSM is based on the use of a second order equation as its major drawback, the biggest mistake is fitting the all-experimental data to the quadratic model. In fact, it is possible to predict a model equation with a higher degree than the second order and modified it by removing non significant terms. We employed the ability of Design Expert (Stat Ease Inc. Minneapolis, USA) mathematically packed program throughout the study to find the best modified fitted models for the experimental data analysis (Montgomery, 2004; Baş and Boyaci, 2007a). On the other hand, in most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. ANNs gather their knowledge by detecting the patterns and relationships in data and learn through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements, connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. The ability of ANNs to learn the process characteristics with little prior knowledge is desirable and eases their implementation and heightens their modelling potential. This property makes ANNs powerful and flexible tools that are well suited to model complex relationships between inputs and outputs or to find patterns in data via non-linear, distributed, parallel and local processing and adaptation (Agatonovic-Kustrin and Beresford, 2000; Baş and Boyaci, 2007b). Indeed ANN is a superior and more accurate modeling technique when compared to the RSM as it represents the nonlinearities in much better way (Dutta et al. 2004). On the other hand, neural networks also have the disadvantage of requiring large amounts of training data in comparison with RSM that offers a large amount of information from a small number of experiments. This advantage of RSM is because of its experimental design (Montgomery, 2004). To overcome this ANN problem, in present study, we used the RSM idea, and then a statistical experimental design, CCRD, was employed to reduce the number of experiments.
The best fitting models
were determined through multiple linear regressions with backward elimination.
Finally, the modified cubic polynomial model with very small "model P-values"
(< 0.0001) and large "lack of fit P-values" (0.2593 for growth and
0.3623 for specific activity) from the analysis of ANOVA (Table 4 and Table 5) and a suitable coefficient of determination (R Lipase specific activity
(Umg Bacterial growth (OD Where T, V, IS, Ag and t are symbols for growth temperature, medium volume, inoculum size, agitation rate and incubation time, respectively.
The best produced ANN
models in the present study were multilayer full feed forward incremental back
propagation networks with Gaussian transfer function and one hidden layer
consisted of 16 neurons. The optimized values of networks for learning rate and
momentum were 0.15 and 0.8, respectively. In the case of training data set, the
coefficient of determination (R
The interaction of the
parameters and optimal value of each variable is clearly represented in the
three dimensional response surface plots. Figure 2a i and Figure 2a ii,
represent the three dimensional plots as function of growth temperature and
inoculum size on lipase specific activity and bacterial growth, respectively,
when other parameters were kept at optimum point. Maximum lipase specific
activity of 13.1 Umg Suitable inoculum size provides sufficient nutrient and oxygen levels for enough growth of bacteria and therefore, enhance the lipase production. If the inoculum size is too small, insufficient number of bacteria will lead to reduced amount of secreted lipase. High inoculum size can result in the lack of oxygen and nutrient depletion in the culture media (Rahman et al. 2005; Shafee et al. 2005). Figures 2b i and ii, depict the interaction between medium volume and agitation rate. The maximum lipase specific activity and bacterial growth were obtained at different culture volume of 139 and 87 ml, respectively but technically similar agitation rate (118 and 115 rpm). Generally, suitable agitation lead to sufficient supply of dissolved oxygen in the culture medium (Kumar and Takagi, 1999). Agitation also promotes a reduction in nutrient particle size, favouring the nutrient homogenization in the culture medium, providing additionally a rise in mass transfer rates and nutrient uptake by bacteria, which favouring microbial growth (Beg et al. 2003). Though medium volume may have a great effect on the enzyme production, yet Figure 2b i reveals that medium volume had no significant effect on bacterial growth at optimum condition within the chosen range (50-200 ml). Although a larger medium volume initially contains more oxygen, nutrients and space for growth of bacteria, the void in the container and subsequently oxygenation of the medium will be decreased. On the other hand, it seems that ratio of surface area to volume (A/V) is important for lipase production where higher ratio cause higher oxygenation and lipase production (Woolley and Petersen, 1994). The combined effect of growth medium initial pH and incubation time on lipase production and bacterial growth is shown in Figure 2c i and Figure 2c ii, respectively. According to the plot, a neutral initial pH (7.0 and 7.2) caused maximum lipase production and bacterial growth. Maximum bacterial growth was achieved after 38 hrs incubation period, while the lipase production needed longer incubation time (48 hrs). Lipases are produced throughout bacterial growth, with peak production being obtained by late exponential growth phase (Gupta et al. 2004). Figure 2c i reveals that after 30 hrs incubation (near to end of the bacterial growth phase), lipase production was stimulated and drastically increased to reach a maximum amount at 48 hrs following by decrease of lipase specific activity due to reduction of active cells, and enzymes most probably because of proteolysis by present proteases as well as physical damages. To obtain the maximum lipase production, achievement of maximum possible number of bacteria (lipase producers) and enough lipase production stimulation is necessary. Significant correlation between lipase production and bacterial growth was observed when Bivariate correlation was used to analyse the data. Pearson correlation coefficient of 0.566 was statistically significant at the 0.01 level (2-tailed). It means that there was 32% correlation between lipase production and bacterial growth. On the other hand, Figure 3 shows the importance percentage of effective parameters on the bacterial growth and lipase production based on the ratio of optimized ANN weights. An ANN is formed from hundreds of artificial neurons, connected with coefficients (weights). The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. During training, the weights are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. In this stage, the ratio of optimized weights shows the incorporation percentage of each input parameter in final output that can be computed and presented as an importance value. pH with 18.2% and 21% of importance on bacterial growth and lipase production, respectively was the most effective factor, while inoculum size was the least effective factor with 13.5% and 13.7%, respectively. Incubation period (18.5%), Growth temperature (16.8%), agitation rate (15.2%) and medium volume (15%) were subsequent degrees of importance for bacterial growth. This sequence was different for lipase production as follows: Growth temperature (17.7%), medium volume (17.4%), incubation period (16.9%) and agitation rate (16%).
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2. Among the various optimum conditions, highest lipase specific activity
(13.1 Umg In the current study, ANN
and modified RSM were carried out and compared to study the bacterial growth
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Full Text - Lipase production and growth modeling of a novel thermophilic bacterium: Aneurinibacillus thermoaerophilus strain AFNA