Optimum DNA curvature using a hybrid approach involving an artificial neural network and genetic algorithm

In the present paper, a hybrid technique involving artificial neural network (ANN) and genetic algorithm (GA) has been proposed for performing modeling and optimization of complex biological systems. In this approach, first an ANN approximates (models) the nonlinear relationship(s) existing between its input and output example data sets. Next, the GA, which is a stochastic optimization technique, searches the input space of the ANN with a view to optimize the ANN output. The efficacy of this formalism has been tested by conducting a case study involving optimization of DNA curvature characterized in terms of the RL value. Using the ANN-GA methodology, a number of sequences possessing high RL values have been obtained and analyzed to verify the existence of features known to be responsible for the occurrence of curvature. A couple of sequences have also been tested experimentally. The experimental results validate qualitatively and also near-quantitatively, the solutions obtained using the hybrid formalism. The ANN-GA technique is a useful tool to obtain, ahead of experimentation, sequences that yield high RL values. The methodology is a general one and can be suitably employed for optimizing any other biological feature.     

Mathematical Modeling of the Reduction of Safranin onto Chemically Modified Rice Husks in Stirred Tank Reactor Using Response Surface Methodology and Artificial Neural Network

ABSTRACT

The effluents coming from the dye industries are colored and polluted, and the disposal of these wastes into receiving waters causes damage to the water as well as the environment. The use of rice husk for the removal of dye from wastewater has been explored in a stir tank reactor. The effects of operation variables such as adsorbent dosage, contact time, dye concentration, initial pH, and agitation on the removal of safranin were investigated in a stirred tank reactor. The combined effect of various process parameters on dye removal were analyzed using response surface methodology (RSM), and the modeling of the process parameter had been done using the artificial neural network simulation method. It was observed that response surface methodology can determine the optimization of the process parameters and the model derived from the simulation of the artificial neural network (ANN) (deviation from experimental results was ～0.09%) described the process variable efficiently. It was observed that at the initial solution pH of 6.28 and adsorbent dosage of 15.63 g L^?1, dye removal of safranin was 97%.     

Optimization of enzyme-assisted improvement of polyphenols and free radical scavenging activity in red rice bran: A statistical and neural network-based approach

The current study is focused on optimizing the parameters involved in enzymatic processing of red rice bran for maximizing total polyphenol (TP) and free radical scavenging activity (FRSA). The sequential optimization strategies using central composite design (CCD) and artificial neural network (ANN) modeling linked with genetic algorithm (GA) was performed to study the effect of incubation time (60–90?min), xylanase concentration (5–10?mg/g), cellulase concentration (5–10?mg/g) on the response, i.e., total polyphenol and FRSA. The result showed that incubation time has a negative effect on the response, while the square effect of xylanase and cellulase showed positive effect on the response. A maximum TP of 2,761?mg ferulic acid Eq/100?g bran and FRSA of 778.4?mg Catechin Eq/100?g bran was achieved with incubation time (min)?=?60.491; xylanase (mg/g)?=?5.4633; cellulase (mg/g)?=?11.5825. Furthermore, ANN-GA-based optimization showed better predicting capabilities as compared to CCD.     

A hybrid of back propagation neural network and genetic algorithm for optimization of collagen extraction from Malaysian cultured catfish (Hybrid Clarias sp.)

Fractional factorial design (FFD) was applied to evaluate the effects of various process parameters in influencing the extraction efficiency of pepsin soluble collagen (PSC) from muscles of cultured catfish (Clarias gariepinus×C. macrocephalus). Result of the first order factorial design showed that acetic acid concentration, acid extraction time, acetic acid to muscles ratio, and stirring speed posed significant effect (P<0.05) on the yield of PSC obtained at the end of the extraction process. Two different artificial intelligence techniques namely artificial neural network (ANN) and genetic algorithm (GA) were then integrated for optimizing the extraction conditions to obtain the highest yield of PSC. The ANN was trained using the back propagation algorithm. A model was successfully generated with R ² value of 0.9527 and MSE value of 0.1672 for unseen data set, implying a good generalization of the network. Input parameters of the established ANN model were subsequently optimized using GA. The hybrid of ANN-GA model predicted a maximum extraction yield of PSC at 238.25 mg/g under the following conditions: an acetic acid concentration of 0.70 M, the acetic acid to muscles ratio of 25.78 mL/g, and the stirring speed of 432.50 rpm. Verification of the optimization showed the percentage error differences between the experimental and predicted values were less than 5%, indicating excellent modeling, predicting ability and optimization by the ANN-GA model.     

Optimum DNA Curvature Using a Hybrid Approach Involving an Artificial Neural Network and Genetic Algorithm

Abstract

In the present paper, a hybrid technique involving artificial neural network (ANN) and genetic algorithm (GA) has been proposed for performing modeling and optimization of complex biological systems. In this approach, first an ANN approximates (models) the nonlinear relationship(s) existing between its input and output example data sets. Next, the GA, which is a stochastic optimization technique, searches the input space of the ANN with a view to optimize the ANN output. The efficacy of this formalism has been tested by conducting a case study involving optimization of DNA curvature characterized in terms of the R_L value. Using the ANN-GA methodology, a number of sequences possessing high R_L values have been obtained and analyzed to verify the existence of features known to be responsible for the occurrence of curvature. A couple of sequences have also been tested experimentally. The experimental results validate qualitatively and also near-quantitatively, the solutions obtained using the hybrid formalism. The ANN-GA technique is a useful tool to obtain, ahead of experimentation, sequences that yield high R_L values. The methodology is a general one and can be suitably employed for optimizing any other biological feature.     

Optimization of butanol production from tropical maize stalk juice by fermentation with Clostridium beijerinckii NCIMB 8052

Mixed sugars from tropical maize stalk juice were used to carry out butanol fermentation with Clostridium beijerinckii NCIMB 8052. Batch experiments employing central composite design (CCD) and response surface methodology (RSM) optimization were performed to evaluate effects of three factors, i.e. pH, initial total sugar concentration, and agitation rate on butanol production. Optimum conditions of pH 6.7, sugar concentration 42.2 g/L and agitation rate 48 rpm were predicted, under which a maximum butanol yield of 0.27 g/g-sugar was estimated. Further experiments demonstrated that higher agitation facilitated acetone production, leading to lower butanol selectivity in total acetone–butanol–ethanol (ABE). While glucose and fructose are more preferable by C. beijerinckii, sucrose can also be easily degraded by the microorganism. This study indicated that RSM is a useful approach for optimizing operational conditions for butanol production, and demonstrated that tropical maize, with high yield of biomass and stalk sugars, is a promising biofuel crop.     

Modeling and optimization of poly(3hydroxybutyrate-co-3hydroxyvalerate) production from cane molasses by Azohydromonas lata MTCC 2311 in a stirred-tank reactor: effect of agitation and aeration regimes

The effects of agitation and aeration rates on copolymer poly(3-hydroxybutyrate-co-3-hydroxyvalerate) [P(3HB-co-3HV)] production by Azohydromonas lata MTCC 2311 using cane molasses supplemented with propionic acid in a bioreactor were investigated. The experiments were conducted in a three-level factorial design by varying the impeller (150-500 rev min(-1)) and aeration (0.5-1.5 vvm) rates. Further, the data were fitted to mathematical models [quadratic polynomial equation and artificial neural network (ANN)] and process variables were optimized by genetic algorithm-coupled models. ANN and hybrid ANN-GA were found superior for modeling and optimization of process variables, respectively. The maximum copolymer concentration of 7.45 g l(-1) with 21.50 mol% of 3HV was predicted at process variables: agitation speed, 287 rev min(-1); and aeration rate, 0.85 vvm, which upon validation gave 7.20 g l(-1) of P(3HB-co-3HV) with 21 mol% of 3HV with the prediction error (%) of 3.38 and 2.32, respectively. Agitation speed established a relative high importance of 72.19% than of aeration rate (27.80%) for copolymer accumulation. The volumetric gas-liquid mass transfer coefficient (k (L) a) was strongly affected by agitation and aeration rates. The highest P(3HB-co-3HV) productivity of 0.163 g l(-1) h(-1) was achieved at 0.17 s(-1) of k (L) a value. During the early phase of copolymer production process, 3HB monomers were accumulated, which were shifted to 3HV units (9-21%) during the cultivation period of 24-42 h. The enhancement of 7.5 and 34% were reported for P(3HB-co-3HV) production and 3HV content, respectively, by hybrid ANN-GA paradigm, which revealed the significant utilization of cane molasses for improved copolymer production.     

Application of artificial neural network coupling particle swarm optimization algorithm to biocatalytic production of GABA

The biotransformation of L-sodium glutamate (L-MSG) to gamma-aminobutyric acid (GABA) catalyzed by the cells of Lactobacillus brevis with higher glutamate decarboxylase activity was investigated. The results showed that pH, temperature, and FeSO(4) x 7H(2)O concentration had significantly positive effect on GABA yield. The individual and interactive effects of pH, temperature, and FeSO(4) x 7H(2)O concentration were further optimized in terms of GABA yield. In the present work, an artificial neural network (ANN) and response surface methodology (RSM) models were developed, which incorporated pH, temperature, and FeSO(4) x 7H(2)O concentration as input variables, and GABA yield as output variable. The optimized ANN topology included four neurons in the hidden layer and the best network architecture was 3-4-1. The trained ANN gave total root-mean square error (sigma) equal to 1.84 for GABA yield while the RSM gave sigma equal to 2.63. The results demonstrated a slightly higher prediction accuracy of ANN compared to RSM. The modeled maximum GABA yield was identified by applying particle swarm optimization algorithm to the ANN model developed. The modeled maximum GABA yield reached 91 mM under the following optimal conditions: 25 mL Na(2)HPO(4)-citric acid buffer (100 mM, pH 4.23), 120 mM L-MSG, 0.83 g/L FeSO(4) x 7H(2)O, 10 microM PLP, the resting cells obtained from a 60-h culture broth, 2.68 g dry cell weight (DCW)/L, and without agitation at 40 degrees C for 5 h. The previous high value of GABA yield that was observed was 81.8 mM. The optimized conditions allowed GABA yield to be increased from 81.8 to 90.57 mM after verification experiments test.     

基于人工神经网络-遗传算法的樟芝发酵培养基优化

采用优化模型对药用丝状真菌樟芝的复杂发酵过程进行建模,并获得最优发酵培养基组成.对樟芝发酵过程中的形态变化过程进行了观察,并分别采用人工神经网络(ANN)和响应面法(RSM)对樟芝发酵过程进行建模,同时采用遗传算法(GA)优化了发酵培养基组成.结果表明,ANN模型比RSM模型具有更好的实验数据拟合能力和预测能力,GA计算得到樟芝生物量理论最大值为6.2 g/L,并获得发酵最佳接种量及培养基组成:孢子浓度1.76× 105个/mL,葡萄糖29.1 g/L,蛋白胨9.4 g/L,黄豆粉2.8 g/L.在最佳培养条件下,樟芝生物量为(6.1±0.2)g/L.基于ANN-GA的优化方法可用于优化其他丝状真菌的复杂发酵过程,从而获得生物量或活性代谢产物.     

Optimization of culture conditions and bench-scale production of L-asparaginase by submerged fermentation of Aspergillus terreus MTCC 1782

Optimization of culture conditions for L-asparaginase production by submerged fermentation of Aspergillus terreus MTCC 1782 was studied using a 3-level central composite design of response surface methodology and artificial neural network linked genetic algorithm. The artificial neural network linked genetic algorithm was found to be more efficient than response surface methodology. The experimental L-asparaginase activity of 43.29 IU/ml was obtained at the optimum culture conditions of temperature 35 degrees C, initial pH 6.3, inoculum size 1% (v/v), agitation rate 140 rpm, and incubation time 58.5 h of the artificial neural network linked genetic algorithm, which was close to the predicted activity of 44.38 IU/ml. Characteristics of L-asparaginase production by A. terreus MTCC 1782 were studied in a 3 L bench-scale bioreactor.     

Rational optimization of culture conditions for the most efficient ethanol production in Scheffersomyces stipitis using design of experiments

Optimization of culture parameters for achieving the most efficient ethanol fermentation is challenging due to multiple variables involved. Here we presented a rationalized methodology for multi‐variables optimization through the design of experiments DoE approach. Three critical parameters, pH, temperature, and agitation speed, affecting ethanol fermentation in S. stipitis was investigated. A predictive model showed that agitation speed significantly affected ethanol synthesis. Reducing pH and temperature also improved ethanol production. The model identified the optimum culture conditions for the most efficient ethanol production with the yield and productivity of 0.46 g/g and 0.28 g/l h, respectively, which is consistent with experimental observation. The results also indicated the scalability of the model from shake flask to bioreactor. Thus, DoE is a promising tool permitting the rapid establishment of culture conditions for the most efficient ethanol fermentation in S. stipitis. The approach could be useful to reduce process development time in lignocellulosic ethanol industry. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Culture conditions optimization of hyaluronic acid production by Streptococcus zooepidemicus based on radial basis function neural network and quantum-behaved particle swarm optimization algorithm

This study aimed to optimize the culture conditions (agitation speed, aeration rate and stirrer number) of hyaluronic acid production by Streptococcus zooepidemicus. Two optimization algorithms were used for comparison: response surface methodology (RSM) and radial basis function neural network coupling quantum-behaved particle swarm optimization algorithm (RBF-QPSO). In RBF-QPSO approach, RBF is employed to model the microbial HA production and QPSO algorithm is used to find the optimal culture conditions with the established RBF estimator as the objective function. The predicted maximum HA yield by RSM and RBF-QPSO was 5.27 and 5.62 g/l, respectively, while a maximum HA yield of 5.21 and 5.58 g/l was achieved in the validation experiments under the optimal culture conditions obtained by RSM and RBF-QPSO, respectively. It was indicated that both models provided similar quality predictions for the above three independent variables in terms of HA yield, but RBF model gives a slightly better fit to the measured data compared to RSM model. This work shows that the combination of RBF neural network with QPSO algorithm has good predictability and accuracy for bioprocess optimization and may be helpful to the other industrial bioprocesses optimization to improve productivity.     

Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO₄, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.     

An integrated approach to optimization of Escherichia coli fermentations using historical data

Using a fermentation database for Escherichia coli producing green fluorescent protein (GFP), we have implemented a novel three-step optimization method to identify the process input variables most important in modeling the fermentation, as well as the values of those critical input variables that result in an increase in the desired output. In the first step of this algorithm, we use either decision-tree analysis (DTA) or information theoretic subset selection (ITSS) as a database mining technique to identify which process input variables best classify each of the process outputs (maximum cell concentration, maximum product concentration, and productivity) monitored in the experimental fermentations. The second step of the optimization method is to train an artificial neural network (ANN) model of the process input-output data, using the critical inputs identified in the first step. Finally, a hybrid genetic algorithm (hybrid GA), which includes both gradient and stochastic search methods, is used to identify the maximum output modeled by the ANN and the values of the input conditions that result in that maximum. The results of the database mining techniques are compared, both in terms of the inputs selected and the subsequent ANN performance. For the E. coli process used in this study, we identified 6 inputs from the original 13 that resulted in an ANN that best modeled the GFP fluorescence outputs of an independent test set. Values of the six inputs that resulted in a modeled maximum fluorescence were identified by applying a hybrid GA to the ANN model developed. When these conditions were tested in laboratory fermentors, an actual maximum fluorescence of 2.16E6 AU was obtained. The previous high value of fluorescence that was observed was 1.51E6 AU. Thus, this input condition set that was suggested by implementing the proposed optimization scheme on the available historical database increased the maximum fluorescence by 55%.     

Biodegradation of 4-chlorophenol by <Emphasis Type="Italic">Arthrobacter chlorophenolicus A6</Emphasis>: effect of culture conditions and degradation kinetics

Among known microbial species, Arthrobacter chlorophenolicus A6 has shown very good potential to treat phenolic wastewaters. In this study, the levels of various culture conditions, namely initial pH, agitation (rpm), temperature (°C), and inoculum age (h) were optimized to enhance 4-chlorophenol (4-CP) biodegradation and the culture specific growth rate. For optimization, central composite design of experiments followed by response surface methodology (RSM) was applied. Results showed that among the four independent variables, i.e., pH, agitation (rpm), temperature (°C), and inoculum age (h) investigated in this study, interaction effect between agitation and inoculum age as well as that between agitation and temperature were significant on both 4-CP biodegradation efficiency and culture specific growth rate. Also, at the RSM optimized settings of 7.5 pH, 207 rpm, 29.6°C and 39.5 h inoculum age, 100% biodegradation of 4-CP at a high initial concentration of 300 mg l⁻¹ was achieved within a short span of 18.5 h of culture. The enhancement in the 4-CP biodegradation efficiency was found to be 23% higher than that obtained at the unoptimized settings of the culture conditions. Results of batch growth kinetics of A. chlorophenolicus A6 for various 4-CP initial concentrations revealed that the culture followed substrate inhibition kinetics. Biokinetic constants involved in the process were estimated by fitting the experimental data to several models available from the literature.     

Studies on process optimization methods for rapamycin production using Streptomyces hygroscopicus ATCC 29253

Rapamycin is a high-value product finding immense use as a drug, in organ transplantation, and as a potential immunosuppressant. Optimization of fermentation parameters of rapamycin production by Streptomyces hygroscopicus NRRL 5491 has been carried out. The low titer value of rapamycin in the original producer strain limits its applicability at industrial level. This study aims at improving the production of rapamycin by optimizing the nutrient requirements. Addition of l-lysine increased the production of rapamycin up to a significant level which supports the fact that it acts as precursor for rapamycin production, as found in previous studies. Effect of optimized medium on the Streptomyces growth rate as well as rapamycin production has been studied. The optimization study incorporates one at a time parameter optimization studies followed by tool-based hybrid methodology. This methodology includes the Plackett–Burman design (PBD) method, artificial neural networks (ANN), and genetic algorithms (GA). PBD screened mannose, soyabean meal, and l-lysine concentrations as significant factors for rapamycin production. ANN was used to construct rapamycin production model. This strategy has led to a significant increase of rapamycin production up to 320.89 mg/L at GA optimized concentrations of 25.47, 15.39, and 17.48 g/L for mannose, soyabean meal, and l-lysine, respectively. The present study must find its application in scale-up study for industrial level production of rapamycin.     

Optimisation of the operational conditions of trichloroethylene degradation using Trametes versicolor under quinone redox cycling conditions using central composite design methodology

Extracellular radicals produced by Trametes versicolor under quinone redox cycling conditions can degrade a large variety of pollutant compounds, including trichloroethylene (TCE). This study investigated the effect of the agitation speed and the gas–liquid phase volume ratio on TCE degradation using central composite design (CCD) methodology for a future scale-up to a reactor system. The agitation speed ranged from 90 to 200 rpm, and the volume ratio ranged from 0.5 to 4.4. The results demonstrated the important and positive effect of the agitation speed and an interaction between the two factors on TCE degradation. Although the volume ratio did not have a significant effect if the agitation speed value was between 160 and 200 rpm, at lower speed values, the specific pollutant degradation was clearly more extensive at low volume ratios than at high volume ratios. The fitted response surface was validated by performing an experiment using the parameter combination in the model that maximised TCE degradation. The results of the experiments carried out using different biomass concentrations demonstrated that the biomass concentration had a positive effect on pollutant degradation if the amount of biomass present was lower than 1.6 g dry weight l⁻¹. The results show that the maximum TCE degradation was obtained at the highest speed (200 rpm), gas–liquid phase volume ratio (4.4), and a biomass concentration of 1.6 g dry weight l⁻¹.     

Maximizing the native concentration and shelf life of protein: a multiobjective optimization to reduce aggregation

A multiobjective optimization was performed to maximize native protein concentration and shelf life of ASD, using artificial neural network (ANN) and genetic algorithm (GA). Optimum pH, storage temperature, concentration of protein, and protein stabilizers (Glycerol, NaCl) were determined satisfying the twin objective: maximum relative area of the dimer peak (native state) after 48 h of storage, and maximum shelf life. The relative area of the dimer peak, obtained from size exclusion chromatography performed as per the central composite design (CCD), and shelf life (obtained as turbidity change) served as training targets for the ANN. The ANN was used to establish mathematical relationship between the inputs and targets (from CCD). GA was then used to optimize the above determinants of aggregation, maximizing the twin objectives of the network. An almost fourfold increase in shelf life (~196 h) was observed at the GA-predicted optimum (protein concentration: 6.49 mg/ml, storage temperature: 20.8 °C, Glycerol: 10.02%, NaCl: 51.65 mM and pH: 8.2). Since no aggregation was observed at the optimum till 48 h, all the protein was found at the dimer position with maximum relative area (64.49). Predictions of the finally adapted network also reveal that storage temperature and solvent glycerol concentration plays key role in deciding the degree of ASD aggregation. This multiobjective optimization strategy was also successfully applied in minimizing the batch culture period and determining optimum combination of medium components required for most economical production of actinomycin D.     

Modeling and optimization of microbial hyaluronic acid production by Streptococcus zooepidemicus using radial basis function neural network coupling quantum‐behaved particle swarm optimization algorithm

Hyaluronic acid (HA) is a natural biopolymer with unique physiochemical and biological properties and finds a wide range of applications in biomedical and cosmetic fields. It is important to increase HA production to meet the increasing HA market demand. This work is aimed to model and optimize the amino acids addition to enhance HA production of Streptococcus zooepidemicus with radial basis function (RBF) neural network coupling quantum‐behaved particle swarm optimization (QPSO) algorithm. In the RBF‐QPSO approach, RBF neural network is used as a bioprocess modeling tool and QPSO algorithm is applied to conduct the optimization with the established RBF neural network black model as the objective function. The predicted maximum HA yield was 6.92 g/L under the following conditions: arginine 0.062 g/L, cysteine 0.036 g/L, and lysine 0.043 g/L. The optimal amino acids addition allowed HA yield increased from 5.0 g/L of the control to 6.7 g/L in the validation experiments. Moreover, the modeling and optimization capacity of the RBF‐QPSO approach was compared with that of response surface methodology (RSM). It was indicated that the RBF‐QPSO approach gave a slightly better modeling and optimization result compared with RSM. The developed RBF‐QPSO approach in this work may be helpful for the modeling and optimization of the other multivariable, nonlinear, time‐variant bioprocesses. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Optimization of nutrients for the production of RNase by <Emphasis Type="Italic">Bacillus firmus</Emphasis> VKPACU1 using response surface methodology

Medium optimization for the nuclease (RNase) production by Bacillus firmus VKPACU-1 was studied using the one-factor-at-a-time method and Response Surface Methodology (RSM). One-factor-at-a-time methodology was used to study the effects of carbon, nitrogen, phosphorus sources, and physical conditions such as pH and temperature, on nuclease (RNase) production. After optimizing the carbon (glucose) and nitrogen (tryptone) sources in the culture medium the physical conditions, pH (6.5) and temperature (35°C) were also optimized. Later these conditions were chosen as the main factors and used in the experimental design. The central composite design (CCD) of the RSM was employed to evaluate the interactive effects of these four variables. The optimized values obtained by the statistical analysis showed that glucose 5.95 g/L, tryptone 22.5 g/L, pH 6.5, and temperature 35°C affected maximum nuclease (RNase) production. When utilizing these proposed optimized conditions, the model predicted nuclease (RNase) production of 43.6 U/mL and in the validation experiments, the nuclease production obtained was 46.5 U/mL. The nuclease production in medium optimized by RSM was 26% higher, than in the non-optimized medium.