Artificial intelligence based optimization of exocellular glucansucrase production from Leuconostoc dextranicum NRRL B-1146

Two different artificial intelligence techniques namely artificial neural network (ANN) and genetic algorithm (GA) were integrated for optimizing fermentation medium for the production of glucansucrase. The experimental data reported in a previous study were used to build the neural network. The ANN was trained using the back propagation algorithm. The ANN predicted values showed good agreement with the experimentally reported ones from a response surface based experiment. The concentrations of three medium components: viz Tween 80, sucrose and K(2)HPO(4) served as inputs to the neural network model and the enzyme activity as the output of the model. A model was generated with a coefficient of correlation (R(2)) of 1.0 for the training set and 0.90 for the test data. A genetic algorithm was used to optimize the input space of the neural network model to find the optimum settings for maximum enzyme activity. This artificial neural network supported genetic algorithm predicted a maximum glucansucrase activity of 6.92U/ml at medium composition of 0.54% (v/v) Tween 80, 5.98% (w/v) sucrose and 1.01% (w/v) K(2)HPO(4). ANN-GA predicted model gave a 6.0% increase of enzyme activity over the regression based prediction for optimized enzyme activity. The maximum enzyme activity experimentally obtained using the ANN-GA designed medium was 6.75+/-0.09U/ml which was in good agreement with the predicted value.     

Optimization of actinomycin V production by Streptomyces triostinicus using artificial neural network and genetic algorithm

Artificial neural network (ANN) and genetic algorithm (GA) were applied to optimize the medium components for the production of actinomycinV from a newly isolated strain of Streptomyces triostinicus which is not reported to produce this class of antibiotics. Experiments were conducted using the central composite design (CCD), and the data generated was used to build an artificial neural network model. The concentrations of five medium components (MgSO₄, NaCl, glucose, soybean meal and CaCO₃) served as inputs to the neural network model, and the antibiotic yield served as outputs of the model. Using the genetic algorithm, the input space of the neural network model was optimized to find out the optimum values for maximum antibiotic yield. Maximum antibiotic yield of 452.0 mg l⁻¹ was obtained at the GA-optimized concentrations of medium components (MgSO₄ 3.657; NaCl 1.9012; glucose 8.836; soybean meal 20.1976 and CaCO₃ 13.0842 gl⁻¹). The antibiotic yield obtained by the ANN/GA was 36.7% higher than the yield obtained with the response surface methodology (RSM).     

Optimization of auto-induction medium for G-CSF production by Escherichia coli using artificial neural networks coupled with genetic algorithm

Granulocyte colony-stimulating factor (G-CSF) is a cytokine widely used in cancer patients receiving high doses of chemotherapeutic drugs to prevent the chemotherapy-induced suppression of white blood cells. The production of recombinant G-CSF should be increased to meet the increasing market demand. This study aims to model and optimize the carbon source of auto-induction medium to enhance G-CSF production using artificial neural networks coupled with genetic algorithm. In this approach, artificial neural networks served as bioprocess modeling tools, and genetic algorithm (GA) was applied to optimize the established artificial neural network models. Two artificial neural network models were constructed: the back-propagation (BP) network and the radial basis function (RBF) network. The root mean square error, coefficient of determination, and standard error of prediction of the BP model were 0.0375, 0.959, and 8.49 %, respectively, whereas those of the RBF model were 0.0257, 0.980, and 5.82 %, respectively. These values indicated that the RBF model possessed higher fitness and prediction accuracy than the BP model. Under the optimized auto-induction medium, the predicted maximum G-CSF yield by the BP-GA approach was 71.66 %, whereas that by the RBF-GA approach was 75.17 %. These predicted values are in agreement with the experimental results, with 72.4 and 76.014 % for the BP-GA and RBF-GA models, respectively. These results suggest that RBF-GA is superior to BP-GA. The developed approach in this study may be helpful in modeling and optimizing other multivariable, non-linear, and time-variant bioprocesses.     

Production of hydantoinase fromPseudomonas fluorescens strain DSM 84

Summary Pseudomonas fluorescens strain DSM 84 was selected as a good hydantoinase (dihydropyrimidinase E.C. 3.5.2.2.) producer from a screening involving 60 collection strains. Optimization of the culture and growth conditions were performed in order to increase the enzyme production. A mineral medium supplemented with 10 g/l of yeast extract having an initial pH of 7.1±0.1 but containing no additional carbon source or inducer was devised. The strain DSM 84 was found to produce the maximal level of hydantoinase in the defined mineral medium within 15 h of incubation at 27°C. When using 5-isopropylhydantoin as substrate, N-carbamyl-valine was detected as the end product of the crude hydantoinase. Conditions leading to the isolation and conservation of a crude hydantoinase as well as its temperature and pH stability are described.     

Modelling and optimization of fermentation factors for enhancement of alkaline protease production by isolated Bacillus circulans using feed-forward neural network and genetic algorithm

Aim: Modelling and optimization of fermentation factors and evaluation for enhanced alkaline protease production by Bacillus circulans. Methods and Results: A hybrid system of feed‐forward neural network (FFNN) and genetic algorithm (GA) was used to optimize the fermentation conditions to enhance the alkaline protease production by B. circulans. Different microbial metabolism regulating fermentation factors (incubation temperature, medium pH, inoculum level, medium volume, carbon and nitrogen sources) were used to construct a ‘6‐13‐1’ topology of the FFNN for identifying the nonlinear relationship between fermentation factors and enzyme yield. FFNN predicted values were further optimized for alkaline protease production using GA. The overall mean absolute predictive error and the mean square errors were observed to be 0·0048, 27·9, 0·001128 and 22·45 U ml^?1 for training and testing, respectively. The goodness of the neural network prediction (coefficient of R²) was found to be 0·9993. Conclusions: Four different optimum fermentation conditions revealed maximum enzyme production out of 500 simulated data. Concentration‐dependent carbon and nitrogen sources, showed major impact on bacterial metabolism mediated alkaline protease production. Improved enzyme yield could be achieved by this microbial strain in wide nutrient concentration range and each selected factor concentration depends on rest of the factors concentration. The usage of FFNN–GA hybrid methodology has resulted in a significant improvement (>2·5‐fold) in the alkaline protease yield. Significance and Impact of the Study: The present study helps to optimize enzyme production and its regulation pattern by combinatorial influence of different fermentation factors. Further, the information obtained in this study signifies its importance during scale‐up studies.     

L-海因酶与D-海因酶的序列及结构比较研究

运用生物信息学的研究方法,从序列及结构上对L型及D型海因酶进行了初步的比较。研究了两种类型的海因酶在序列、骨架结构及活性中心的区别,并探讨了产生这些差异的理论基础,为海因酶进一步的理论及应用研究提供一定的指导。     

L-asparaginase production by <Emphasis Type="Italic">Streptomyces albidoflavus</Emphasis>

Attempts were made to optimize the cultural conditions for the production of L-asparaginase by Streptomyces albidoflavus under submerged fermentations. Enhanced level of L-asparaginase was found in culture medium supplemented with maltose as carbon source. Yeast extract (2%) was served as good nitrogen source for the production of L-asparaginase. The optimum pH for enzyme production was 7.5 and temperature was 35°C. The release of L-asparaginase from the cells of S. albidoflavus was high when strain was treated with cell disrupting agents like EDTA and lysozyme. The enzyme produced by the strain was purifi ed by ammonium sulfate, Sephadex G-100 and CM-Sephadex C-50 gel fi ltration and the molecular weight was apparently determined as 112 kDa.     

L-缬氨酸发酵培养基的神经网络建模与遗传算法优化

运用神经网络对L-缬氨酸发酵培养基组成进行建模,在神经网络模型的基础上采用遗传算法对培养基组成进行优化,得到最佳发酵培养基组成.结果表明,运用神经网络并结合遗传算法是一种行之有效的优化方法.按最佳发酵培养基组成进行发酵实验64h,可在发酵液中积累L-缬氨酸28.5g/L.     

Machine learning-based model predictive controller design for cell culture processes

The biopharmaceutical industry continuously seeks to optimize the critical quality attributes to maintain the reliability and cost-effectiveness of its products. Such optimization demands a scalable and optimal control strategy to meet the process constraints and objectives. This work uses a model predictive controller (MPC) to compute an optimal feeding strategy leading to maximized cell growth and metabolite production in fed-batch cell culture processes. The lack of high-fidelity physics-based models and the high complexity of cell culture processes motivated us to use machine learning algorithms in the forecast model to aid our development. We took advantage of linear regression, the Gaussian process and neural network models in the MPC design to maximize the daily protein production for each batch. The control scheme of the cell culture process solves an optimization problem while maintaining all metabolites and cell culture process variables within the specification. The linear and nonlinear models are developed based on real cell culture process data, and the performance of the designed controllers is evaluated by running several real-time experiments.     

Modeling of polygalacturonase enzyme activity and biomass production by <Emphasis Type="Italic">Aspergillus sojae</Emphasis> ATCC 20235

Aspergillus sojae, which is used in the making of koji, a characteristic Japanese food, is a potential candidate for the production of polygalacturonase (PG) enzyme, which of a major industrial significance. In this study, fermentation data of an A. sojae system were modeled by multiple linear regression (MLR) and artificial neural network (ANN) approaches to estimate PG activity and biomass. Nutrient concentrations, agitation speed, inoculum ratio and final pH of the fermentation medium were used as the inputs of the system. In addition to nutrient conditions, the final pH of the fermentation medium was also shown to be an effective parameter in the estimation of biomass concentration. The ANN parameters, such as number of hidden neurons, epochs and learning rate, were determined using a statistical approach. In the determination of network architecture, a cross-validation technique was used to test the ANN models. Goodness-of-fit of the regression and ANN models was measured by the R ² of cross-validated data and squared error of prediction. The PG activity and biomass were modeled with a 5-2-1 and 5-9-1 network topology, respectively. The models predicted enzyme activity with an R ² of 0.84 and biomass with an R ² value of 0.83, whereas the regression models predicted enzyme activity with an R ² of 0.84 and biomass with an R ² of 0.69.     

Mathematical modeling and optimization of cellulase protein production using Trichoderma reesei RL-P37

The enzyme cellulase, a multienzyme complex made up of several proteins, catalyzes the conversion of cellulose to glucose in an enzymatic hydrolysis-based biomass-to-ethanol process. Production of cellulase enzyme proteins in large quantities using the fungus Trichoderma reesei requires understanding the dynamics of growth and enzyme production. The method of neural network parameter function modeling, which combines the approximation capabilities of neural networks with fundamental process knowledge, is utilized to develop a mathematical model of this dynamic system. In addition, kinetic models are also developed. Laboratory data from bench-scale fermentations involving growth and protein production by T. reesei on lactose and xylose are used to estimate the parameters in these models. The relative performances of the various models and the results of optimizing these models on two different performance measures are presented. An approximately 33% lower root-mean-squared error (RMSE) in protein predictions and about 40% lower total RMSE is obtained with the neural network-based model as opposed to kinetic models. Using the neural network-based model, the RMSE in predicting optimal conditions for two performance indices, is about 67% and 40% lower, respectively, when compared with the kinetic models. Thus, both model predictions and optimization results from the neural network-based model are found to be closer to the experimental data than the kinetic models developed in this work. It is shown that the neural network parameter function modeling method can be useful as a "macromodeling" technique to rapidly develop dynamic models of a process.     

Effect of metal binding and posttranslational lysine carboxylation on the activity of recombinant hydantoinase

Bacterial hydantoinase possesses a binuclear metal center in which two metal ions are bridged by a posttranslationally carboxylated lysine. How the carboxylated lysine and metal binding affect the activity of hydantoinase was investigated. A significant amount of iron was always found in Agrobacterium radiobacter hydantoinase purified from unsupplemented cobalt-, manganese-, or zinc-amended Escherichia coli cell cultures. A titration curve for the reactivation of apohydantoinase with cobalt indicates that the first metal was preferentially bound but did not give any enzyme activity until the second metal was also attached to the hydantoinase. The pH profiles of the metal-reconstituted hydantoinase were dependent on the specific metal ion bound to the active site, indicating a direct involvement of metal in catalysis. Mutation of the metal binding site residues, H57A, H59A, K148A, H181A, H237A, and D313A, completely abolished hydantoinase activity but preserved about half of the metal content, except for K148A, which lost both metals in its active site. However, the activity of K148A could be chemically rescued by short-chain carboxylic acids in the presence of cobalt, indicating that the carboxylated lysine was needed to coordinate the binuclear ion within the active site of hydantoinase. The mutant D313E enzyme was also active but resulted in a pH profile different from that of wild-type hydantoinase. A mechanism for hydantoinase involving metal, carboxylated K148, and D313 was proposed. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Mathematical modeling of Kluyveromyces marxianus growth in solid-state fermentation using a packed-bed bioreactor

This work investigated the growth of Kluyveromyces marxianus NRRL Y-7571 in solid-state fermentation in a medium composed of sugarcane bagasse, molasses, corn steep liquor and soybean meal within a packed-bed bioreactor. Seven experimental runs were carried out to evaluate the effects of flow rate and inlet air temperature on the following microbial rates: cell mass production, total reducing sugar and oxygen consumption, carbon dioxide and ethanol production, metabolic heat and water generation. A mathematical model based on an artificial neural network was developed to predict the above-mentioned microbial rates as a function of the fermentation time, initial total reducing sugar concentration, inlet and outlet air temperatures. The results showed that the microbial rates were temperature dependent for the range 27–50°C. The proposed model efficiently predicted the microbial rates, indicating that the neural network approach could be used to simulate the microbial growth in SSF.     

Substrate- and stereospecificity,induction and metallo-dependence of a microbial hydantoinase

Summary D, L-5-monosubstituted hydantoins can be used as substrates for a two-step-enzymatic production of optically active aminoacids. The substrate- and stereospecificity of the first enzyme — a hydantoinase -, investigations on its induction and on its dependence upon metallo-ions are described. It is shown, that the activity of this hydantoinase, which is not identical with the well-known enzyme D-hydantoinase, depends on manganese-ions. Of synthetic and natural compounds tested as inductors, D, L-5-indolylmethylhydantoin showed the best effect. The hydantoinase has a wide substrate-specificity. Its stereoselectivity seems to depend on the structure of the side chain in 5-position of the hydantoin.     

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.     

Conditions used with a continuous cultivation system to screen for d-hydantoinase-producing microorganisms

The continuous cultivation technique has been used to screen for microorganisms producing d-hydantoinase, a biocatalyst involved in the production of optically active amino acids. Pseudomonas putida strain DSM 84 was used as a model hydantoinase producer to establish selective culture conditions through the addition of various pyrimidines, dihydropyrimidines, hydantoins and 5-monosubstituted hydantoins. Thymine induced more activity than all cyclic amides tested. Addition of thymine as a non-metabolised inducer at a concentration of 0.05 g l^–1 in a continuous culture of P. putida stimulated hydantoinase production up to 80 times the basal level. Using continuous culture conditions established with the model strain, a different strain of P. putida having hydantoinase activity was isolated from commercial mixed cultures of microorganisms. DNA fingerprinting revealed that this new isolate was distinct from strain DSM 84. When used as a probe, the d-hydantoinase gene of strain DSM 84 hybridized with the DNA of the new P. putida isolate.     

Over-production of hydantoinase and N-carbamoylamino acid amidohydrolase enzymes by regulatory mutants of Agrobacterium tumefaciens

While the hydantoin-hydrolysing enzymes from Agrobacterium strains are used as biocatalysts in the commercial production of D-p-hydroxyphenylglycine, they are now mostly produced in heterologous hosts such as Escherichia coli. This is due to the fact that the activity of these enzymes in the native strains is tightly regulated by growth conditions. Hydantoinase and N-carbamoylamino acid amidohydrolase (NCAAH) activities are induced when cells are grown in the presence of hydantoin or an hydantoin analogue, and in complete medium, enzyme activity can be detected only in early stationary growth phase. In this study, the ability of Agrobacterium tumefaciens RU-OR cells to produce active enzymes was found to be dependent upon the choice of nitrogen source and the presence of inducer, 2-thiouracil, in the growth medium. Growth with (NH4)2SO4 as the nitrogen source repressed the production of both enzymes (nitrogen repression) and also resulted in a rapid, but reversible loss of hydantoinase activity in induced cells (ammonia shock). Mutant strains with inducer-independent production of the enzymes and/or altered response to nitrogen control were isolated. Of greatest importance for industrial application was strain RU-ORPN1F9, in which hydantoinase and NCAAH enzyme activity was inducer-independent and no longer sensitive to nitrogen repression or ammonia shock. Such mutants offer the potential for native enzyme production levels equivalent to those achieved by current heterologous expression systems.     

Modeling of the pyruvate production with Escherichia coli: comparison of mechanistic and neural networks-based models

Three different models: the unstructured mechanistic black-box model, the input–output neural network-based model and the externally recurrent neural network model were used to describe the pyruvate production process from glucose and acetate using the genetically modified Escherichia coli YYC202 ldhA::Kan strain. The experimental data were used from the recently described batch and fed-batch experiments [ Zelić B, Study of the process development for Escherichia coli-based pyruvate production. PhD Thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, Zagreb, Croatia, July 2003. (In English); Zelić et al. Bioproc Biosyst Eng 26:249–258 (2004); Zelić et al. Eng Life Sci 3:299–305 (2003); Zelić et al Biotechnol Bioeng 85:638–646 (2004)]. The neural networks were built out of the experimental data obtained in the fed-batch pyruvate production experiments with the constant glucose feed rate. The model validation was performed using the experimental results obtained from the batch and fed-batch pyruvate production experiments with the constant acetate feed rate. Dynamics of the substrate and product concentration changes was estimated using two neural network-based models for biomass and pyruvate. It was shown that neural networks could be used for the modeling of complex microbial fermentation processes, even in conditions in which mechanistic unstructured models cannot be applied.     

Efficient production of the industrial biocatalysts hydantoinase and N-carbamyl amino acid amidohydrolase: Novel non-metabolizable inducers

Abstract The biosynthesis of the hydantoin-hydrolysing enzymes hydantoinase and N -carbamyl amino acid amidohydrolase from Agrobacterium sp. IP I-671, a Gram-negative bacterium used as a biocatalyst for the production of enantiomerically pure ( R ) amino acids, was found to be highly inducible by the addition to the cultivation medium of different non-metabolizable thiolated hydantoins or pyrimidines. Among these inducers the hexacyclic pyrimidine thioderivatives were more potent than all the pentacyclic thiohydantoin compounds. Addition of 2,4-thiouracil to the cultures, at a rate of 0.1 g (g cell dry mass)⁻¹, led to no appreciable growth inhibition and yielded a biocatalyst exhibiting a 40-fold higher hydantoinase and a 15-fold higher N -carbamyl amino acid amidohydrolase activity than the corresponding inducer-free cultures.