Optimization of the Production of Inactivated Clostridium novyi Type B Vaccine Using Computational Intelligence Techniques.

Clostridium novyi causes necrotic hepatitis in sheep and cattle, as well as gas gangrene. The microorganism is strictly anaerobic, fastidious, and difficult to cultivate in industrial scale. C. novyi type B produces alpha and beta toxins, with the alpha toxin being linked to the presence of specific bacteriophages. The main strategy to combat diseases caused by C. novyi is vaccination, employing vaccines produced with toxoids or with toxoids and bacterins. In order to identify culture medium components and concentrations that maximized cell density and alpha toxin production, a neuro-fuzzy algorithm was applied to predict the yields of the fermentation process for production of C. novyi type B, within a global search procedure using the simulated annealing technique. Maximizing cell density and toxin production is a multi-objective optimization problem and could be treated by a Pareto approach. Nevertheless, the approach chosen here was a step-by-step one. The optimum values obtained with this approach were validated in laboratory scale, and the results were used to reload the data matrix for re-parameterization of the neuro-fuzzy model, which was implemented for a final optimization step with regards to the alpha toxin productivity. With this methodology, a threefold increase of alpha toxin could be achieved.

On-line prediction of the feeding phase in high-cell density cultivation of rE. coli using constructive neural networks.

Streptococcus pneumoniae (pneumococcus) is a bacterium responsible for a wide spectrum of illnesses. The surface of the bacterium consists of three distinctive membranes: plasmatic, cellular and the polysaccharide (PS) capsule. PS capsules may mediate several biological processes, particularly invasive infections of human beings. Prevention against pneumococcal related illnesses can be provided by vaccines. There is a sound investment worldwide in the investigation of a proteic antigen as a possible alternative to pneumococcal vaccines based exclusively on PS. A few proteins which are part of the membrane of the pneumococcus seem to have antigen potential to be part of a vaccine, particularly the PspA. A vital aspect in the production of the intended conjugate pneumococcal vaccine is the efficient production (in industrial scale) of both, the chosen PS serotypes as well as the PspA protein. Growing recombinant Escherichia coli (rE. coli) in high-cell density cultures (HCDC) under a fed-batch regime requires a refined continuous control over various process variables where the on-line prediction of the feeding phase is of particular relevance and one of the focuses of this paper. The viability of an on-line monitoring software system, based on constructive neural networks (CoNN), for automatically detecting the time to start the fed-phase of a HCDC of rE. coli that contains a plasmid used for PspA expression is investigated. The paper describes the data and methodology used for training five different types of CoNNs, four of them suitable for classification tasks and one suitable for regression tasks, aiming at comparatively investigate both approaches. Results of software simulations implementing five CoNN algorithms as well as conventional neural networks (FFNN), decision trees (DT) and support vector machines (SVM) are also presented and discussed. A modified CasCor algorithm, implementing a data softening process, has shown to be an efficient candidate to be part of an on-line HCDC monitoring system for detecting the feeding phase of the HCDC process.

An innovative biocatalyst for production of ethanol from xylose in a continuous bioreactor.

The use of the hemicellulose fraction of biomass may be important for the feasibility of the production of second generation bioethanol. Wild strains of Saccharomyces cerevisiae are widely used in industry for production of 1st generation ethanol, and the robustness of this yeast is an important advantage in large scale applications. Isomerization of xylose to xylulose is an essential step in this process. This reaction is catalyzed by glucose isomerase (GI). A new biocatalyst is presented here for the simultaneous isomerization and fermentation (SIF) of xylose. GI from Streptomyces rubiginosus was immobilized in chitosan, through crosslinking with glutaraldehyde, and the support containing the immobilized GI (IGI-Ch) was co-immobilized with S. cerevisiae, in calcium alginate gel. The immobilization experiments led to high immobilized protein loads (30-68 mg × g(support)(-1)), high yields (circa of 100%) and high recovered enzyme activity (>90%). The IGI-Ch derivative with maximum activity presented 1700 IU × g(catalyst)(-1), almost twice the activity of a commercial immobilized GI, GENSWEET(®) IGI-HF. At typical operational conditions for xylose SIF operation (pH 5, 30-35 °C, presence of nutrients and ethanol concentrations in the medium up to 70 L(-1)), both derivatives, IGI-Ch and GENSWEET(®) IGI-HF retained app. 90% of the initial activity after 120 h, while soluble GI was almost completely inactive at pH 5, 30 °C. The isomerization xylose/xylulose, catalyzed by IGI-Ch, reached the equilibrium in batch experiments after 4h, with 12,000 IU × L(-1) (7 g(der) × L(-1)), at pH 5 and 30 °C, in the presence of fermentation nutrients. After co-immobilization of IGI-Ch with yeast in alginate gel, this biocatalyst succeeded in producing 12 g × L(-1) of ethanol, 9.5 g × L(-1) of xylitol, 2.5 g × L(-1) of glycerol and 1.9 g × L(-1) of acetate after consumption of 50 g × L(-1) of xylose, in 48 h, using 32.5 × 10(3) IU × L(-1) and 20 g(yeast) × L(-1), at 35 °C and initial pH 5.3.

Influence of mass transfer limitations on the enzymatic synthesis of beta-lactam antibiotics catalyzed by penicillin G acylase immobilized on glioxil-agarose.

Mass transfer effects were investigated for the synthesis of ampicillin and amoxicillin, at pH 6.5 and 25 degrees C, catalyzed by penicillin G acylase immobilized on agarose. The influence of external mass transfer was analysed using different stirring rates, ranging form 200 to 800 rpm. Above 400 rpm, the film resistance may be neglected. Intra-particle diffusion limitation was investigated using biocatalysts prepared with different enzyme loads and agarose with different mean pore diameters. When agarose with 6, 8 and 10% of crosslinking were used, for the same enzyme load, substrates and products concentration profiles presented no expressive differences, suggesting pore diameter is not important parameter. An increase on enzyme load showed that when more than 90 IU of enzyme activity were used per mL of support, the system was influenced by intra-particle mass transfer. A reactive-diffusive model was used to estimate effective diffusivities of substrates and products.

Optimization of medium and cultivation conditions for capsular polysaccharide production by Streptococcus pneumoniae serotype 23F.

The influence of medium composition and culture conditions on Streptococcus pneumoniae serotype 23F cultivation was investigated in order to develop an industrial method for polysaccharide (PS) production. Acid-hydrolyzed casein (AHC) and dialyzed enzymatically hydrolyzed soybean meal (EHS) were investigated as nitrogen sources, and the vitamin solution of Hoeprich's medium and dialyzed yeast extract as vitamin sources. The influence of initial glucose concentration was also evaluated. In flask experiments, the best nitrogen source for PS production was AHC; EHS yielded small amounts of PS without interfering with bacterial growth. Dialyzed yeast extract provided an approximately 2-fold increase in PS production when compared to Hoeprich's vitamin solution. In a 5-l bioreactor, it was observed that the pneumococcus did not grow under aerobic conditions, CO(2) did not increase PS yield, glucose was inhibitory above 30 g l(-1), and the main glucose catabolism product was lactate, which had an inhibitory effect on cell growth. When anaerobic cultivation was performed under N(2) flow using the optimized medium, 240 mg l(-1) of soluble PS was obtained, which represents a 3-fold increase in yield as compared to that described in the published patent [Yavordios and Cousin (1983) European Patent 0 071515 A1]. Application of these results would considerably simplify upstream and downstream processes for PS production.

Optimization of medium and cultivation conditions for capsular polysaccharide production by Streptococcus pneumoniae serotype 23F

The influence of medium composition and culture conditions on Streptococcus pneumoniae serotype 23F cultivation was investigated in order to develop an industrial method for polysaccharide (PS) production. Acid-hydrolyzed casein (AHC) and dialyzed enzymatically hydrolyzed soybean meal (EHS) were investigated as nitrogen sources, and the vitamin solution of Hoeprich's medium and dialyzed yeast extract as vitamin sources. The influence of initial glucose concentration was also evaluated. In flask experiments, the best nitrogen source for PS production was AHC; EHS yielded small amounts of PS without interfering with bacterial growth. Dialyzed yeast extract provided an approximately 2-fold increase in PS production when compared to Hoeprich's vitamin solution. In a 5-l bioreactor, it was observed that the pneumococcus did not grow under aerobic conditions, CO2 did not increase PS yield, glucose was inhibitory above 30 g l-1, and the main glucose catabolism product was lactate, which had an inhibitory effect on cell growth. When anaerobic cultivation was performed under N2 flow using the optimized medium, 240 mg l-1 of soluble PS was obtained, which represents a 3-fold increase in yield as compared to that described in the published patent [Yavordios and Cousin (1983) European Patent 0 071515 A1]. Application of these results would considerably simplify upstream and downstream processes for PS production.

A hybrid neural network algorithm for on-line state inference that accounts for differences in inoculum of Cephalosporium acremonium in fed-batch fermentors.

One serious difficulty in modeling a fermentative process is the forecasting of the duration of the lag phase. The usual approach to model biochemical reactors relies on first-principles, unstructured mathematical models. These models are not able to take into account changes in the process response caused by different incubation times or by repeated fedbatches. To overcome this problem, we have proposed a hybrid neural network algorithm. Feedforward neural networks were used to estimate rates of cell growth, substrate consumption, and product formation from on-line measurements during cephalosporin C production. These rates were included in the mass balance equations to estimate key process variables: concentrations of cells, substrate, and product. Data from fed-batch fermentation runs in a stirred aerated bioreactor employing the microorganism Cephalosporium acremonium ATCC 48272 were used. On-line measurements strongly related to the mass and activity of the cells used. They include carbon dioxide and oxygen concentrations in the exhausted gas. Good results were obtained using this approach.

Modeling and simulation of cephalosporin C production in a fed-batch tower-type bioreactor.

Immobilized cell utilization in tower-type bioreactor is one of the main alternatives being studied to improve the industrial bioprocess. Other alternatives for the production of beta-lactam antibiotics, such as a cephalosporin C fed-batch process in an aerated stirred-tankbioreactor with free cells of Cephalosporium acremonium, or a tower-type bioreactor with immobilized cells of this fungus, have proven to be more efficient than the batch process. In the fed-batch process, it is possible to minimize the catabolite repression exerted by the rapidly utilization of carbon sources (such as glucose) in the synthesis of antibiotics by utilizing a suitable flow rate of supplementary medium. In this study, several runs for cephalosporin C production, each lasting 200 h, were conducted in a fed-batch tower-type bioreactor using different hydrolyzed sucrose concentrations. For this study's model, modifications were introduced to take into account the influence of supplementary medium flow rate. The balance equations considered the effect of oxygen limitation inside the bioparticles. In the Monod-type rate equations, cell concentrations, substrate concentrations, and dissolved oxygen were included as reactants affecting the bioreaction rate. The set of differential equations was solved by the numerical method, and the values of the parameters were estimated by the classic nonlinear regression method following Marquardt's procedure with a 95% confidence interval. The simulation results showed that the proposed model fit well with the experimental data, and based on the

Kinetic and mass transfer parameters of maltotriose hydrolysis catalyzed by glucoamylase immobilized on macroporous silica and wrapped in pectin gel.

Kinetic and mass transport parameters were estimated for maltotriose hydrolysis using glucoamylase immobilized on macroporous silica and wrapped in pectin gel at 30 degrees C. Free enzyme assays were used to obtain the intrinsic kinetic parameters of a Michaelis-Menten equation, with product inhibition by glucose. The uptake method, based on transient experimental data, was employed in the estimation of mass transfer parameters. Effective diffusivities of maltotriose in pectin gel were estimated by fitting a classical diffusion model to experimental data of maltotriose diffusion into particles of pectin gel in the absence of silica. The effective diffusivities of maltotriose in silica were obtained after fitting a bidisperse model to experimental data of maltotriose hydrolysis using glucoamylase immobilized in silica and wrapped in pectin gel.

Brazilian bioethanol program.

Brazil is the largest producer of bioethanol, and sugarcane is the main raw material. Bioethanol is produced by both batch and continuous processes, and in some cases, flocculating yeast is used. This article analyzes the Brazilian Ethanol Program. For the 1996-1997 harvest, Brazil produced 14.16 billion L of ethanol and 13.8 million metric t of sugar, from 286 million metric t of sugarcane. These products were produced by 328 industries in activity, with 101 autonomous ethanol plants producing only ethanol, and 227 sugar mills producing sugar and ethanol. The sugar-ethanol market reaches about 7.5 billion US$/yr, accounting for direct and indirect revenues.

Comparison between experimental and theoretical values of effectiveness factor in cephalosporin C production process with immobilized cells.

Cells of Cephalosporium acremonium ATCC 48272 immobilized in calcium alginate beads were utilized for cephalosporin C production and the results were compared with those obtained with free cells. The experiments were performed with synthetic medium containing glucose and sucrose as carbon and energy sources. Experimental effectiveness factor values were obtained at various cell and dissolved-oxygen concentrations, considering Monod kinetics for the respiration rate, and were compared with the values calculated with zero-order kinetics in spherical bioparticle. The results showed that the assumption of oxygen limitation by diffusion in the bioparticle was correct, and that cephalosporin C production with immobilized cells is perfectly viable, although a slightly lower rate than that obtained in the free cell process was observed.

Phenomenological and neural-network modeling of cephalosporin C production bioprocess.

Cephalosporin C production process with Cephalosporium acremonium ATCC 48272 in synthetic medium was investigated and the experimental results allowed the development of a mathematical model describing the process behavior. The model was able to explain fairly well the diauxic phenomenon, higher growth rate during the glucose-consumption phase, and the production occurring only in the sucrose-consumption phase. Moreover, the process was simulated utilizing the neural-networks technique. Two feed-forward neural-networks with one hidden layer were employed. Both models, phenomenological and neural-networks based, satisfactorily describe the bioprocess. The difficulties in determining kinetic parameters are avoided when neural networks are utilized.