Kinetic studies of recombinant human interferon-gamma expression in continuous cultures of <Emphasis Type="Italic">E. coli</Emphasis>

A series of continuous cultures was performed to understand the product formation kinetics of recombinant human interferon gamma (rhIFN-γ) in Escherichia coli at different dilution rates ranging from 0.1 to 0.3 h⁻¹ in different media. A T7 promoter-based vector was used for expression of IFN-γ in E. coli BL21 (DE3) cells. The recombinant protein was produced as inclusion bodies, thus allowing a rapid buildup of rhIFN-γ inside the cell, with the specific product yield (Y _p/X ) reaching a maximum value of 182 mg g⁻¹ dry cell weight (DCW). In all the media tested, the specific product formation rate (q _p ) was found to be strongly correlated with the specific growth rate (μ), demonstrating the growth-associated nature of product formation. The q _p values show no significant decline with time postinduction, even though the recombinant protein has been over produced inside the cell. The maximum q _p level of 75.5 mg g⁻¹ h⁻¹ was achieved at the first hour of induction at the dilution rate of 0.3 h⁻¹. Also, this correlation between q _p and μ was not critically dependent on media composition, which would made it possible to grow cells in defined media in the growth phase and then push up the specific growth rate just before induction by pulse addition of glucose and yeast extract. This would ensure the twin objectives of high biomass and high specific productivities, leading to high volumetric product concentration.     

Kinetic studies of constitutive human granulocyte-macrophage colony stimulating factor (hGM-CSF) expression in continuous culture of <Emphasis Type="Italic">Pichia pastoris</Emphasis>

Extracellular human granulocyte-macrophage colony stimulating factor (hGM-CSF) expression was studied under the control of the GAP promoter in recombinant Pichia pastoris in a series of continuous culture runs (dilution rates from 0.025 to 0.2 h⁻¹). The inlet feed concentration was also varied and the steady state biomass concentration increased proportionally demonstrating efficient substrate utilization and constancy of the biomass yield coefficient (Y_x/s) for a given dilution rate. The specific product formation rate (q_P) showed a strong correlation with dilution rates demonstrating growth associated product formation of hGM-CSF. The volumetric product concentration achieved at the highest feed concentration (4×) and a dilution rate of 0.2 h⁻¹ was 82 mg l⁻¹ which was 5-fold higher compared to the continuous culture run with 1× feed concentration at the lowest dilution rate thus translating to a 40 fold increase in the volumetric productivity. The specific product yield (Y_P/X) increased slightly from 2 to 2.5 mg g⁻¹, with increasing dilution rates, while it remained fairly invariant, for all feed concentrations demonstrating negligible product degradation or feed back inhibition. The robust nature of this expression system would make it easily amenable to scale up for industrial production.     

Observer design for differential-algebraic model of an aerobic culture of a recombinant yeast

The mathematical model of an aerobic culture of recombinant yeast presented in work by Zhang et al. (1997) is given by a differential-algebraic system. The classical nonlinear observer algorithms are generally based on ordinary differential equations. In this paper, first we extend the nonlinear observer synthesis to differential-algebraic dynamical systems. Next, we apply this observer theory to the mathematical model proposed in Zhang et al. (1997). More precisely, based on the total cell concentration and the recombinant protein concentration, the observer gives the online estimation of the glucose, the ethanol, the plasmid-bearing cell concentration and a parameter that represents the probability of plasmid loss of plasmid-bearing cells. Numerical simulations are given to show the good performances of the designed observer.Symbols C ₁ activity of pacing enzyme pool for glucose fermentation (dimensionless) - C ₂ activity of pacing enzyme pool for glucose oxidation (dimensionless) - C ₃ activity of pacing enzyme pool for ethanol oxidation (dimensionless) - E ethanol concentration (g/l) - G glucose concentration (g/l) - k _a regulation constant for (g glucose/g cell h^–1) - k _b regulation constant for (dimensionless) - k _c regulation constant for (g glucose/g cell h^–1) - k _d regulation constant for (dimensionless) - K _m1 saturation constant for glucose fermentation (g/l) - K _m2 saturation constant for glucose oxidation (g/l) - K _m3 saturation constant for ethanol oxidation (g/l) - L ( t) time lag function (dimensionless) - p probability of plasmid loss of plasmid-bearing cells (dimensionless) - P recombinant protein concentration (mg/g cell) - q _G total glucose flux culture time (g glucose/g cell h) - t culture time (h) - t _lag lag time (h) - X total cell concentration (g/l) - X ⁺ plasmid-bearing cell concentration (g/l) - Y ^F _{X / G} cell yield for glucose fermentation pathway (g cell/g glucose) - Y ^O _{X / G} cell yield for glucose oxidation pathway (g cell/g glucose) - Y _{X / E} cell yield for ethanol oxidation pathway (g cell/g ethanol) - Y _{E / X} ethanol yield for fermentation pathway based on cell mass (g ethanol·g cell) - ₂ glucoamylase yield for glucose oxidation (units/g cell) - ₃ glucoamylase yield for ethanol oxidation (units/g cell) - µ₁ specific growth rate for glucose fermentation (h^–1) - µ₂ specific growth rate for glucose oxidation (h^–1) - µ₃ specific growth rate for ethanol oxidation (h^–1) - µ_1max maximum specific growth rate for glucose fermentation (h^–1) - µ_2max maximum specific growth rate for glucose oxidation (h^–1) - µ_3max maximum specific growth rate for ethanol oxidation (h^–1)     

Nitrogen and Phosphorus Release from Decomposing Leaves under Sub‐Humid Tropical Conditions1

For many soils of the tropics, inputs of organic materials are essential to sustain soil fertility and crop production. Research in the quality of organic inputs, a key factor controlling rates of decomposition and nutrient release, continues to guide selection and use of organic materials as nutrient sources. The relationship between decomposition patterns and the quality parameters of the fresh leaves of six agroforestry species: Sesbania sesban, Croton megalocarpus, Calliandra calothyrsus, Tithonia diversifolia, Lantana camara, and Senna spectabilis, was investigated in a litterbag study over a period of 77 days in the highlands of western Kenya. The litterbags were buried 1 cm below the soil surface and covered with soil of ca 1 cm thickness. Percent leaf mass and total N and P that remained with time strongly correlated with total P and C/P ratio (R²= 0.60‐0.90) during the first 35 days of study; but afterwards, correlation was stronger with the initial soluble polyphenolics (Pp)/P ratio (R²= 0.69‐0.92) than with total P and C/P ratio. Loss of leaf mass and release of N and P followed the exponential function, y_t= y₀* e‐^kt, from which the specific decay rate constants (k) were calculated for loss of leaf mass (k_B) and release of N (k_N) and P (K_p). Among the plant species, the k values were lowest in Calliandra with k_B= 0.012/d, k_N= 0.017/d and k_p= 0.044/d. Lantana had the highest K values with k_g= 0.067/d and k_p= 0.119/d, but the highest k_N value of 0.109/d occurred in Tithonia. The k_B values for all organic materials were lower than their corresponding k_N and k_p values, suggesting that leaching of N and P from litters may have augmented the microbial mineralization of N and P. There was a strong correlation between the k_B, k_N, and k_p values and total P (r = 0.82‐0.96; P 0.01), but not total N, lignin (LIG), or Pp. Rates of N and P release followed the general trend: Tithonia > Senna > Lantana > Sesbania > Croton > Calliandra. The results indicated that, among the quality parameters studied, total P is the most important factor controlling rate of decomposition and N and P release from organic inputs in the area of study.     

Model aided design of repeated fed-batch penicillin fermentation

Structured models of antibiotic fermentation that quantify maturation and aging of product forming biomass are fitted to experimental data. Conditions of superiority of repeated fed batch cultivation are characterized on the basis of a performance criterion that includes penicillin productivity and costs of operation. Emphasis is placed on the relevance of such research to the model aided design of optimal cyclic operation.List of Symbols c IU/mg cost factor - D s^–1 dilution rate - J IU · cm^–3 · h^–1 net productivity - k _p IU · mg^–11 · h^–1 specific product formation rate - k _pm IU · mg^–1 · h^–1 maximum specific product formation rate - p IU/cm³ concentration of penicillin - T s final time of fermentation - t s fermentation time - X kg/m³ concentration of biomass dry weight - X ₁kg/m³ concentration of young, immature biomass - X ₂ kg/m³ concentration of mature product forming biomass - X _c kg/m³ biomass concentration of the end of growth phase - X _mkg/m³ maximum biomass concentration Greek Letters s^–1 specific maturation rate - s^–1 specific aging rate - s^–1 specific growth rate - _m s^–1 maximum specific growth rate - _p s^–1 specific growth rate during the product formation phase - s cycle time - % volume fraction of draw-off Abbreviations CC chemostat culture - RFBC repeated fed batch culture - RBC repeated batch culture     

Optimization of the microbial synthesis of dihydroxyacetone from glycerol with <Emphasis Type="Italic">Gluconobacter oxydans</Emphasis>

An optimized repeated-fed-batch fermentation process for the synthesis of dihydroxyacetone (DHA) from glycerol utilizing Gluconobacter oxydans is presented. Cleaning, sterilization, and inoculation procedures could be reduced significantly compared to the conventional fed-batch process. A stringent requirement was that the product concentration was kept below a critical threshold level at all times in order to avoid irreversible product inhibition of the cells. On the basis of experimentally validated model calculations, a threshold value of about 60 kg m^-3 DHA was obtained. The innovative bioreactor system consisted of a stirred tank reactor combined with a packed trickle-bed column. In the packed column, active cells could be retained by in situ immobilization on a hydrophilized Ralu-ring carrier material. Within 17 days, the productivity of the process could be increased by 75% to about 2.8 kg m^-3 h^-1. However, it was observed that the maximum achievable productivity had not been reached yet.Abbreviations K _O Monod half saturation constant of dissolved oxygen (kg m^-3) - K _S Monod half saturation constant of substrate glycerol (kg m^-3) - O Dissolved oxygen concentration (kg m^-3) - P Product concentration (kg m^-3) - P _crit Critical product concentration constant (kg m^-3) - S Substrate concentration (kg m^-3) - t Time (s) - X Biomass concentration (dry weight) (kg m^-3) - Y _P/S Yield coefficient of product from substrate - Y _X/S Yield coefficient of biomass from substrate - Growth dependent specific production rate constant (kg m^-3) - Growth independent specific production rate constant (s^-1) - Specific growth rate (s^-1) - _max Maximum specific growth rate constant (s^-1)     

On-line parameter and state estimation of continuous cultivation by extended Kalman filter

Summary The on-line estimation of biomass concentration and of three variable parameters of the non-linear model of continuous cultivation by an extended Kalman filter is demonstrated. Yeast growth in aerobic conditions on an ethanol substrate is represented by an unstructured non-linear stochastic t-variant dynamic model. The filter algorithm uses easily accessible data concerning the input substrate concentration, its concentration in the fermentor and dilution rate, and estimates the biomass concentration, maximum specific growth rate, saturation constant and substrate yield coefficient. The microorganismCandida utilis, strain Vratimov, was cultivated on the ethanol substrate. The filter results obtained with the real data from one cultivation experiment are presented. The practical possibility of using this method for on-line estimation of biomass concentration, which is difficult to measure, is discussed.Nomenclature D dilution rate (h^-1) - DO₂ dissolved oxygen concentration (%) - E identity matrix - F Jacobi matrix of the deterministic part of the system equations g - g continuousn-vector non-linear real function - h m-vector non-linear real function - K Kalman filter gain matrix - K _S saturation constant (kgm^-3) - K_S expectation of the saturation constant estimate - M Jacobi matrix of the deterministic part of the measurement equations h - P(t₀) co-variance matrix of the initial values of the state - P(t_k/t_k) c-variance matrix of the error in (t _k|t _k) - P(t_k+1/t_k) co-variance matrix of the error in (t _k+1|t _k - Q co-variance matrix of the state noise - R co-variance matrix of the output noise - S substrate concentration (kgm^-3) - S _i input substrate concentration - t time - t _k discrete time instant with indexk=0, 1, 2,... - u(t) input vector - v(t_k) measurement (output) noise sequence - w(t) n-vector white Gaussian random process - x(t₀) initial state of the system - (t₀) expectation of the initial state values - x(t) n-dimensional state vector - x(t_k) state vector at the time instantt _k - (t_k|t_k) expectation of the state estimate at timet _k when measurements are known to the timet _k - (t_k+1|t_k) expectation of the state prediction - X biomass concentration (kgm^-3) - expectation of the biomass concentration estimate - y(t_k) m-dimensional output vector at the time instantt _k - Y _XIS substrate yield coefficient - _X|S expectation of the substrate yield coefficient estimate - specific growth rate (h^-1) - _M maximum specific growth rate (h^-1) - expectation of the maximum specific growth rate estimate - state transition matrix     

Microcalorimetric Study on the Toxic Effect of Pb<Superscript>2+</Superscript> to <Emphasis Type="Italic">Tetrahymena</Emphasis>

The toxic effect of Pb²⁺ has been studied in eukaryotic cells by using Tetrahymena as a target. The maximum power (P _m) and the growth rate constant (k) were determined, which showed that values of P _m and k were linked to the concentration (C) of Pb²⁺. The addition of Pb²⁺ caused a decrease of the maximum heat production and growth rate constant, indicating that Tetrahymena growth was inhibited in the presence of Pb²⁺, and Pb²⁺ took part in the metabolism of cells. From micrographs, morphological changes of Tetrahymena were observed with addition of Pb²⁺, indicating that the toxic effect of Pb²⁺ derived from destroying the membrane of surface of Tetrahymena. According to the thermogenic curves and photos of Tetrahymena under different conditions, it is clear that metabolic mechanism of Halobacterium halobium R1 growth has been changed with the addition of Pb²⁺.     

An investigation of cell density effects on hybridoma metabolism in a homogeneous perfusion reactor

In this work, metabolite and antibody production kinetics of hybridoma cultures were investigated as a function of cell density and growth rate in a homogeneous perfusion reactor. Hydrophilized hollow fiber polypropylene membranes with a pore size of 0.2 m were used for medium perfusion. Oxygen was supplied to the cells through thin walled silicone tubing. The mouse-mouse hybridoma cells were grown in three identical bioreactors at perfusion rates of 1.1, 2.0, and 3.2/day for a period of eight days during which the viable cell concentrations reached stable values of 2.6×10⁶, 3.5×10⁶, and 5.2×10⁶ cells/ml, respectively. Total cell densities reached values ranging from 8×10⁶ to 1×10⁶ cells/ml. Specific substrate consumption and product formation rates responded differently to changes in cell density and apparent specific growth rate, which were not varied independently. Using multiple regression analysis, the specific glucose consumption rate was found to vary with viable cell density while the specific glutamine uptake and lactate production rates varied with both viable cell density and apparent specific growth rate. These results suggest that cell density dictates the rate of glucose consumption while the cell growth rate influences how glucose is metabolized, i.e., through glycolysis or the TCA cycle. The specific antibody production rate was found to be a strong function of cell density, increasing as cell density increased, but was essentially independent of the specific growth rate for the cell line under study.List of Symbols MAb monoclonal antibody - X _v viable cell density (cells/ml) - X _d nonviable cell density (cells/ml) - specific growth rate (1/day) - k _d specific death rate (1/day) - D dilution rate (1/day) - S _f substrate concentration in feed (g/l or mM) - S substrate concentration (g/l or mM) - P _f product concentration in feed (g/l or g/ml) - P product concentration (g/l or ug/ml) - q _s specific consumption rate of substrate (g/hr/cell or mmol/hr/cell) - q _p specific production rate of product (g/hr/cell) - q _MAb specific production rate of monoclonal antibody (g/hr/cell) This work was supported in part by a grant for the National Science Foundation (BCS-9157851) and by matching funds from Merck and Monsanto. We sincerely thank Mr. Roland Buchele of Akzo Inc. (Germany) for donation of the polypropylene membranes, Dr. Michael Fanger (Dartmouth Medical School) for the hybridoma cell line, Dr. Sadettin Ozturk (Verax Corp., Lebanon, NH) for technical discussions regarding reactor design, and Dr. Derrick Rollins (Iowa State University) for advice on statistical methods.     

Enzyme production in a cell recycle fermentation system evaluated by computer simulations

Enzyme production in a cell recycle fermentation system was studied by computer simulations, using a mathematical model of -amylase production by Bacillus amyloliquefaciens. The model was modified so as to enable simulation of enzyme production by hypothetical organisms having different production kinetics at different fermentation conditions important for growth and production. The simulations were designed as a two-level factorial assay, the factor studied being fermentation with or without cell recycling, repression of product synthesis by glucose, kinetic production constants, product degradation by a protease, mode of fermentation, and starch versus glucose as the substrate carbon source.The main factor of importance for ensuring high enzyme production was cell recycling. Product formation kinetics related to the stationary growth phase combined with continuous fermentation with cell recycling also had a positive impact. The effect was greatest when two or more of these three factors were present in combinations, none of them alone guaranteeing a good result. Product degradation by a protease decreased the amount of product obtained; however, when combined with cell recycling, the protease effect was overshadowed by the increased production. Simulation of this type should prove a useful tool for analyzing troublesome fermentations and for identifying production organisms for further study in integrated fermentation systems.List of Symbols a proportionality constant relating the specific growth rate to the logarithm of G (h) - a ₁ reaction order with respect to starch concentration - a ₂ reaction order with respect to glucose concentration - c starch concentration (g/l) - c ₀ starch concentration in the feed (g/l) - D dilution rate (h^–1) - e intrinsic intracellular amylase concentration (g product/g cell mass) - E extracellular amylase concentration (g/l) - F volumetric flow rate (l/h) - G average number of genome equivalents of DNA/cell - K ₁ intracellular repression constant - K ₂ intracellular repression constant - K _s Monod saturation constant (g/l) - k ₃ product excretion rate constant (h^–1) - k _I translation constant (g product/g mRNA/h) - k _d first order decay constant (h^–1) - k _dw first order decay constant (h^–1) - k _gl rate constant for glucose production (g/l/h) - k _{m, dgr} saturation constant for product degradation (g/l) - k _st rate constant for starch hydrolysis (g/l/h) - k _t1 proportionality constant for amylase production (g mRNA/g substrate) - k _t2 proportionality constant for amylase production (g mRNA *h/g substrate) - k _w protease excretion rate constant (h^–1) - k _wt1 proportionality constant for protease production (g mRNA/g substrate) - k _wt2 proportionality constant for protease production (g mRNA *h/g substrate) - k _wI translation constant (g protease/g mRNA/h) - m maintenance coefficient (g substrate/g cell mass/h) - n number of binding sites for the co-repressor on the cytoplasmic repressor - Q repression function, K₁/K₂ less than or equal to 1.0 - Q _w repression function, K₁/K₂ less than or equal to 1.0 - r intrinsic amylase mRNA concentration (g mRNA/g cell mass) - r _m intrinsic protease mRNA concentration (g mRNA/g cell mass) - R _ex retention by the filter of the compounds x=: C starch, E amylase, or S glucose - R _t amylase transport rate (g product/g cell mass/h) - R _wt protease transport rate (g protease/g cell mass/h) - R _s rate of glucose production (g/l/h) - R _c rate of starch hydrolysis (g/l/h) - S ₀ feed concentration of free reducing sugar (g/l) - s extracellular concentration of reducing sugar (g/l) - t time (h) - V volume (1) - w intracellular protease concentration (g/l) - W extracellular protease concentration (g/l) - X cell mass concentration (dry weight) (g/l) - Y yield coefficient (g cell mass/g substrate) - substrate uptake (g substrate/g cell mass/h) - specific growth rate of cell mass (h^–1) - _d specific death rate of cells (h^–1) - _m maximum specific growth rate of cell mass (h^–1) - _m,dgr maximum specific rate of amylase degradation (h^–1) This study was supported by the Nordic Industrial Foundation Bioprocess Engineering Programme and the Center for Process Biotechnology, The Technical University of Denmark.     

Diurnal changes in gas exchange and chlorophyll fluorescence parameters of <Emphasis Type="Italic">Fritillaria cirrhosa</Emphasis> and <Emphasis Type="Italic">F. delavayi</Emphasis> under field conditions

To determine what factors limit the growth of wild Fritillaria cirrhosa and Fritillaria delavayi in field conditions, we investigated diurnal changes of the net photosynthetic rate (P _N) and the correlation between P _N and various environmental factors. Parameters of chlorophyll (Chl) fluorescence were evaluated to test whether ecological fragility caused the extinction of wild F. cirrhosa and F. delavayi. Our study reveals for the first time that F. cirrhosa and F. delavayi did not encounter significant stress under field conditions. A small reduction in maximum photochemical efficiency was observed under high irradiance. The maximum P _N of F. cirrhosa was 30 % higher than F. delavayi (p<0.05), and a similar difference was observed for apparent quantum yield (27.3 %, p<0.01). F. delavayi was better adapted to a wide range of irradiances and high environmental temperature. Correlation between P _N and environmental factors (without considering the effects of interactions among environmental factors on P _N) using leaves of F. cirrhosa revealed that the three primary influencing factors were air pressure (p<0.01), relative humidity (p<0.01), and soil temperature (p<0.05). In F. delavayi, the influencing factors were relative humidity (p<0.01), soil temperature (p<0.05), CO₂ concentration (p<0.05), and air pressure (p<0.05). Path analysis (considering effects among environmental factors on P _N) showed that air temperature (negative correlation), photosynthetic photon flux density (PPFD) and relative humidity were the three primary limiting factors influencing the growth of F. cirrhosa. For this species, relative humidity reacted indirectly with air pressure, which was reported singularly in other species. Limiting growth factors for F. delavayi were PPFD, air pressure (negative correlation), soil temperature (negative correlation) and air temperature (negative correlation).     

Influence of pH,lactose and lactic acid on the growth of Streptococcus cremoris: a kinetic study

Summary The effect of various culture conditions on growth kinetics of an homofermentative strain of the lactic acid bacterium Streptococcus cremoris were investigated in batch cultures, in order to facilitate the production of this organism as a starter culture for the dairy industry. An optimal pH range of 6.3–6.9 was found and a lactose concentration of 37 g·l^-1 was shown to be sufficient to cover the energetic demand for biomass formation, using the recommended medium. The study of the effect of lactic acid concentration on growth kinetics revealed that the end-product was not the sole factor affecting growth. The strain was characterized for its tolerance towards lactic acid and a critical concentration of 70 g·l^-1 demonstrated. With the product yield of 0.9 g·g^-1 at non-lactose limiting conditions the lactic acid concentration of 33 g·l^-1 could not explain the low growth rates obtained, implicating a nutritional limitation.Symbols t _f fermentation duration (h) - X Biomass concentration (g·l^-1) - X _m maximum biomass concentration (g·l^-1) - S lactose concentration (g·l^-1) - S _r residual lactose concentration (g·l^-1) - P produced lactic acid concentration (g·l^-1) - P _a added lactic acid concentration (g·l^-1) - P _c critical lactic acid concentration (g·l^-1) - specific growth rate (h^-1) - _max maximum specific growth rate (h^-1) - R _x/S biomass yield (g·g^-1) calculated when =0 - R _P/S product yield (g·g^-1)     

Advantages of fungal enzyme production in solid state over liquid fermentation systems

The present paper attempts to explain why enzyme production in solid-state fermentation (SSF) is higher than in submerged fermentation (SmF). Recent work done in our laboratory [Biotechnol. Lett. 22 (2000) 1255; J. Ind. Microbiol. Biotechnol. 26 (5) (2001) 271; J. Ind. Microbiol. Biotechnol. 26 (5) (2001) 296] related to the production of invertase, pectinases and tannases, by Aspergillus niger grown by SSF and SmF is reviewed. To do such a comparative study, logistic and Luedeking–Piret equations are used in order to estimate the values of the following coefficients: maximal specific growth rate (μ_M), maximal biomass level (X_M), enzyme/biomass yield (Y_P/X) and secondary rate of production, or breakdown (k). It is shown that enzyme productivity is proportional to group, μ_MY_P/XX_M, corrected by a function of ν=k/Y_P/Xμ_M. In all three cases of enzyme production studied, productivity using a SSF system was higher than in SmF. Studies with invertase resulted in higher values of μ_MX_M. Studies with pectinases resulted in higher values of Y_P/XX_M. Studies with tannases resulted in higher Y_P/X and less negative values of k. Finally, a reaction–diffusion model is presented to try to explain such differences based on micrographic measurements of mycelial aggregates for each kind of fermentation system.     

Optimization of polylactic-co-glycolic acid nanoparticles containing itraconazole using 2<Superscript>3</Superscript> factorial design

This study investigated the utility of a 2³ factorial design and optimization process for polylactic-co-glycolic acid (PLGA) nanoparticles containing itraconazole with 5 replicates at the center of the design. Nanoparticles were prepared by solvent displacement technique with PLGAX ₁ (10, 100 mg/mL), benzyl benzoateX ₂ (5, 20 μg/mL), and itraconazoleX ₃ (200, 1800 μg/mL). Particle size (Y ₁), the amount of itraconazole entrapped in the nanoparticles (Y ₂), and encapsulation efficiency (Y ₃) were used as responses. A validated statistical model having significant coefficient figures (P<.001) for the particle size (Y ₁), the amount of itraconazole entrapped in the nanoparticles (Y ₂), and encapsulation efficiency (Y ₃) as function of the PLGA (X ₁), benzyl benzoate (X ₂), and itraconazole (X ₃) were developed: Y₁=373.75+66.54X₁+52.09X₂+105.06X₃−4.73X₁X₂+46.30X₁X₃; Y₂=472.93+73.45X₁+ 169.06X₂+333.03X₃+62.40X₁X₃+141.49X₂X₃; Y₃= 57.36+6.53X₁+15.52X₂−12.59X₃+1.01X₁X₃+ 1.73X₂X₃.X ₁,X ₂, andX ₃ had a significant effect (P<.001) onY ₁,Y ₂, andY ₃. The particle size, the amount of itraconazole entrapped in the nanoparticles, and the encapsulation efficiency of the 4 formulas were in agreement with the predictions obtained from the models (P<.05). An overlay plot for the 3 responses shows the boundary in whichY ₁ shows the boundary in which a number of combinations of concentration of PLGA, benzyl benzoate, and itraconazole will result in a satisfactory process. Using the desirability approach with the same constraints, the solution composition having the highest overall desirability (D=0.769) was 10 mg/mL of PLGA, 16.94 μg/mL of benzyl benzoate, and 1001.01 μg/mL of itraconazole. This approach allowed the selection of the optimum formulation ingredients for PLGA nanoparticles containing itraconazole of 500 μg/mL.     

A mathematical model of kinetics of the biosynthesis of tetracyclines

Summary A mathematical model simulating the behaviour or Streptomyces aureofaciens in batch culture under conditions when tetracyclines are synthesized in excessive amounts has been formulated. The response of the mathematical model to the experimental conditions applied corresponds with data obtained in the experiments. The mathematical model demonstrated that the level of tetracycline production is determined during the period of culture growth beginning with exhaustion of inorganic phosphate from the medium and ending with inhibition of the synthesis of enzymes caused by the synthesized tetracyclines. Further tetracycline synthesis is then proportional to the amount of enzymes synthesized in this interval.List of symbols E Activity of ACT-oxygenase (10×nkat/g) - P Product concentration (mg/l) - k ₁-k ₆ Rate constants - K _S Saturation constant (g sugar/l) - K _I1 Inhibition constant (mg product/l) - K _I2 Inhibition constant (mM phosphate/l) - K _I3 Inhibition constant (mg product/l) - S ₁ Substrate sucrose (g sugar/l) - S ₂ Substrate concentration — phosphate (mM/l) - r _P Specific rate of product formation (mg product/g · h) - r _E Specific rate of enzyme synthesis (10×nkat/g² · h), Expressed by activity units - t Cultivation time (hour) - X Biomass dry weight (g/l) - Y _S/X Yield coefficient - Specific growth rate (h^-1)     

Changes in intracellular ATP‐content of CHO cells as response to hyperosmolality

A variety of approaches has been published to enhance specific productivity (q_p) of recombinant Chinese hamster ovary (CHO) cells. Changes in culture conditions, e. g. temperature shifts, sodium butyrate treatment and hyperosmolality, were shown to improve q_p. To contribute to a better understanding of the correlation between hyperosmolality and enhanced q_p, we analyzed cellular kinetics and intracellular adenine nucleotide pools during osmotic shift periods. Known phenotypes like increased formation rates for lactate and product (anti‐IL‐8 antibody; q_lactate, q_p) as well as increased cell specific uptake rate for glucose (q_glucose) were found—besides inhibition of cell growth and G1‐arrest occurred during batch cultivations with osmotic shift. The analysis of intracellular AXP pools revealed enlarged ATP amounts for cells as response to hyperosmolality while energy charges remained unchanged. Enhanced ATP‐pools coincided with severely increased ATP formation rates (q_ATP) which outweighed by far the putative requirements attributed to regulatory volume increase. Therefore elevated q_ATP mirrored an increased cellular demand for energy while experiencing hyperosmotic shift. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1212–1216, 2015     

Modeling of the pyruvate production with<Emphasis Type="Italic"> Escherichia coli</Emphasis> in a fed-batch bioreactor

A family of 10 competing, unstructured models has been developed to model cell growth, substrate consumption, and product formation of the pyruvate producing strain Escherichia coli YYC202 ldhA::Kan strain used in fed-batch processes. The strain is completely blocked in its ability to convert pyruvate into acetyl-CoA or acetate (using glucose as the carbon source) resulting in an acetate auxotrophy during growth in glucose minimal medium. Parameter estimation was carried out using data from fed-batch fermentation performed at constant glucose feed rates of q_VG=10 mL h^–1. Acetate was fed according to the previously developed feeding strategy. While the model identification was realized by least-square fit, the model discrimination was based on the model selection criterion (MSC). The validation of model parameters was performed applying data from two different fed-batch experiments with glucose feed rate q_VG=20 and 30 mL h^–1, respectively. Consequently, the most suitable model was identified that reflected the pyruvate and biomass curves adequately by considering a pyruvate inhibited growth (Jerusalimsky approach) and pyruvate inhibited product formation (described by modified Luedeking–Piret/Levenspiel term).List of symbols c_A acetate concentration (g L^–1) - c_A,0 acetate concentration in the feed (g L^–1) - c_G glucose concentration (g L^–1) - c_G,0 glucose concentration in the feed (g L^–1) - c_P pyruvate concentration (g L^–1) - c_P,max critical pyruvate concentration above which reaction cannot proceed (g L^–1) - c_X biomass concentration (g L^–1) - K_I inhibition constant for pyruvate production (g L^–1) - K_I^A inhibition constant for biomass growth on acetate (g L^–1) - K_P saturation constant for pyruvate production (g L^–1) - K_P inhibition constant of Jerusalimsky (g L^–1) - K_S^A Monod growth constant for acetate (g L^–1) - K_S^G Monod growth constant for glucose (g L^–1) - m_A maintenance coefficient for growth on acetate (g g^–1 h^–1) - m_G maintenance coefficient for growth on glucose (g g^–1 h^–1) - n constant of extended Monod kinetics (Levenspiel) (–) - q_V volumetric flow rate (L h^–1) - q_VA volumetric flow rate of acetate (L h^–1) - q_VG volumetric flow rate of glucose (L h^–1) - r_A specific rate of acetate consumption (g g^–1 h^–1) - r_G specific rate of glucose consumption (g g^–1 h^–1) - r_P specific rate of pyruvate production (g g^–1 h^–1) - r_P,max maximum specific rate of pyruvate production (g g^–1 h^–1) - t time (h) - V reaction (broth) volume (L) - Y_P/G yield coefficient pyruvate from glucose (g g^–1) - Y_X/A yield coefficient biomass from acetate (g g^–1) - Y_X/A,max maximum yield coefficient biomass from acetate (g g^–1) - Y_X/G yield coefficient biomass from glucose (g g^–1) - Y_X/G,max maximum yield coefficient biomass from glucose (g g^–1) - growth associated product formation coefficient (g g^–1) - non-growth associated product formation coefficient (g g^–1 h^–1) - specific growth rate (h^–1) - _max maximum specific growth rate (h^–1)     

Transfer and consumption of oxygen during the cultivation of the ectomycorrhizal fungus Rhizopogon nigrescens in an airlift bioreactor

The study had the objective of examining the aspects involved in the cultivation of ectomycorrhizal fungi for the production of commercially sustainable inoculant to attend the demands of the seedling nursery industry. It focused on certain parameters, such as the oxygen consumption levels, during the cultivation of the ectomycorrhizal fungus Rhizopogon nigrescens CBMAI 1472, which was performed in a 5-L airlift bioreactor. The dynamic method was employed to determine the volumetric coefficient for the oxygen transfer (k _L a) and the specific oxygen uptake rate (Q _O2 ). The results indicate that specific growth rates (μ _X ) and oxygen consumption decline rapidly with time, affected mainly by increases in biomass concentration (X). Increases in X are obtained primarily by increases in the size of pellets that are formed, altering, consequently, the cultivation dynamics. This is the result of natural increases in transferring resistance that are observed in these environments. Therefore, to avoid critical conditions that affect viability and the productivity of the process, particular settings are discussed.

     

Kinetics of <Emphasis Type="Italic">Bacillus thuringiensis</Emphasis> var. <Emphasis Type="Italic">israelensis</Emphasis> growth on high glucose concentrations

The kinetic and general growth features of Bacillus thuringiensis var. israelensis were evaluated. Initial glucose concentration (S ₀) in fermentation media varied from 10 to 152 g/l. The results afforded to characterize four morphologically and physiologically well-defined culture phases, independent of S ₀ values: Phase I, vegetative growth; Phase II, transition to sporulation; Phase III, sporulation; and Phase IV, spores maturation and cell lysis. Important process parameters were also determined. The maximum specific growth rates (μ _X,m) were not affected with S ₀ up to 75 g/l (1.0–1.1 per hour), but higher glucose concentrations resulted in growth inhibition by substrate, revealed by a reduction in μ _X,m values. These higher S ₀ values led to longer Phases III and IV and delayed sporulation. Similar biomass concentrations (X _m = 15.2–15.9 g/l) were achieved with S ₀ over 30.8 g/l, with increasing residual substrate, suggesting a limitation in some other nutrients and the use of glucose to form other metabolites. In this case, with S ₀ from 30.8 to 152 g/l, cell yield (Y _X/S ) decreased from 0.58 to 0.41 g/g. On the other hand, with S ₀ = 10 g/l growth was limited by substrate, and Y _X/S has shown its maximum value (0.83 g/g).     

Maturity and product formation in cultures of microorganisms

The concept of a maturation time (t_m) for a product formation by a microbial culture is developed and a simple method is described for determining this parameter and also the product formation rate constant (k_p) from batch culture experiments. The concept has been utilized in a general model for the prediction of steady state product concentrations in single-stage continuous-flow culture systems.