A theoretical and experimental evaluation of a novel radial-flow hollow fiber reactor for mammalian cell culture

A hollow fiber perfusion reactor constructed from pairs of concentric fibers forming a thin annular space is analyzed theoretically in terms of mass transfer resistances, and is shown experimentally to support the growth of an anchorage-dependent cell line in high-density culture. Hollow fiber perfusion reactors described in the literature typically employ a perfusion pathlength much greater than the distance that could be supported by diffusion alone, and analyses of these reactors typically incorporate the assumption of uniform perfusion throughout the cell mass despite many reported observations of inhomogeneous cell growth in perfusion reactors. The mathematical model developed for the annular reactor predicts that the metabolism of oxygen, carbon substrates, and proteins by anchorage-dependent cells can be supported by the reactor even in the absence of perfusion. The implications of nonuniform cell growth in perfusion reactors in general is discussed in terms of nutrient distribution. In the second part of the paper, the growth and metabolism of the mouse adrenal tumor line Y-1 in flask culture and in the annular reactor are compared. The reactor is shown to be a promising means for culturing anchorage-dependent cells at high density.List of Symbols c mol/dm³ substrate concentration - D _mm²/s effective diffusivity of substrate in the membrane - D _tm²/s effective diffusivity of substrate in the cell region - L _pm²s/kg hydraulic permeability of fiber - Pe _m Peclet number for membrane transport, _wR₁/D _m - Pe _t Peclet number for transport through cell mass, v _wR₂/D _t - Q mol/m³s zero-order consumption rate of substrate per unit volume of cell mass - r m radial distance from centerline of fiber lumen - R ₁, R ₂ m inner and outer radii of inner annular fiber (Fig. 1) - R ₃, ₄ m inner and outer radii of outer annular fiber (Fig. 1) - v _wm/s fluid velocity through the fiber wall at R ₁ - fraction of shell side filled with cells - dimensionless radial distance, R ₃/R₁ - dimensionless radial distance, R ₂/R ₁ - cm² hydraulic conductivity - viscosity - ², Thiele modulus - dimensionless radial distance, R ₄/R ₁     

Reactor design for the enzymatic isomerization of glucose to fructose

A comprehensive methodology is presented for the design of reactors using immobilized enzymes as catalysts. The design is based on material balances and rate equations for enzyme action and decay and considers the effect of mass transfer limitations on the expression of enzyme activity. The enzymatic isomerization of glucose into fructose with a commercial immobilized glucose isomerase was selected as a case study. Results obtained are consistent with data obtained from existing high-fructose syrup plants. The methodology may be extended to other cases, provided sound expressions for enzyme action and decay are available and a simple flow pattern within the reactor might be assumed.List of Symbols C kat/kg specific activity of the catalyst - D m²/s substrate diffusivity within the catalyst particle - Dr m reactor diameter - d d operating time of each reactor - E kat initial enzyme activity - E _i kat initial enzyme activity in each reactor - F m³/s process flowrate - F _i m³/s reactor feed flowrate at a given time - F ₀ m³/s initial feed flowrate to each reactor - H number of enzyme half-lives used in the reactors - K mole/m³ equilibrium constant - K _S mole/m³ Michaelis constant for substrate - K _P mole/m³ Michaelis constant for product - K _m mole/m³ apparent Michaelis constant f(K, K _s, K_p, s₀) - k mole/s · kat reaction rate constant - k _d d^–1 first-order thermal inactivation rate constant - L m reactor height - L _r m height of catalyst bed - N _R number of reactors - P _i kg catalyst weight in each reactor - p mole/m³ product concentration - R m particle radius - R _P ratio of minimum to maximum process flowrate - r m distance to the center of the spherical particle - s mole/m³ substrate concentration - s _0i mole/m³ substrate concentration at reactor inlet - s ₀ mole/m³ bulk substrate concentration - s mole/m³ apparent substrate concentration - T K temperature - t d time - t _i d operating time for reactor i - t _s d time elapsed between two successive charges of each reactor - V m³ reactor volumen - V _m mole/m³ s maximum apparent reaction rate - V _p mole/m³ s maximum reaction rate for product - V _R m³ actual volume of catalyst bed - V _r m³ calculated volume of catalyst bed - V _S mol/m³ s maximum reaction rate for substrate - v mol/m³ s initial reaction rate - v _i m/s linear velocity - v _m mol/m³ s apparent initial reaction rate f(K_m, s,V_m) - X substrate conversion - X _eq substrate conversion at equilibrium - =s/K dimensionless substrate concentration - ₀=s₀/K bulk dimensionless substrate concentration - _eq=s_eq/K dimensionless substrate concentration at equilibrium - local effectiveness factor - mean integrated effectiveness factor - Thiéle modulus - =r/R dimensionless radius - _s kg/m³ hydrated support density - substrate protection factor - s residence time     

Cost minimization in the predesign of an enzymatic CSTR: an overall approach

The balance equations pertaining to the modelling of a CSTR performing an enzyme-catalyzed reaction in the presence of enzyme deactivation are developed. Combination of heuristic correlations for the size-dependent cost of equipment and the purification-dependent cost of recovery of product with the mass balances was used as a basis for the development of expressions relating a (suitably defined) dimensionless economic parameter with the optimal outlet substrate concentration under the assumption that overall production costs per unit mass of product were to be minimized. The situation of Michaelis-Menten kinetics for the substrate depletion and first order kinetics for the deactivation of enzyme (considering that the free enzyme and the enzyme in the enzyme/substrate complex deactivate at different rates) was explored, and plots for several values of the parameters germane to the analysis are included.List of Symbols C _E mol m^–3 concentration of active enzyme - C _E,0 mol m^–3 initial concentration of active enzyme - C _p mol m^–3 concentration of product of interest - C _s mol m^–3 concentration of substrate - C _s,0 mol m^–3 initial concentration of substrate - I $ capital cost of equipment - k _d s^–1 deactivation constant of free enzyme - k _d s^–1 deactivation constant of enzyme in enzyme/substrate complex - K _m mol m^–3 Michaelis-Menten constant - K _m dimensionless counterpart of K _m - k _r s^–1 rate constant associated with conversion of enzyme/substrate complex into product - M _w kg mol^–1 molecular weight of product of interest - P $ kg^–1 cost of recovery of product of interest in pure form - Q m³s^–1 volumetric flow rate - V m³ volume of reactor - X $ kg^–1 global manufacture cost of product of interest in pure form - X dimensionless counterpart of X Greek Symbols ₁ $ m^–1.8 constant - ₂ $ m^–3 constant - t s useful life of CSTR - ₀ ratio of initial concentrations of enzyme and substrate - ratio of deactivation constant of free enzyme to rate constant of depletion of substrate - ratio of deactivation constants - univariate function expressing the dependence of the rate of enzyme deactivation on C _S - univariate function expressing the dependence of the rate of substrate depletion on C _S - dimensionless economic parameter     

The effect of micro- and macromixing on enzyme reaction in a real CSTR

The effect of micromixing and macromixing on enzyme reaction of Michaelis-Menten type in a real continuously stirred tank reactor (CSTR) is considered. The effect of bypassing of a fraction of feed stream, dead space, initial enzyme concentration and Michaelis-Menten constant on substrate conversion is evaluated. Bypass reduces the substrate conversion significantly compared with other parameters in the case of micro and macromixing. Micromixing predicts higher substrate conversions compared with macromixing. The effect of micro and macromixing on substrate conversion is negligible at low and high conversions.List of Symbols C kmol/m³ concentration of reactant - ¯C kmol/m³ average concentration of reactant - C_A kmol/m³ exit concentration of reactant A - C_Aa kmol/m³ exit concentration of reactant A from active zone - C_AO kmol/m³ initial concentration of reactant A - C_EO kmol/m³ initial enzyme concentration - C_O kmol/m³ initial concentration of reactant - E(t) 1/s exit age distribution function - k 1/s reaction rate constant - M kmol/m³ Michaelis-Menten constant - r kmol/(m³s) rate of reaction - –r_A kmol/(m³s) rate of reaction with respect to A - t s time - v m³/s volumetric feed rate - v_a m³/s volumetric feed rate entering the active zone - v_b m³/s volumetric feed rate entering the bypass stream - V m³ total volume of the vessel - V_a m³ active volume of the vessel - V_d m³ volume of dead space - X_A conversion of A Greek Letters fraction of feed stream bypassing the vessel (v_b/v) - fraction of the total volume as dead space (V_d/V) - (t) 1/s Dirac delta function, an ideal pulse occurring at time t = 0 - s life expectancy of a molecule - 1/s intensity function or escape probability function - s space time or mean residence time     

A kinetic study of the anaerobic digestion of piggery waste using down-flow stationary fixed film reactors

Summary In this paper the behaviour of the down-flow stationary fixed film digesters is studied at laboratory and bench scale. Several organic loading rates are applied to the reactors in order to examine the support surface behaviour. Specific support surfaces of about 50 m²/m³ void volume seems to be optimal. A set of experiments carried out in a continuous stirred reactor is used to fit the kinetic constants of the Chen and Hashimoto's model. The model is then used to assess its applicability to the DSFF digesters. The results show that its application, is possible as a first approximation.Nomenclature B₀ Ultimate methane yield (m³ CH₄/Kg VS) - B Specific methane production (m³ CH₄/Kg VS) - CSTR Continuous stirred tank reactor - DSFF Down-flow Stationary Fixed Film - HRT Hydraulic retention time (days) - K Kinetic constant of the Chen and Hashimoto model (dimensionless) - S Biodegradable substrate concentration (g/l) - SLR Superficial loading rate (Kg VS/m²·d) - SSS Specific support surface (m² support surface/m³ digester void volume) - S₀ Initial substrate concentration (g/l) - VS Volatile solids (g/l) - VFA Volatile Fatty Acids (mg/dm³) - Microorganisms specific growth (day^-1) - _m Kinetic constant of Chen and Hashimoto's model (day^-1) - Retention time (days) - _m Minimum retention time to avoid microorganisms washout (days)     

Biotransformation of cephalosporin C to 7-aminocephalosporanic acid with coimmobilized biocatalyst in a batch bioreactor

Biotransformation of cephalosporin C (CPS-C) to 7-aminocephalosporanic acid (7-ACA) was carried out with coimmobilized permeabilized cells of Trigonopsis variabilis and Pseudomonas species entrapped in Ca-pectate gel beads. Good aeration and stirring during the process was assured. The analysis of this complicated biochemical process in a heterogeneous system was based on the identification of individual effects (internal diffusion, reaction) running simultaneously. A spectrophotometric method was proposed for the determination of 7-(-ketoadipyl amido) cephalosporanic acid (CO-GL-7-ACA) and 7-ACA. The reaction-diffusion model containing dimensionless partial differential equations was solved by using the orthogonal collocation method. A good agreement between experimental values and values predicted by the mathematical model was obtained. Numerical simulations were performed on the basis of following the two assumptions:- several times higher activity of both cells,- hydrogen peroxide was continuously supplied in the bioreactor.List of Symbols A m² surface of the bead - c _i mol/dm³ concentration of component in the bead and/or in the solution - c _i0 mol/dm³ initial concentration of component in the solution - c _l0 mol/dm³ initial concentration of CPS-C in the solution - C _jl orthogonal collocation weights of the first derivation - D _ei m²/s effective diffusion coefficient of the components - D _jl orthogonal collocation weights of the second derivation - k ₅ dm³/(mol · s) kinetic parameter of non-enzyme reaction - K _inh mol/dm³ inhibition parameter for the first enzyme reaction - K _i dimensionless Michaelis constant for the first and second enzyme reaction, defined in Eq. (7) - K _l dimensionless inhibition parameter for the first enzyme reaction, defined in Eq. (7) - K _mi mol/dm³ Michaelis constant for the first and second enzyme reaction - n number of beads - P( _i ) symbol of dimensionless reaction rate, defined in Eq. (13) - r m radial coordinate inside the bead - R m radius of the bead - R(c _i ) mol/(dm³ · s) symbol for reaction rate, defined in Eq. (6) - t s time - V _max mol/(dm³ · s) max. reaction rate for the first and second enzyme reaction - V _L dm³ volume of solution excluding the space occupied by beads - voidage in batch bioreactor - _P porosity of the bead - i dimensionless effective diffusion coefficient of the components, defined in Eq. (7) - dimensionless time, defined in Eq. (7) - _mi Thiele modulus, defined in Eq. (7) - _i dimensionless concentration, defined in Eq. (7) - dimensionless radial position inside the bead, defined in Eq. (7) - _l0 initial dimension concentration of CPS-C, defined in Eq. (9), (10) - _i0 initial dimension concentration of component, defined in Eq. (9), (10) The authors wish to thank Dr. P. Gemeiner of Slovak Academy of Sciences for rendering of pectate gel. This work is supported by Ministry of Education (Grant No. 1/990 935/93).     

Analysis of gas exchange in seedlings of Acer saccharum: integration of field and laboratory studies

In the field, photosynthesis of Acer saccharum seedlings was rarely light saturated, even though light saturation occurs at about 100 mol quanta m^-2 s^-1 photosynthetic photon flux density (PPFD). PPFD during more than 75% of the daylight period was 50 mol m^-2 s^-1 or less. At these low PPFD's there is a marked interaction of PPFD with the initial slope (CE) of the CO₂ response. At PPFD-saturation CE was 0.018 mol m^-2 s^-1/(l/l). The apparent quantum efficiency (incident PPFD) at saturating CO₂ was 0.05–0.08 mol/mol. and PPFD-saturated CO₂ exchange was 6–8 mol m^-2 s^-1. The ratio of internal CO₂ concentration to external (C _i /C _a ) was 0.7 to 0.8 except during sunflecks when it decreased to 0.5. The decrease in C _i /C _a during sunflecks was the result of the slow response of stomates to increased PPFD compared to the response of net photosynthesis. An empirical model, which included the above parameters was used to simulate the measured CO₂ exchange rate for portions of two days. Parameter values for the model were determined in experiments separate from the daily time courses being sumulated. Analysis of the field data, partly through the use of simulations, indicate that the elimination of sunflecks would reduce net carbon gain by 5–10%.List of symbols A measured photosynthetic rate under any set of conditions (mol m^-2 s^-1) - A _m (atm) measured photosynthetic rate at saturating PPFD, 350 l/l CO₂ and 21% (v/v) O₂ (mol m^-2 s^-1) - C constant in equation of Smith (1937, 1938) - C _a CO₂ concentration in the air (l/l) - C _i CO₂ concentration in the intercellular air space (l/l) - C _i ^/* C _i corrected for CO₂ compensation point, i.e., C _i -I ^*, (l/l) - CE initial slope of the CO₂ response of photosynthesis (mol m^-2 s^-1/(l/l)) - CEM CE at PPFD saturation - E transpiration rate (mmol m^-2 s^-1) - F predicted photosynthetic rate (mol m^-2 s^-1) - G leaf conductance to H₂O (mol m^-2 s^-1) - I photosynthetic photon flux density (mol m^-2 s^-1) - N number of data points - P _m predicted photosynthetic rate at saturating CO₂ and given PPFD (mol m^-2 s^-1) - P _ml predicted photosynthetic rate at saturating CO₂ and PPFD (mol m^-2 s^-1) - R _d residual respiratory rate (mol m^-2 s^-1) - T _a air temperature (°C) - T _l leaf temperature (°C) - V reaction velocity in equation of Smith (1937, 1938) - V _max saturated reaction velocity in equation of Smith (1937, 1938) - VPA vapor pressure of water in the air (mbar/bar) - VPD vapor pressure difference between leaf and air (mbar/bar) - X substrate concentration in equation of Smith (1937, 1938) - initial slope of the PPFD response of photosynthesis at saturating CO₂ (mol CO₂/mol quanta) - (atm) initial slope of the PPFD response of photosynthesis at 340 l/l CO₂ and 21% (v/v) O₂ (mol CO₂/mol quanta) - I ^* CO₂ compensation point after correction for residual respiration (l/l) - PPFD compensation point (mol m^-2 s^-1)     

Production of glutamine by micro-gel bead-immobilized Corynebacterium glutamicum 9703-T cells in a stirred tank reactor

The effectiveness of using micro-gel bead-immobilized cells for aerobic processes was investigated. Glutamine production by Corynebacterium glutamicum, 9703-T, cells was used as an example. The cells were immobilized in Sr-alginate micro-gel beads 500 m in diameter and used for fermentation processes in a stirred tank reactor with a modified impeller at 400 min^–1. Continuous production of glutamine was carried out for more than 220 h in this reactor and no gel breakage was observed. As a result of the high oxygen transfer capacity of this system, the glutamine yield from glucose was more than three times higher, while the organic acid accumulation was more than 24 times lower than those obtained with 3.0 mm-gel bead-immobilized cells in an airlift fermentor under similar experimental conditions. During the continuous fermentations there was evolution and proliferation of non-glutamine producing strains which led to a gradual decrease in the productivity of the systems. Although a modified production medium which suppresses cell growth during the production phase was effective in maintaining the productivity, the stability of the whole system was shortened due to high cell deactivation rate in such a medium.List of Symbols C kg/m³ glutamine concentration - C _A mol/m ³ local oxygen concentration inside the gel beads - C _AS mol/m ³ oxygen concentration at the surface of the gel beads - De m²/h effective diffusion coefficient of oxygen in the gel bead - DO mol/m³ dissolved oxygen concentration - F dm³/h medium flow rate - K h^–1 glutamine decomposition rate constant - Km mol/m³ Michaelis Menten constant - QO _2max mol/(kg · h) maximum specific respiration rate - R m radius of the gel beads - r m radial distance - t h time - V _C dm ³ volume of the gel beads - V _L dm ³ liquid volume in the reactor - Vm mol/(m³ · h) maximum respiration rate - X kg/m³ cell concentration - x r/R - y C _A /C_AS - h^–1 cell deactivation rate constant - Thiele modulus defined by R(Vm/De Km) ^1/2 - C _AS /Km - _C kg/(m³-gel · h) specific glutamine formation rate - _c dm³-gel/dm³ V _C /V _L      

Optimization and control of a continuous stirred tank fermenter using learning system

A variable structure learning automaton is used as an optimization and control of a continuous stirred tank fermenter. The algorithm requires no modelling of the process. The use of appropriate learning rules enables to locate the optimum dilution rate in order to maximize an objective cost function. It is shown that a hierarchical structure of automata can adapt to environmental changes and can also modify efficiently the domain of variation of the control variable in order to encompass the optimum value.List of Symbols f Random number - F Dimensionless flow rate (F/V ₀) - F m³/h Flow rate - F ₀ m³/h Inlet flow rate - J Objective function - K _i Dimensionless constant in Eq. (3) (k _i/s₀) - k _i · kg/m³ Substrate inhibition constant in Haldane model - K _m Dimensionless constant in equation (3) (k _s/s₀) - k _m kg/m³ Substrate inhibition constant in Haldane model - L Number of levels of the hierarchical system of automata - N Number of possible control actions - p Probability - S Dimensionless substrate concentration (s/s ₀) - s kg/m³ Substrate concentration - T Dimensionless sampling period - t h Time - v Dimensionless volume (V/V ₀) - V m³ Liquid volume in fermenter - W Input to the stochastic automaton - X Dimensionless biomass concentration - x kg/m³ Biomass concentration - Y Biomass/substrate yield coefficient - Weighting factor in Eq. (4) - Dimensionless specific growth rate (/ ^*) - ^* h^–1 Maximum specific growth rate - h^–1 Specific growth rate - Dimensionless time ( t)     

Turbulent breakage of protein precipitates in mechanically stirred bioreactors

Experimental data relating to the breakage of isoelectric Soya protein precipitates in a mechanically agitated bioreactor are provided and examined in the light of a proposed mechanistic model which relates the size of the maximum attainable aggregate diameter to the energy dissipation rate in the vessel. The analysis suggests that protein precipitation results in the formation of scale-invariant fractal aggregates with a dimensionality of 2.2. Comparing the fractal dimensionality of the protein precipitates with reported values based on computer simulation studies suggests that the aggregates undergo considerable restructuring during agitation.List of Symbols A Hamaker constant (J) - D impeller diameter (m) - d _p primary particle diameter (m) - d _f maximum aggregate diameter (m) - G shear rate (s^–1) - H ₀ separation distance between two primary particles (m) - k constant in Eq. (5) - K constant in Eq. (6) - N impeller speed (rpm or rps) - r radial position in an aggregate, measured from the centre (m) - t time of exposure to shear (mins) - T _e eddy period (s^–1) - v _f aggregate volume (m³) Greek Symbols aggregate dimensionality constant - energy dissipation rate (W/kg) - dynamic viscosity of particle-free liquid (kg/ms) - kinematic viscosity of particle-free liquid (m²/s) - collision probability (–) - _p aggregate density (kg/m³) - _p continuous phase density (kg/m³) - aggregate mechanical strength (N/m²) - shear stress (N/m²) - particle concentration in an aggregate (m³/m³) - (r) porosity at radial position, r     

Optimizing control of a continuous stirred tank fermenter using a neural network

Feedforward neural networks are a general class of nonlinear models that can be used advantageously to model dynamic processes. In this investigation, a neural network was used to model the dynamic behaviour of a continuous stirred tank fermenter in view of using this model for predictive control. In this system, the control setpoint is not known explicitly but it is calculated in such a way to optimize an objective criterion. The results presented show that neural networks can model very accurately the dynamics of a continuous stirred tank fermenter and, the neural model, when used recursively, can predict the state variables over a long prediction horizon with sufficient accuracy. In addition, neural networks can adapt rapidly to changes in fermentation dynamics.List of Symbols F Dimensionless flow rate (F/ V₀) - F m³/h Flow rate - F ₀ m³/h Inlet flow rate - J Objective cost function - K _i Dimensionless constant in Eq. (3) (k _i /s₀) - k _i kg/m³ Substrate inhibition constant in Haldane model - k _m Dimensionless constant in Eq. (3) (k _s /s₀) - k _m kg/m³ Substrate inhibition constant in Haldane model - n prediction horizon - S Dimensionless substrate concentration (s/s₀) - s kg/m³ Substrate concentration - t h Time - v Dimensionless volume (V/V₀) - V m³ Liquid volume in fermenter - W _ij , W _jk Weight matrices in neural network - X Dimensionless biomass concentration - x kg/m³ Biomass concentration - Y Biomass/substrate yield coefficient - Weighting factor in Eq. (4) - Dimensionless specific growth rate (/ ) - 1/h Maximum specific growth rate - 1/h Specific growth rate - Dimensionless time ( t)     

Procedure for determination of elemental sulphur bio-oxidation rate

A procedure is described for measuring the rate of biooxidation of elemental sulphur in nutrient solutions. Results of preliminary measurements of sulphur bio-oxidation rate in a dynamic system are presented. The rate of sulphur bio-oxidation has been determined at the level of 0.02–0.05 g of sulphur per m² of sulphur per h.List of Symbols C g/dm³ concentration of sulphate ions - C ₂ g/dm³ concentration of sulphate ions in withdrawn solution - C g/dm³ C difference between solution outlet and inlet to sulphur bed - F m² sulphur surface exposed to bacteria action - m g mass of elemental sulphur - V dm³ volume of solution - V ₀ dm³/h volume of fresh solution supplied to the set - V ₁ dm³/h circulating solution flow rate - V ₂ dm³/h volume of solution withdrawn - h time Abbreviations RBES rate of bio-oxidation of elemental sulphur     

Purification and properties of the ATP: adenylylsulphate 3′-phosphotransferase from Chlamydomonas reinhardii

APS-kinase (ATP: adenylylsulphate 3-phosphotransferase, EC 2.7.1.25) has been purified from the alga Chlamydomonas reinhardii, strain CW 15 by means of chromatofocussing and affinity chromatography. The isolated protein showed an apparent molecular mass of 44,000 upon sodium dodecylsulphate polyacrylamide gel electrophoresis. The transfer of phosphate groups from ATP onto APS required a pH of 6.8, the presence of Mg²⁺ ions and a reducing thiol. Its catalytical activity was destroyed by sulphhydryl group inhibitors (phenyl-mercuri compounds, dithiopyridine) and alkylating reagents.The purified enzyme attained a V _max of 360 pkat under optimal reaction conditions declining to v _limit of 260 pkat in the presence of excess substrate APS. This sensitivity towards changes in substrate concentrations was parallelled by a high affinity and specificity: apparent K _m APS: 2 · 10^-6 mol · l^-1, and K _m ATP: 7 · 10^-6 mol · l^-1. The enzyme was found specific for ATP, d-ATP and CTP, while UTP, ITP and GTP showed marginal activity. The Hill coefficients suggested 4 binding sites for APS and 1 for ATP. Excessive APS resulted in a negative slope indicating 3 inhibiting sites of the substrate.Abbreviations APS Adenosine 5-phosphosulphate - dATP 2-deoxyadenosine 5-triphosphate - p-CMB p-chloromercuribenzoate - DTE dithioerythritol - DTT dithiothreitol - -MSH -mercaptoethanol - PAPS 3-phosphoadenosine 5-phosphosulphate - PAP 3-phosphoadenosine 5-phosphate - SDS sodium dodecyl sulphate This work is part of a dissertation submitted by H. G. J., Bochum 1982     

High yeast concentration in continuous fermentation with cell recycle obtained by tangential microfiltration

Summary Continuous fermentation fed by 150 kg/m³ of glucose with total cell recycling by tangential microfiltration enabled yeasts concentration of 300 kg/m³ (dry weight) to be reached with a dilution rate of 0,5h^–1 and a cell viability greater than 75%. The stability of this system was tested for 50 residence times of the permeate. The method can be used both for the production of cell concentrates and for high rates of metabolite production.Nomenclature D. W. dry weight - X_T (kg/m³) total cell concentration D.W. - X_V (kg/m³) viable cell concentration D.W. - V viability of cell culture in per cent of total cell concentration - S (kg/m³) glucose concentration - P (kg/m³) ethanol concentration - D (h) dilution rate - R (kg/kg) fermentation yield - (h) specific growth rate - vp(kg/kg/h) specific alcohol production rate - (m) yeast size - (kg/kg) kg of intracellular water per kg of dry cells     

Large-scale expression of recombinant sialyltransferases and comparison of their kinetic properties with native enzymes

Values ofK _m were determined for three purified sialyltransferases and the corresponding recombinant enzymes. The enzymes were Gal1-4GlcNAc 2-6sialyltransferase and Gal1-3(4)GlcNAc 2-3sialyltransferase from rat liver; these enzymes are responsible for the attachment of sialic acid to N-linked oligosaccharide chains; and the Gal1-3GalNAc 2-3sialyltransferase from porcine submaxillary gland that is responsible for the attachment of sialic acid to O-linked glycoproteins and glycolipids. A procedure for the large scale expression of active sialyltransferases from recombinant baculovirus-infected insect cells is described. For the liver enzymes values ofK _m were determined using rat and human asialo₁ acid glycoprotein andN-acetyllactosamine as variable substrates; lacto-N-tetraose was also used with the Gal1-3(4)GlcNAc 2-3sialyltransferase. Antifreeze glycorprotein was used as the macromolecular acceptor for the porcine enzyme. Values forK _m were also determined using CMP-NeuAc as the variable substrate.Abbreviations NeuAc N-acetylneuraminic acid - Gal galactose - GlcNAc N-acetylglucosamine Enzymes: Gal1-4GlcNAc 2-6sialyltransferase, EC 2.4.99.1; Gal1-3(4)GlcNAc 2-3sialyltransferase, EC 2.4.99.5; Gal1-3GalNAc 2-3sialyltransferase, EC 2.4.99.4.     

Strategy for the simulation of batch reactors when the enzyme-catalyzed reaction is accompanied by enzyme deactivation

The balance equations pertaining to the modelling of batch reactors performing an enzyme-catalyzed reaction in the presence of enzyme deactivation are developed. The functional form of the solution for the general situation where both the rate of the enzyme-catalyzed reaction and the rate of enzyme deactivation are dependent on the substrate concentration is obtained, as well as the condition that applies if a maximum conversion of substrate is sought. Finally, two examples of practical interest are explored to emphasize the usefulness of the analysis presented.List of Symbols C _E mol/m³ concentration of active enzyme - C _E,O mol/m³ initial concentration of active enzyme - C _S mol/m³ concentration of substrate - C _S,O mol/m³ initial concentration of substrate - C _S,min mol/m³ minimum value for the concentration of substrate - k 1/s first order rate constant associated with conversion of enzyme/substrate complex into product - k ₁ 1/s first order deactivation constant of enzyme (or free enzyme) - k ₂ 1/s first order deactivation constant of enzyme in enzyme/substrate complex form - K _m mol/m³ Michaelis-Menten constant - p mol/(m³s) time derivative of C _S - q mol/m³ auxiliary variable - t s time elapsed after reactor startup Greek Symbols 1/s univariate function expressing the dependence of the rate of enzyme deactivation on C _S - mol/m³ dummy variable of integration - mol/m³ dummy variable of integration - 1/s univariate function expressing the dependence of the rate of substrate depletion on C _S - m³/(mol s) derivative of with respect to C _S     

Packed-bed immobilized enzyme reactors for complex processes

Utilization of enzymic reactors for biotechnological-biomedical applications is currently developing at a sustained pace.Our present study concentrates on development of procedures for describing the performance of devices where enzyme-catalyzed reactions between two substrates take place, and for the rational design and optimization of the reactors considered. Within this context, an analytical model was developed for immobilized enzyme packed-bed reactors; it takes into account internal diffusion limitations for the cosubstrates, and hydrodynamic backmixing effects. In order to overcome the complex mathematical problems involved, the compartmental analysis approach was employed.Using this model, performance was simulated for various configurations of the enzymic unit, i.e. from a continuously operated stirred tank reactor (CSTR) to an essentially plug flow type. In addition, an experimental method is described for quantitatively assessing the backmixing effects prevailing in the reactor.The procedures established also provide the ground for further developments, particularly for systems where, in parallel to the enzymic reaction, additional processes (e. g. complexation) take place.List of Symbols C _j,i mM Concentration of substrate j in the pores of stage - iD _j cm²/s Internal (pore) diffusion coefficient of substrate j; defined in Eq. (7) - D _e cm²/s Axial dispersion diffusion coefficient - D _j, cm²/s cm²/s Bulk diffusion coefficient for substrate j - E mM Enzyme concentration inside the catalytic pores - J _j,immol/s/cm² Net flux of substrate j taking place from the bulk of stage i into the corresponding pores; defined in Eq. (6) - K _m,1, K _m,2 mM Michaelis-Menten constants for cosubstrates 1 and 2, respectively - k s ^–1 Catalytic constant - k _s cm/s Catalytic constant - n Total number of elementary stages in the reactor - Q cm³/s Volumetric flow rate throught the reactor - r cm Radius of the pore - R _j,i mM/s Reaction rate of substrate j in stage i, in terms of volumetric units - S cm² Internal surface of a pore - S _j,0 mM Concentration of substrate j in the reactor feed - S _j,i–1, S _j,i mM Concentration of substrate j in the bulk phase leaving stages i — 1 and i, respectivley - V _i cm³ Total volume of stage i (bulk phase + pore phase + inert solid carrier) - V cm³ Total volume of the reactor - V _m ^* mmol/s/cm² Maximal reaction rate in terms of surface units; defined in Eq. (8) - V _m mM/s Maximal reaction rate in terms of volumetric units; defined in Eq. (8) - V _p cm³ Volume of one pore - y cm Axial coordinate of the pores - y ₀ cm Depth of the pores - Z cm Axial coordinate of the reactor - Z ₀ cm Length of the reactor - ₁ Dimensionless parameter; defined in Eq. (27) - ₂ Dimensionless parameter; defined in Eq. (27) - ₁ Dimensionless parameter; defined in Eq. (27) - ₂ Dimensionless parameter; defined in Eq. (27) - Ratio between the radius of the enzyme molecule and the radius of the pore (dimensionless) - V₁ Dimensionless parameter; defined in Eq. (21) - v₂ Dimensionless parameter; defined in Eq. (21) - Q Volumetric packing density of catalytic particles (dimensionless) - Ø Porosity of the catalytic particles (dimensionless) - Ø Dimensionless concentration of substrate j in pores of stage i; defined in Eq. (16) - _j,i-1,_j,i Dimensionless concentration of substrate j in the bulk phase of stage i; defined in Eq. (18) - Dimensionless position; defined in Eq. (16) - ² s² Variance; defined in Eq. (33) - Mean residence time in the reactor; defined in Eq. (33)     

On the appropriateness of use of a continuous formulation for the modelling of discrete multireactant systems following Michaëlis–Menten kinetics

The possibility of solving the mass balances to a multiplicity of substrates within a CSTR in the presence of a chemical reaction following Michaelis-Menten kinetics using the assumption that the discrete distribution of said substrates is well approximated by an equivalent continuous distribution on the molecular weight is explored. The applicability of such reasoning is tested with a convenient numerical example. In addition to providing the limiting behavior of the discrete formulation as the number of homologous substrates increases, the continuous formulation yields in general simpler functional forms for the final distribution of substrates than the discrete counterpart due to the recursive nature of the solution in the latter case.List of Symbols C{N. M} mol/m³ concentration of substrate containing N monomer residues each with molecular weight M - {N, M} normalized value of C{N. M} - C {M} mol/m³ da concentration of substrate of molecular weight M - _in normalized value of C {M} at the i-th iteration of a finite difference method - {M} normalized value of C {M} - C ₀{N.M} mol/m³ inlet concentration of substrate containing N monomer residues each with molecular weight M - {N ·M} normalized value of C₀{N. M} - _{0 i} normalized value of C ₀ {M} at the i-th iteration of a finite difference method - C ₀ {M} mol/m³ da initial concentration of substrate of molecular weight M - C _tot mol/m³ (constant) overall concentration of substrates (discrete model) - C _tot mol/m³ (constant) overall concentration of substrates (continuous model) - D deviation of the continuous approach relative to the discrete approach - i dummy integer variable - I arbitrary integration constant - j dummy integer variable - k dummy integer variable - K _m mol/m³ Michaëlis-Menten constant for the substrates - l dummy integer variable - M da molecular weight of substrate - M normalized value of M - M da maximum molecular weight of a reacting substrate - N number of monomer residues of a reacting substrate - N maximum number of monomer residues of a reacting substrate - N total number of increments for the finite difference method - Q m³/s volumetric flow rate of liquid through the reactor - S inert product molecule - S _i substrate containing i monomer residues - V m³ volume of the reactor - v _max mol/m³ s reaction rate under saturating conditions of the enzyme active site with substrate - v _max{N. M} mol/m³ s reaction rate under saturating conditions of the enzyme active site with substrate containing N monomer residues with molecular weight M - _max{N · M} dimensionless value of v_max{N. M} (discrete model) - _max{M} dimensionless value of v _max {M} (continuous model) - mol/m³ s molecular weight-averaged value of v_max (discrete model) - mol.da/m³s molecular weight-averaged value of v_max (continuous model) - v _max {M} mol.da/m³s reaction rate under saturating conditions of the enzyme active site with substrate with molecular weight M - _max {M} dimensionless value of v_max{M} - _{max, (i)} dimensionless value of v_max{M} at the i-th iteration of a finite difference method - v _max mol/m³ s reference constant value of v _max Greek Symbols dimensionless operating parameter (discrete distribution) - dimensionless operating parameter (continuous distribution) - M da (average) molecular weight of a monomeric subunit - M selected increment for the finite difference method - auxiliary corrective factor (discrete model)     

Interrelationships Between Nitrogen Supply and Photosynthetic Parameters in Vicia faba L.

We determined for Vicia faba L the influence of nitrogen uptake and accumulation on the values of photon saturated net photosynthetic rate (P _Nmax), quantum yield efficiency (), intercellular CO₂ concentration (C _i), and carboxylation efficiency (C _e). As leaf nitrogen content (N_L) increased, the converged onto a maximum asymptotic value of 0.0664±0.0049 mol(CO₂) mol(quantum)^–1. Also, as N_L increased the C _i value fell to an asymptotic minimum of 115.80±1.59 mol mol^–1, and C _e converged onto a maximum asymptotic value of 1.645±0.054 mol(CO₂) m^–2 s^–1 Pa^–1 and declined to zero at a N_L-intercept equal to 0.596±0.096 g(N) m^–2. fell to zero for an N_L-intercept of 0.660±0.052 g(N) m^–2. As N_L increased, the value of P _Nmax converged onto a maximum asymptotic value of 33.400±2.563 mol(CO₂) m^–2 s^–1. P _N fell to zero for an N_L-intercept of 0.710±0.035 g(N) m^–2. Under variable daily meteorological conditions the values for N_L, specific leaf area (_L), root mass fraction (R_f), P _Nmax, and remained constant for a given N supply. A monotonic decline in the steady-state value of R_f occurred with increasing N supply. _L increased with increasing N supply or with increasing N_L.     

Molecular basis for erythrocyte Le(a+b+) and salivary ABH partial-secretor phenotypes: expression of a FUT2 secretor allele with an A→T mutation at nucleotide 385 correlates with reducedα(1,2) fucosyltransferase activity

TheSe ^wA385T mutation of the FUT2 gene was found to correlate with both the erthrocyte Le(a+b+) and/or salivary ABH partial-secretor phenotypes of Polynesians. Constructs with FUT1 and FUT2 wild type genes, and the FUT2Se ^wA385T,se ^G428A andse ^C571T mutated alleles, were cloned into pcDNAI, and expressed in COS-7 cells. COS-7 cells transfected with theSe ^wA385T allele had weak, but detectable, (1,2)fucosyltransferase activity, with an acceptor substrate pattern similar to the wild type FUT2 gene. Comparative kinetic studies from cell extracts with mutatedSe ^wA385T and wild type FUT2 alleles gave similarK _m values, but less enzyme activity was present in cells transfected withSe ^wA385T (V _max 230 pmol h^–1 mg^–1), as compared to those transfected with FUT2 (V _max 1030 pmol h^–1 mg^–1), suggesting that the mutated enzyme is more unstable. These results confirm that the molecular basis for the erythrocyte Le(a+b+) and the associated ABH salivary partial-secretor phenotype, is an amino acid change of Ile 129Phe in the secretor (1,2)fucosyltransferase.Abbreviations (1,3/1,4)fucosyltransferase GDP-L-fucose:-D-N-acetylglucosaminide 3/4--L-fucosyltransferase - (1,2)fucosyltransferase GDP-L-fucose: -D-galactoside-2--L-fucosyltransferase - bp base pairs - FUT1 H gene; FUT2,Se gene - FUT3 Lewis gene or Fuc-TIll gene - FUT4 Fuc-TIV gene - FUT5 Fuc-TV gene - FUT6 Fuc-TVI gene - MAb monoclonal antibody - PCR polymerase chain reaction - RFLP restriction fragment length polymorphism - se ^G428A FUT2 nonsecretor GA mutation at nucleotide 428 - se ^C571T FUT2 nonsecretor CT mutation at nucleotide 571 - Se ^wA385T FUT2 secretor weak AT mutation at nucleotide 385 - SSP sequence specific primer