Rapid estimation of activation enthalpies for cytochrome-P450-mediated hydroxylations

Cytochrome P450 (CYP) enzymes play a critical role in detoxication and bioactivation of xenobiotics; thus, the ability to predict the biotransformation rates and regioselectivity of CYP enzymes toward substrates is an important goal in toxicology and pharmacology. Here, we present the use of the semiempirical quantum chemistry method SAM1 to rapidly estimate relative activation enthalpies (ΔH^?) for the hydroxylation of aliphatic carbon centers of various substrates. The ΔH^? were determined via a reaction path calculation, in the reverse direction (RRP), using the iron‐hydroxo‐porphine intermediate and the substrate radical. The SAM1 ΔH^? were calculated via unrestricted Hartree‐Fock (UHF) and configuration interaction (CI) formalisms for both the doublet and quartet spin states. The SAM1 RRP ΔH^?, after subtracting a correction factor, were compared with density functional theory (DFT) B3LYP activation energies for two sets of substrates and showed R² ranging from 0.69 to 0.89, and mean absolute differences ranging from 1.2 ± 1.0 to 1.7 ± 1.5 kcal/mol. SAM1 UHF and CI RRP calculation times were, on average, more than 200 times faster than those for the corresponding forward reaction path DFT calculations. Certain key transition‐state (TS) geometry measurements, such as the forming O···H bond length, showed good correlation with the DFT values. These results suggest that the SAM1 RRP approach can be used to rapidly estimate the DFT activation energy and some key TS geometry measurements and can potentially be applied to estimate substrate hydroxylation rates and regioselectivity by CYP enzymes. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011     

Simultaneous enzymatic kinetic determination of carbamate pesticides with the aid of chemometrics

A method for the simultaneous enzymatic kinetic determination of the pesticides, oxamyl, aldicarb and aminocarb in fruit, vegetables and water samples, has been researched and developed. It was based on enzymatic reaction kinetics and spectrophotometric measurements, and results were interpreted with the aid of chemometrics. The analytical method relies on the inhibitory effect of the pesticides on acetylcholinesterase (AChE), and the use of 5,5′-dithiobis (2-nitrobenzoic) acid (DTNB) as a chromogenic reagent for the thiocholine iodide (TChI) released from the acetylthiocholine iodide (ATChI) substrate. The complex rate equation for the formation of the chromogenic product, P, was solved under certain experimental conditions, and this enabled the absorbance (A _p, at λ _max = 412 nm) from the mixtures of the three pesticide inhibitors to be directly related to their concentrations. The detection limits of the enzymatic kinetic spectrophotometric procedures for the determination of the oxamyl, aldicarb and aminocarb were 0.81, 2.13 and 1.25 ng mL^?1, respectively. Calibration models were constructed for principal component regression (PCR), partial least squares (PLS), and radial basis function-artificial neural network (RBF-ANN), and verified with synthetic samples of the three pesticides. The prediction performance of these models showed generally satisfactory results, and the RBF-ANN one performed slightly better than the other two (RPE_T = 7.59% and average %recovery = 99%). This model was then successfully applied to estimate the amounts of the three compounds in fruit, vegetables and water with satisfactory results.     

Optimizing control of simulated moving beds--linear isotherm

A new optimization based adaptive control strategy for simulated moving beds (SMBs) is proposed. A linearized reduced order model, which accounts for the periodic nature of the SMB process, is used for online optimization and control. The manipulated variables are the four inlet flow rates, the outputs are the raffinate and extract concentrations. Concentration measurements at the raffinate and extract outlets are used as the feedback information. The state estimate from the periodic Kalman filter is used for the prediction of the outlet concentrations over a chosen horizon. Predicted outlet concentrations are the basis for the calculation of the optimal input adjustments, which maximize the productivity and minimize the desorbent consumption subject to constraints on product purities. The realization of this concept is discussed and the implementation on a virtual eight column SMB platform is assessed, in the case of binary linear systems. For a whole series of typical plant disturbances it is shown that the proposed approach is effective in minimizing off-spec products and in achieving optimal SMB operation, also in the case where there are significant model uncertainties.     

Kinetic Modeling of Ethanol Production by Scheffersomyces stipitis from Xylose

This work focuses on the kinetics of ethanol production by Scheffersomyces stipitis on xylose with the development of a mathematical model considering the effect of substrate and product concentrations on growth rate. Experiments were carried out in batch and continuous modes, with substrate concentration varying from 7.2 to 145 g L^?1. Inhibitory effects on cell growth, substrate uptake, and ethanol production rates were found to be considerable. Kinetic parameters were obtained through linear and non-linear regression methods. Experiments in continuous mode were performed at different dilution rates to evaluate the inhibitory effect of ethanol. A mixed mathematical model which combined Andrews and Levenspiel's models, combining substrate and product inhibition, was used. A quasi-Newton routine was applied to obtain a more accurate fitting of kinetic parameters. The parameters such as cell to product factor (Y _P/X) and limiting cell yield (Y _X) were shown to be dependent on substrate concentration. The kinetic model fitted satisfactorily the experimental data.     

Evolving chemometric models for predicting dynamic process parameters in viscose production

In viscose production, it is important to monitor three process parameters in order to assure a high quality of the final product: the concentrations of H₂SO₄, Na₂SO₄ and Z_nSO₄. During on-line production these process parameters usually show a quite high dynamics depending on the fiber type that is produced. Thus, conventional chemometric models, which are trained based on collected calibration spectra from Fourier transform near infrared (FT-NIR) measurements and kept fixed during the whole life-time of the on-line process, show a quite imprecise and unreliable behavior when predicting the concentrations of new on-line data. In this paper, we are demonstrating evolving chemometric models which are able to adapt automatically to varying process dynamics by updating their inner structures and parameters in a single-pass incremental manner. These models exploit the Takagi–Sugeno fuzzy model architecture, being able to model flexibly different degrees of non-linearities implicitly contained in the mapping between near infrared spectra (NIR) and reference values. Updating the inner structures is achieved by moving the position of already existing local regions and by evolving (increasing non-linearity) or merging (decreasing non-linearity) new local linear predictors on demand, which are guided by distance-based and similarity criteria. Gradual forgetting mechanisms may be integrated in order to out-date older learned relations and to account for more flexibility of the models. The results show that our approach is able to overcome the huge prediction errors produced by various state-of-the-art chemometric models. It achieves a high correlation between observed and predicted target values in the range of [0.95,0.98] over a 3 months period while keeping the relative error below the reference error value of 3%. In contrast, the off-line techniques achieved correlations below 0.5, ten times higher error rates and the more deteriorate, the more time passes by.     

A hybrid neural model of ethanol production by Zymomonas mobilis

A hybrid neural model was developed for the alcoholic fermentation by Zymomonas mobilis. This model is composed by the mass-balance equations of the process and neural networks, which describe the kinetic rates. Strategies that combines scarce experimental data with approximate models of the process were used to generate new data for the training of the networks, minimizing the number of experiments required. The proposed hybrid neural methodology uses all the information avail able about the process to deal with the difficulties in the development of the model.     

Multicomponent adsorption modeling: isotherms for ABE model solutions using activated carbon F-400

Biobutanol has attracted significant interest in recent decades and is seriously considered as a potential biofuel to partly replace gasoline. However, some production challenges must be addressed to make butanol economically viable such as the low product concentration and product toxicity inhibiting the microorganism. To alleviate these limitations, several in situ or ex situ separation techniques have been investigated in view of their integration to the biobutanol production process to enhance its economic viability. One of these techniques is adsorption which is one of the most energy-efficient techniques used for biobutanol separation. Considering the number of chemical species present in the ABE fermentation broth, it is essential to develop multicomponent adsorption isotherms for all components as a first step to design a high performance adsorption process. Few multicomponent isotherm models have been proposed such as multicomponent Langmuir and Freundlich. In this study, these two models as well as artificial neural networks were used to model the isotherms of each component in an ABE fermentation broth as a function of the equilibrium concentrations of all components for activated carbon F-400. Results showed that the multicomponent Langmuir model was not accurate due to the many simplifying assumptions. The multicomponent Freundlich and feedforward neural network (FFNN) isotherm models were able to predict the behavior of multicomponent systems very well. Indeed, the predictive model of the experimental data had a coefficient of determination (R²) of 0.97 and 0.99, for multicomponent Freundlich and FFNN isotherm models, respectively.     

Heterogeneous modeling for the alcoholic fermentation process

In this paper a heterogeneous model is developed for the alcoholic fermentation process. The model is expressed in terms of intracellular and extracellular concentrations of ethanol and sugar as well as biomass concentration as state variables. The model takes into consideration the floc size and the mass transfer rates of both ethanol and sugar. The intrinsic kinetics of the process used in the model was developed from published data which includes the inhibitory effects of ethanol and cells. The model development is achieved through comparison with one set of experimental results given by Novak et al. (1). The model is then checked against two other sets of experimental results. The developed model is also used to simulate an industrial fed-batch fermentar.     

Effects of Substrate Loading on Enzymatic Hydrolysis and Viscosity of Pretreated Barley Straw

In this study, the applicability of a “fed-batch” strategy, that is, sequential loading of substrate or substrate plus enzymes during enzymatic hydrolysis was evaluated for hydrolysis of steam-pretreated barley straw. The specific aims were to achieve hydrolysis of high substrate levels, low viscosity during hydrolysis, and high glucose concentrations. An enzyme system comprising Celluclast and Novozyme 188, a commercial cellulase product derived from Trichoderma reesei and a β-glucosidase derived from Aspergillus niger, respectively, was used for the enzymatic hydrolysis. The highest final glucose concentration, 78 g/l, after 72 h of reaction, was obtained with an initial, full substrate loading of 15% dry matter weight/weight (w/w DM). Conversely, the glucose yields, in grams per gram of DM, were highest at lower substrate concentrations, with the highest glucose yield being 0.53 g/g DM for the reaction with a substrate loading of 5% w/w DM after 72 h. The reactions subjected to gradual loading of substrate or substrate plus enzymes to increase the substrate levels from 5 to 15% w/w DM, consistently provided lower concentrations of glucose after 72 h of reaction; however, the initial rates of conversion varied in the different reactions. Rapid cellulose degradation was accompanied by rapid decreases in viscosity before addition of extra substrate, but when extra substrate or substrate plus enzymes were added, the viscosities of the slurries increased and the hydrolytic efficiencies decreased temporarily.     

A Dynamic Model for Cellulosic Biomass Hydrolysis: a Comprehensive Analysis and Validation of Hydrolysis and Product Inhibition Mechanisms

The objective of this study is to perform a comprehensive enzyme kinetics analysis in view of validating and consolidating a semimechanistic kinetic model consisting of homogeneous and heterogeneous reactions for enzymatic hydrolysis of lignocellulosic biomass proposed by the U.S. National Renewable Energy Laboratory (Kadam et al., Biotechnol Prog 20(3):698–705, 2004) and its variations proposed in this work. A number of dedicated experiments were carried out under a range of initial conditions (Avicel® versus pretreated barley straw as substrate, different enzyme loadings and different product inhibitors such as glucose, cellobiose and xylose) to test the hydrolysis and product inhibition mechanisms of the model. A nonlinear least squares method was used to identify the model and estimate kinetic parameters based on the experimental data. The suitable mathematical model for industrial application was selected among the proposed models based on statistical information (weighted sum of square errors). The analysis showed that transglycosylation plays a key role at high glucose levels. It also showed that the values of parameters depend on the selected experimental data used for parameter estimation. Therefore, the parameter values are not universal and should be used with caution. The model proposed by Kadam et al. (Biotechnol Prog 20(3):698–705, 2004) failed to predict the hydrolysis phenomena at high glucose levels, but when combined with transglycosylation reaction(s), the prediction of cellulose hydrolysis behaviour over a broad range of substrate concentrations (50–150 g/L) and enzyme loadings (15.8–31.6 and 1–5.9 mg protein/g cellulose for Celluclast and Novozyme 188, respectively) was possible. This is the first study introducing transglycosylation into the semimechanistic model. As long as these type of models are used within the boundary of their validity (substrate type, enzyme source and substrate concentration), they can support process design and technology improvement efforts at pilot and full-scale studies.     

A mathematical model of ethanol fermentation from cheese whey

The cybernetic approach to modeling of microbial kinetics in a mixedsubstrate environment has been used in this work for the fermentative production of ethanol from cheese whey. In this system, the cells grow on multiple substrates and generate metabolic energy during product formation. This article deals with the development of a mathematical model in which the concept of cell maintenance was modified in light of the specific nature of product formation. Continuous culture data for anaerobic production of ethanol byKluyveromyces marxianus CBS 397 on glucose and lactose were used to estimate the kinetic parameters for subsequent use in predicting the behavior of microbial growth and product formation in new situations.     

Spectral Modeling of UV Inhibition of In Situ Antarctic Primary Production Using A Field-Derived Biological Weighting Function

Abstract— Our major aim is to illustrate an approach for hindcasting or forecasting UV radiation (UVR, 280–400 nm) effects on in situ rates of aquatic primary production when field measurements do not include estimates of UVR effects. A composite of spectral field measurements is employed to model UVR-dependent rates of photosynthesis in diatomdominated waters in a coastal region of the Southern Ocean. Assumptions, caveats and limitations of the modeling are discussed. Calculations begin with the 1991 Palmer Long Term Ecological Research (LTER) primary production and optical databases, from which daily integrated rates of carbon fixation in the absence of UVR are calculated as a function of depth for a 140 km transect line sampled between dawn and dusk of a single day (14 November 1991). The UVR measurements from the nearby NSF/OPP Polar Network at Palmer Station are used to determine ozone (O₃) concentration on the day of the transect, which is then employed in Madronich's (In UV-B Radiation and Ozone Depletion (Edited by M. Tevini), pp. 17–68. Lewis, Boca Raton, FL, 1993) spectral code to model daytime variations in surface spectral irradiances under clear sky conditions. These data are corrected for cloudiness and then combined with estimates of in-water UVR spectral attenuation coefficients, derived from Icecolors 90 data, to estimate in situ light exposure for phytoplankton collected at different depths and locations. An absolute chlorophyllspecific biological weighting function (BWF), determined under natural solar light fields for Antarctic diatom communities and shown to be reproducible while differing from a laboratory diatom BWF and other in situ BWF determined for other phytoplankton assemblages, is combined with estimates of in situ UVR exposure to derive in situ estimates of chlorophyllspecific losses of carbon fixation due to UVR inhibittion. By repeating calculations for every sampling site along the transect, we derive a spatial map of estimated UVR effects on primary production across the region. We repeat calculations for different O, concentrations expected during the austral spring over Antarctica and illustrate the O, dependency of UVB (280–320 nm) inhibition effects in near surface waters. We estimate ambient UVR reduced carbon fixation rates up to 65% in surface waters, depending upon location, down to undetectable levels at 36 m. Reducing stratospheric O₃ concentrations by 50% further inhibits near surface primary production by 8% and integrated primary production by 5%. Primary production was forced to subsurface maxima across the entire transect line in the presence of UVR.     

Toward Cost‐Effective Manufacturing of Silicon Solar Cells: Electrodeposition of High‐Quality Si Films in a CaCl2‐based Molten Salt

Electrodeposition of Si films from a Si‐containing electrolyte is a cost‐effective approach for the manufacturing of solar cells. Proposals relying on fluoride‐based molten salts have suffered from low product quality due to difficulties in impurity control. Here we demonstrate the successful electrodeposition of high‐quality Si films from a CaCl₂‐based molten salt. Soluble Si^IV−O anions generated from solid SiO₂ are electrodeposited onto a graphite substrate to form a dense film of crystalline Si. Impurities in the deposited Si film are controlled at low concentrations (both B and P are less than 1 ppm). In the photoelectrochemical measurements, the film shows p‐type semiconductor character and large photocurrent. A p–n junction fabricated from the deposited Si film exhibits clear photovoltaic effects. This study represents the first step to the ultimate goal of developing a cost‐effective manufacturing process for Si solar cells based on electrodeposition.     

On the accuracy of the hydroperoxide method of determining absolute rate constants for hydrogen atom abstraction by the tert-butylperoxy radical

Measurements of rates of oxygen absorption and steady-state peroxy radical concentrations for the autoxidation of tetralin in the presence of tert-butyl hydroperoxide have shown that the rate constant for reaction of the tert-butylperoxy radical with tetralin at 60°C is approximately 11.0 M^?1 s^?1. This rate constant is about a factor of 4 larger than the value recently reported by Niki, Okayasu, and Kamiya for this reaction. The present work emphasizes that great care should be taken when the hydroperoxide method is used to estimate cross-propagation rate constants for a substrate as reactive as tetralin at a temperature as high as 60°C.     

Determination of long-lived fission products and actinides in Savannah River Site HLW sludge and glass for waste acceptance

Savannah River Site (SRS) is immobilizing the radioactive, high-level waste sludge in Tank 51 into a borosilicate glass for disposal in a geologic repository. A requirement for repository aceeptance is that SRS report the concentrations of certain fission product and actinide radionuclides in the glass. This paper presents measurements of many of these concentrations in both Tank 51 sludge and the final glass. The radionuclides were measured by inductively coupled plasma mass spectrometry and α, β, and γ counting methods. Examples of the radionuclides are⁹⁰Sr,¹³⁷Cs,²³⁸U and,²³⁹Pu. Concentrations in the glass are 3.1 times lower due to dilution of the sludge with a nonradio-active glass forming frit in the vitrification process. Results also indicated that in both the sludge and glass the relative concentrations of the long lived fission products insoluble in caustic are in proportion to their yields from the fission of²³⁵U waste in the SRS reactors. This allowed the calculation of a fission yield scaling factor. This factor in addition to the sludge dilution factor can be used to estimate concentrations of waste acceptance radionuclides that cannot be measured in the glass. Examples of these radionuclides are⁷⁹Se,⁹³Zr, and¹⁰⁷Pd.     

Lipid analysis of flat-mounted eye tissue by imaging mass spectrometry with identification of contaminants in preservation

Matrix-assisted laser desorption/ionization imaging mass spectrometry was used to analyze donor eye tissue specimens for phospholipid content to evaluate lipid distribution. Phosphatidylcholines and sphingomyelins were detected in the positive ion mode using 2,5-dihydroxybenzoic acid as the matrix. During this study, unknown ion signals in the lower m/z region (less than m/z 400) were detected, mainly in the far periphery of human flat-mounted tissue but not in age-matched rhesus monkey tissue prepared in a similar manner. The unknown ion signals occurred at m/z 304, 332, 360, and 388. These ions were subjected to tandem mass spectrometry directly from the tissue sample, and exact mass measurements of extracts were prepared for further identification. These ions were identified as alkyl dimethylbenzylammonium surfactants (benzalkonium chlorides (BACs)). The classification of these species was verified by comparing an eye tissue extract to an over-the-counter eye-care product containing BACs.     

Polarization induced electrospray ionization mass spectrometry for the analysis of liquid,viscous and solid samples

In this study, a polarization‐induced electrospray ionization mass spectrometry (ESI‐MS) was developed. A micro‐sized sample droplet was deposited on a naturally available dielectric substrate such as a fruit or a stone, and then placed close to (~2 mm) the orifice of a mass spectrometer applied with a high voltage. Taylor cone was observed from the sample droplet, and a spray emitted from the cone apex was generated. The analyte ion signals derived from the droplet were obtained by the mass spectrometer. The ionization process is similar to that in ESI although no direct electric contact was applied on the sample site. The sample droplet polarized by the high electric field provided by the mass spectrometer initiated the ionization process. The dielectric sample loading substrate facilitated further the polarization process, resulting in the formation of Taylor cone. The mass spectral profiles obtained via this approach resembled those obtained using ESI‐MS. Multiply charged ions dominated the mass spectra of peptides and proteins, whereas singly charged ions dominated the mass spectra of small molecules such as amino acids and small organic molecules. In addition to liquid samples, this approach can be used for the analysis of solid and viscous samples. A small droplet containing suitable solvent (5–10 µl) was directly deposited on the surface of the solid (or viscous) sample, placed close the orifice of mass spectrometer applied with a high voltage. Taylor cone derived from the droplet was immediately formed followed by electrospray processes to generate gas‐phase ions for MS analysis. Analyte ions derived from the main ingredients of pharmaceutical tablets and viscous ointment can be extracted into the solvent droplet in situ and observed using a mass spectrometer. Copyright © 2015 John Wiley & Sons, Ltd.     

Analyses of the temperature and pH effects on the complexation of magnesium and calcium in human blood plasma: an approach using artificial neural networks

The temperature and pH effects on the equilibrium of a blood plasma model have been studied on the basis of artificial neural networks. The proposed blood plasma was modeled considering two important metals, calcium and magnesium, and six ligands, namely, alanate, carbonate, citrate, glycinate, histidinate and succinate. A large data set has been used to simulate different concentrations of magnesium and calcium as a function of temperature and pH and these data were used for training the neural network. The proposed model allowed different types of analyses, such as the effects of pH on calcium and magnesium concentrations, the competition between calcium and magnesium for ligands and the effects of temperature on calcium and magnesium concentrations. The model developed was also used to predict how the variation of calcium concentration can affect magnesium concentrations. A comparison of neural network predictions against experimental data produced errors of about 3%. Moreover, in agreement with experimental measurements (Wang et al. in Arch. Pathol. 126:947–950, 2002; Heining et al. in Scand. J. Clin. Lab. Invest. 43:709–714, 1983), the artificial neural network predicted that calcium and magnesium concentrations decrease when pH increases. Similarly, the magnesium concentrations are less sensitive than calcium concentrations to pH changes. It is also found that both calcium and magnesium concentrations decrease when the temperature increases. Finally, the theoretical model also predicted that an increase of calcium concentrations will lead to an increase of magnesium concentration almost at the same rate. These results suggest that artificial neural networks can be efficiently applied as a complementary tool for studying metal ion complexation, with especial attention to the blood plasma analysis. Figure Artificial neural networks for predicting the behavior of calcium and magnesium as a function of pH and temperature in human blood plasma     

Determination of ion structures in structurally related compounds using precursor ion fingerprinting

Structurally-related alkaloids were analyzed by electrospray ionization/multiple stage mass spectrometry (ESI/MS ⁿ ) at varying collision energies to demonstrate a conceptual algorithm, precursor ion fingerprinting (PIF). PIF is a new approach for interpreting and library-searching ESI mass spectra predicated on the precursor ions of structurally-related compounds and their matching product ion spectra. Multiple-stage mass spectra were compiled and constructed into “spectral trees” that illustrated the compounds’ product ion spectra in their respective mass spectral stages. The precursor ions of these alkaloids were characterized and their spectral trees incorporated into an MS ⁿ library. These data will be used to construct a universal, searchable, and transferable library of MS ⁿ spectra. In addition, PIF will generate a proposed structural arrangement utilizing previously characterized ion structures, which will assist in the identification of unknown compounds.