Estimation Of Kinetic Parameters Research Articles

Mathematical Modeling and Simulation of 1,3-Propanediol Production by Klebsiella pneumoniae BLh-1 in a Batch Bioreactor Using Bayesian Statistics.

Mathematical modeling and computer simulation are fundamental for optimizing biotechnological processes, enabling cost reduction and scalability, thereby driving advancements in the bioindustry. In this work, mathematical modeling and estimation of fermentative kinetic parameters were carried out to produce 1,3-propanediol (1,3-PDO) from residual glycerol and Klebsiella pneumoniae BLh-1. The Markov chain Monte Carlo method, using the Metropolis-Hastings algorithm, was applied to experimental data from a batch bioreactor under aerobic and anaerobic conditions. Sensitivity analysis and parameter evolution studies were conducted. The root-mean-square error (rRMSE) was chosen as the validation and calibration metric for the developed mathematical model. The results indicated that the average tolerance of glycerol was 174.68 and 44.85 g L-1, the inhibitory products was 150.95 g L-1 for ethanol and 35.56 g L-1 for 1,3-PDO, and the maximum specific rate of cell growth was 0.189 and 0.275 h-1, for aerobic and anaerobic cultures, respectively. The model presented excellent fits in both crops, with rRMSE values between 0.09 - 33.74% and 3.58 - 31.82%, for the aerobic and anaerobic environment, respectively. With this, it was possible to evaluate and extract relevant information for a better understanding and control of the bioprocess.

Metal–Organic Framework-Derived CeO2/Gold Nanospheres in a Highly Sensitive Electrochemical Sensor for Uric Acid Quantification in Milk

In this work, CeBTC (a cerium(III) 1,3,5-benzene-tricarboxylate), was used as a precursor for obtaining CeO2 nanoparticles (nanoceria) with better sensor performances than CeO2 nanoparticles synthesized by the solvothermal method. Metal–organic framework-derived nanoceria (MOFdNC) were functionalized with spheric gold nanoparticles (AuNPs) to further improve non-enzymatic electrode material for highly sensitive detection of prominent biocompound uric acid (UA) at this modified carbon paste electrode (MOFdNC/AuNPs&CPE). X-ray powder diffraction (XRPD) and transmission electron microscopy (TEM) analysis were used for morphological structure characterization of the obtained nanostructures. Cyclic voltammetry and electrochemical impedance spectroscopy, both in an [Fe(CN)6]3−/4− redox system and uric acid standard solutions, were used for the characterization of material electrocatalytic performances, the selection of optimal electrode modifier, and the estimation of nature and kinetic parameters of the electrode process. Square-wave voltammetry (SWV) was chosen, and the optimal parameters of technique and experimental conditions were established for determining uric acid over MOFdNC/AuNPs&CPE. Together with the development of the sensor, the detection procedure was optimized with the following analytical parameters: linear operating ranges of 0.05 to 1 µM and 1 to 50 µM and a detection limit of 0.011 µM, with outstanding repeatability, reproducibility, and stability of the sensor surface. Anti-interference experiments yielded a stable and nearly unchanged current response with negligible or no change in peak potential. After minor sample pretreatment, the proposed electrode was successfully applied for the quantification of UA in milk.

Maximizing similarity: using correlation coefficients to calibrate kinetic parameters in population balance models

Crystallization plays a crucial role as a separation and purification technique, particularly in the chemical and pharmaceutical industries. By adjusting process parameters, the productivity, product quality, and efficiency of downstream processes can be improved. In complex processes, model-based design becomes invaluable. Population Balance Models (PBMs) have successfully aided the chemical industry in achieving more effective production processes for decades. These models can utilize various input data sources to identify the dominant mechanisms and calibrate model parameters. While inline particle monitoring tools serve as excellent qualitative descriptors, challenges arise from experimentation and data interpretation, hindering their direct application in the kinetic parameter estimation of PBMs. In this study, we present a novel approach that utilizes information from inline particle monitoring tools for the kinetic parameter estimation of PBMs, bypassing the associated obstacles. Our pioneering approach relies on offline product size data and the correlation-based utilization of inline particle monitoring information. The paper compares this novel strategy with two parameter estimation techniques: the classical method using solute concentration and product size data and a somewhat naïve approach, which assumes that the inline particle monitoring data can directly be compared with the simulations. The prediction capabilities are evaluated through two in-silico case studies. The results indicate that the precision and predictive capability of the correlation-based technique are comparable to the classical approach, both for noisy data and for a system undergoing significant agglomeration and deagglomeration. The use of Pearson’s correlation coefficient yields the best results in the novel cases. These findings in in-silico datasets provide a foundation and motivation for the practical application of this idea, unleashing the so-far hidden model development potential of such measurements.

Automatization of theoretical kinetic data generation for tabulated TS models building - Part 1: Application to 1,3-H-shift reactions

Estimation of kinetic parameters is a key aspect of chemical combustion modeling and several approaches were developed to approximate unknown data. In this work, a code in Python was developed to build tables of transition state (TS) models automatically for intramolecular H-shift reactions in alkyl radicals. The code generates the kinetic rules for all the possible combinations of methyl-substituted reactions based on the structures of the minimal, non-substituted, reactant, TS, and product. The code is able to create and differentiate multiple transition state configurations, considering the axial and equatorial positions for the cyclic substituents and including all the possible pathways for the reactions, which is shown to be an important feature in performing accurate automatic kinetic calculations. Each structure is automatically submitted to geometry optimization and electronic energy calculations, as well as the relaxed scans of the torsional modes identified by the code. From the results of electronic calculations, the rate constants for each pathway are obtained automatically by the application of the transition state theory with tunneling corrections, in a defined temperature range. The kinetic coefficients, as well as the modified Arrhenius parameters, are then assembled and organized to create a final table that connects the kinetic data with TS structure characteristics. These tables can be directly applied as a kinetic data source for reaction mechanism development. The ability of the code to generate reliable rate constants was tested for 1,3-H-shift reactions and the results were compared with theoretical data manually produced, and showed a good agreement. In particular, the code was able to create all the transition state configurations, with an exhaustive description of all possible reaction pathways, using a rigorous and systematic counting based on symmetry, stereocenters, and diastereomers. The proposed method leads to more accurate results on these aspects, compared to repetitive hand calculations of dozens of rate constants.

A Python tool for parameter estimation of “black box” macro- and micro-kinetic models with Bayesian optimization – petBOA

We develop an open-source Python-based Parameter Estimation Tool utilizing Bayesian Optimization (petBOA) with a unique wrapper interface for gradient-free parameter estimation of expensive black-box kinetic models. We provide examples for Python macrokinetic and microkinetic modeling (MKM) tools, such as Cantera and OpenMKM. petBOA leverages surrogate Gaussian processes to approximate and minimize the objective function designed for parameter estimation. Bayesian Optimization (BO) is implemented using the open-source BoTorch toolkit. petBOA employs local and global sensitivity analyses to identify important parameters optimized against experimental data, and leverages pMuTT for consistent kinetic and thermodynamic parameters while perturbing species binding energies within the typical error of conventional DFT exchange-correlation functionals (20-30 kJ/mol). The source code and documentation are hosted on GitHub. Program summaryProgram title: petBOACPC Library link to program files:https://doi.org/10.17632/hwwvksbb75.1Developer's repository link: https://github.com/VlachosGroup/petBOALicensing provisions: MIT licenseProgramming language: PythonExternal routines: NEXTorch, PyTorch, GPyTorch, BoTorch, Matplotlib, PyDOE2, NumPy, SciPy, pandas, pMuTT, SALib, docker.Nature of the problem: An open-source, gradient-free parameter estimation of black-box microkinetic modeling tools, such as OpenMKM is lacking.Solution method: petBOA is a Python-based tool that utilizes Bayesian Optimization and offers a unique wrapper interface for expensive black-box kinetic models. It leverages the pMuTT library for consistent kinetic and thermodynamic parameter estimation and employs both local and global sensitivity analyses to identify crucial parameters.

Kinetic insights into heavy crude oil upgrading in supercritical water

Heavy crude oil upgrading in a supercritical water environment is a promising technology owing to diverse advantages such as heteroatom removal, high yields of low molecular weight fractions, and inhibition of coke production. In order to increase the performance of this process, some challenges need to be overcome by researching the reaction mechanisms and developing proper numerical models. Therefore, the kinetic studies reported in the literature for hydrothermal upgrading of heavy crude oil under supercritical water conditions and their results were systematically analyzed and discussed to achieve a better understanding of the diverse approaches considered to determine the distribution of reaction products and limitations. Additionally, fundamental aspects of the kinetic models, including experimental considerations and numerical methodologies applied for kinetic parameter estimation, are reviewed to obtain representative information about the reactant system that can be subsequently integrated into reactor design models or reservoir simulations.

Predicting reaction rate constants of organic compounds with oxidants in the atmospheric aqueous-phase through multi-task learning

The atmospheric aqueous-phase chemistry has received increasing attention in the last decades for its non-negligible environmental significance. Yet, the insufficient experimental data on oxidative reaction rate constants (kaq) obstructs the further analysis and modeling of this system. Predictive models based on machine learning (ML) algorithms have shown potential as an effective estimation tool, however, they are restricted to the lack of training data as well. To overcome this data limitation, we developed multi-task (MT) models that could exploit the common knowledge from reactions in gas- and aqueous-phases simultaneously. Toward kaq of organic compounds with hydroxyl radical (OH), nitrate radical (NO3), and ozone (O3), the MT models showed a notably better predictive ability compared to benchmark models, while obtaining wide applicability on compounds from different chemical classes. By interpreting the models using Shapley additive explanations (SHAP), we evidenced that the MT models utilized the common knowledge in both phases and correctly identified the reaction mechanisms. This study aims to provide new insight into the estimation of necessary kinetic parameters in atmospheric aqueous-phase chemistry, as well as a reference to ML research for other predictive tasks of atmospheric interest.

Elucidating the impact of non-spherical morphology on kinetic behavior of graphite using single-particle microelectrode

The precise assessment of the kinetic properties of graphite is crucial for the design of the material and the development of electrochemical models for lithium-ion batteries. However, conventional evaluation methods using composite electrodes fall short in excluding the influence of its porous structure and evaluating the influence of particle shape on kinetic behavior. In this work, we elucidate the impact of non-spherical morphology on kinetic behavior of single artificial graphite particle, employing the single-particle microelectrode technique. Three single artificial graphite particles with different sizes are pre-cycled and exhibit outstanding rate capability, maintaining a maximum capacity retention of 78.1 % at 200 C. Further, exchange current density and diffusion coefficients are determined through Tafel plots and the potentiostatic intermittent titration technique (PITT), respectively. It is found that the particle with a higher exposure of edge planes present elevated exchange current density and diffusion coefficients. Considering that the graphite particles typically feature an ellipsoidal rather than spherical shape, we propose an approach based on an ellipsoidal diffusion model for extracting diffusion coefficients. This model takes into account the non-spherical morphology of graphite, addressing the limitations inherent in the geometric assumptions of previous methods. The average relative error between the diffusion coefficients derived from the ellipsoidal diffusion model and the previous method using spherical assumptions is 215 %, indicating that accurately depicting the shape of ellipsoidal graphite particles in the model is crucial for obtaining correct estimates of kinetic parameters. This study offers direct experimental evidence of superior kinetic behavior of edge planes over basal planes. To leverage this characteristic, it is recommended to employ an out-of-plane aligned architecture for composite electrodes using novel techniques, e.g., 3D printing or magnetic alignment techniques.

Total-Body Dynamic Imaging and Kinetic Modeling of [18F]F-AraG in Healthy Individuals and a Non-Small Cell Lung Cancer Patient Undergoing Anti-PD-1 Immunotherapy.

Immunotherapies, especially checkpoint inhibitors such as anti-programmed cell death protein 1 (anti-PD-1) antibodies, have transformed cancer treatment by enhancing the immune system's capability to target and kill cancer cells. However, predicting immunotherapy response remains challenging. 18F-arabinosyl guanine ([18F]F-AraG) is a molecular imaging tracer targeting activated T cells, which may facilitate therapy response assessment by noninvasive quantification of immune cell activity within the tumor microenvironment and elsewhere in the body. The aim of this study was to obtain preliminary data on total-body pharmacokinetics of [18F]F-AraG as a potential quantitative biomarker for immune response evaluation. Methods: The study consisted of 90-min total-body dynamic scans of 4 healthy subjects and 1 non-small cell lung cancer patient who was scanned before and after anti-PD-1 immunotherapy. Compartmental modeling with Akaike information criterion model selection was used to analyze tracer kinetics in various organs. Additionally, 7 subregions of the primary lung tumor and 4 mediastinal lymph nodes were analyzed. Practical identifiability analysis was performed to assess the reliability of kinetic parameter estimation. Correlations of the SUVmean, the tissue-to-blood SUV ratio (SUVR), and the Logan plot slope (K Logan) with the total volume of distribution (V T) were calculated to identify potential surrogates for kinetic modeling. Results: Strong correlations were observed between K Logan and SUVR with V T, suggesting that they can be used as promising surrogates for V T, especially in organs with a low blood-volume fraction. Moreover, practical identifiability analysis suggested that dynamic [18F]F-AraG PET scans could potentially be shortened to 60 min, while maintaining quantification accuracy for all organs of interest. The study suggests that although [18F]F-AraG SUV images can provide insights on immune cell distribution, kinetic modeling or graphical analysis methods may be required for accurate quantification of immune response after therapy. Although SUVmean showed variable changes in different subregions of the tumor after therapy, the SUVR, K Logan, and V T showed consistent increasing trends in all analyzed subregions of the tumor with high practical identifiability. Conclusion: Our findings highlight the promise of [18F]F-AraG dynamic imaging as a noninvasive biomarker for quantifying the immune response to immunotherapy in cancer patients. Promising total-body kinetic modeling results also suggest potentially wider applications of the tracer in investigating the role of T cells in the immunopathogenesis of diseases.

Adsorption of methane by modified-biochar aiming to improve the gaseous fuels storage/transport capacity: process evaluation and modeling.

The CH4 storage by adsorption on activated carbons for natural gas handling has gained interest due to the appearance of lightweight materials with large surface areas and pore volumes. Consequently, kinetic parameters estimation of the adsorptive process can play a crucial role in understanding and scaling up the system. Concerning its versatility, banana peel (BP) is a biomass with potential for obtaining different products, such as biochar, a solid residue from the biomass' thermal decomposition of difficult disposal, where through an activation process, the material porous features are taken advantage to application as adsorbent of gaseous substances. This research reported data for the CH4 adsorption kinetic modeling by biochar from BP pyrolysis. The activated biochar textural characterization showed particles with fine mesoporous structure (pore diameter ranging between 29.39 and 55.62Å). Adsorption kinetic analysis indicated that a modified pseudo-first-order model was the most suitable to represent the experimental data, with equilibrium adsorption of 28mgg-1 for the samples activated with 20.0% vol wt.-1 of H3PO4 and pyrolysis at 500°C. The equilibrium constant was consistent with the Freundlich isotherm model, suggesting a physisorption mechanism, and led to a non-ideal, reversible, and not limited to monolayer CH4 adsorption.

Intrinsic kinetics of steam methane reforming over a monolithic nickel catalyst in a fixed bed reactor system

The intrinsic kinetics of steam methane reforming were experimentally investigated using a monolithic nickel-based catalyst in a specially designed fixed bed system. All kinetic tests were performed under various operating conditions: steam to carbon ratio of 2 to 5, methane volumetric concentration in feed gas of 3 % to 9 %, temperature of 500 to 650 °C, and typical biogas compositions with N2 dilution (carbon dioxide 35.7–55.6 %, methane 44.4–64.3 %, with 86–91 % nitrogen dilution). A deconvolution process was applied during the rate calculation and the reaction orders with respect to different gases were determined, coupled with thermodynamic system analysis. The reaction mechanism was then proposed and a Langmuir-Hinshelwood-Hougen-Watson (LHHW) kinetic model was established, followed by kinetic parameter estimations for the model and a further model validation with experimental data. Based on the model, the intrinsic kinetic of SMR reaction using monolithic catalysts was described. The validity and feasibility of applying the faster approach for kinetic parameter estimation were demonstrated, and this work is the first demonstration of a complex reaction system. Also, the reaction mechanism appeared to show a suitably high correlation with alternative and more sustainable methane sources (i.e. biogas).

A Comparison of Arterial Input Function Interpolation Methods for Patlak Plot Analysis of 68Ga-PSMA-11 PET Prostate Cancer Studies.

Positron emission tomography (PET) imaging enables quantitative assessment of tissue physiology. Dynamic pharmacokinetic analysis of PET images requires accurate estimation of the radiotracer plasma input function to derive meaningful parameter estimates, and small discrepancies in parameter estimation can mimic subtle physiologic tissue variation. This study evaluates the impact of input function interpolation method on the accuracy of Patlak kinetic parameter estimation through simulations modeling the pharmacokinetic properties of [68Ga]-PSMA-11. This study evaluated both trained and untrained methods. Although the mean kinetic parameter accuracy was similar across all interpolation models, the trained node weighting interpolation model estimated accurate kinetic parameters with reduced overall variability relative to standard linear interpolation. Trained node weighting interpolation reduced kinetic parameter estimation variance by a magnitude approximating the underlying physiologic differences between normal and diseased prostatic tissue. Overall, this analysis suggests that trained node weighting improves the reliability of Patlak kinetic parameter estimation for [68Ga]-PSMA-11 PET.

Estimation of kinetic and thermodynamic parameters during thermal decomposition of raw, boiled, and torrefied banana peel through arrhenius and CR methods

The objective of the present manuscript was to investigate the bioenergy generation potential of banana peel as solid or liquid biofuel, by examining physicochemical, kinetic, as well as thermodynamic parameters. Raw banana peel, boiled banana peel, and torrefied banana peel were subjected to a thermal decomposition environment. BBP was prepared by boiling RBP in a hot plate at 120 °C using 600 rpm, for 150 min. Besides, RBP was torrefied at 225 °C (nitrogen atmosphere), at 15 °C/min and a residence time of 30 min, to prepare TBP. Pyrolysis of all three materials was carried out at 10 and 15 °C /min using thermogravimetric analysis. The kinetic and thermodynamic parameters were evaluated using Arrhenius and CR methods. The overall activation energy at 15 °C/min was 107.04, 106.88 and 74.85 kJ/mol, for RBP, BBP, and TBP, respectively.

Bacterial neuraminidase inhibitory chalcones from flowers of Coreopsis lanceolata, their kinetic characterization and antibiofilm effect

BackgroundBacteria within biofilms are thousand times more resistant to antibiotics. Neuraminidase is a crucial enzyme for bacterial adhesion and biofilm formation, it hydrolyzes glycosidic residue of glycoproteins, glycolipids, and oligosaccharides. Coreopsis lanceolata L. flowers may have a significant potential of bacterial neuraminidase (BNA) inhibition because of high natural abundance of chalcones. PurposeThe investigation of bacterial biofilm inhibitors has emerged as a novel therapeutic strategy against antibiotic resistance. Therefore, individual chalcones were isolated from C. lanceolata and their capacity to inhibit BNA and formation of Escherichia coli biofilm were evaluated. MethodsDifferent chromatographic techniques were used to isolate the compounds (1-12). Enzyme inhibition and detailed kinetic behavior of compounds was determined by estimation of kinetic parameters (Michaelis-Menten constants (Km), maximum velocity (Vmax), dissociation constant for binding with the free enzyme (KI) and enzyme-substate complex (KIS)). Binding affinities (KSV) and binding modes of inhibitors were elucidated by fluorescence quenching and molecular docking, respectively. The natural abundance of chalcones was established through UPLC-Q-TOF/MS. The most potent inhibitor (1) was tested for its ability to inhibit the formation of E. coli biofilm, which was examined by crystal violet assay, scanning electron microscope (SEM) and confocal laser scanning microscope (CLSM). ResultsA series of eight chalcones (1-8) and four chalcone glucosides (9-12), inhibited BNA in a dose-dependent manner with IC50 of 8.3 ∼ 77.0 µM. The most potent chalcones were butein (1, IC50 = 8.3 µM) and its glucoside 9 (IC50 = 13.8 µM). The aglycones (1-8) showed non-competitive inhibition, while chalcone glucosides (9-12) displayed a mixed type I (KI < KIS). Inhibitory behaviors were doubly confirmed by KSV and matched with tendency of IC50. The functional group responsible for BNA inhibition were disclosed as 4ʹ-hydroxyl group on B-ring by structure activity relationship (SAR) and molecular docking experiments. Butein (1) suppressed E. coli biofilm formation by > 50 % at 100 µM according to crystal violet assay, which was confirmed by SEM and CLSM imaging. ConclusionThe results showed that chalcones (1-8) and chalcone glucosides (9-12), metabolites isolated from the flowers of C. lanceolata, had BNA inhibitory and antibiofilm formation effect on E. coli.

An optimization-free Fisher information driven approach for online design of experiments

Developing mathematical models used to elucidate reaction kinetics plays a crucial role in the design, control, and optimization of chemical processes. One of the most challenging tasks in kinetic model identification is the precise estimation of unknown kinetic model parameters. This challenge can be effectively addressed through the application of Model-Based Design of Experiments (MBDoE) techniques, which enable the design of experiments facilitating precise model parameter estimation with minimal runs and analytical resources. Nevertheless, MBDoE techniques rely on an optimization procedure that is susceptible to parametric uncertainty, making the design procedure computationally intensive and prone to issues of local optimality. MBDoE techniques are also employed in online procedures to expedite the identification of kinetic models in autonomous platforms. As a result, ensuring rapid convergence becomes imperative to mitigate numerical issues during operational processes. In this paper a Fisher Information Matrix Driven (FIMD) approach is introduced to tackle these challenges. The methodology integrates a sampling-based experimental design approach with experiment ranking based on FIM to select the most informative experiment at each iteration. The effectiveness of the proposed design methodology is examined and discussed via two different case studies of increasing complexity: a fed-batch reactor in which the fermentation of baker’s yeast is carried out and a nucleophilic aromatic substitution in a flow reactor system.

Kinetic Parameter Estimation of the Polyethylene Process by Bayesian Optimization

Kinetic Parameter Estimation of the Polyethylene Process by Bayesian Optimization

Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data

Kinetic models of metabolism are promising platforms for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of ‘bottom up’ parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, and in vitro-in vivo differences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard ‘macroscopic’ kinetic parameters, including Km, kcat, Ki, Keq, and nh, as well as diverse reaction mechanisms defined in terms of mass action reactions and ‘microscopic’ rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either within in vitro experiments or between in vitro and in vivo enzyme function. We further demonstrate how parameterized enzyme modules can be used to assemble pathway-scale kinetic models consistent with in vivo behavior. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates.

Optimization-derived blood input function using a kernel method and its evaluation with total-body PET for brain parametric imaging

Dynamic PET allows quantification of physiological parameters through tracer kinetic modeling. For dynamic imaging of brain or head and neck cancer on conventional PET scanners with a short axial field of view, the image-derived input function (ID-IF) from intracranial blood vessels such as the carotid artery (CA) suffers from severe partial volume effects. Alternatively, optimization-derived input function (OD-IF) by the simultaneous estimation (SIME) method does not rely on an ID-IF but derives the input function directly from the data. However, the optimization problem is often highly ill-posed. We proposed a new method that combines the ideas of OD-IF and ID-IF together through a kernel framework. While evaluation of such a method is challenging in human subjects, we used the uEXPLORER total-body PET system that covers major blood pools to provide a reference for validation. MethodsThe conventional SIME approach estimates an input function using a joint estimation together with kinetic parameters by fitting time activity curves from multiple regions of interests (ROIs). The input function is commonly parameterized with a highly nonlinear model which is difficult to estimate. The proposed kernel SIME method exploits the CA ID-IF as a priori information via a kernel representation to stabilize the SIME approach. The unknown parameters are linear and thus easier to estimate. The proposed method was evaluated using 18F-fluorodeoxyglucose studies with both computer simulations and 20 human-subject scans acquired on the uEXPLORER scanner. The effect of the number of ROIs on kernel SIME was also explored. ResultsThe estimated OD-IF by kernel SIME showed a good match with the reference input function and provided more accurate estimation of kinetic parameters for both simulation and human-subject data. The kernel SIME led to the highest correlation coefficient (R = 0.97) and the lowest mean absolute error (MAE = 10.5 %) compared to using the CA ID-IF (R = 0.86, MAE = 108.2 %) and conventional SIME (R = 0.57, MAE = 78.7 %) in the human-subject evaluation. Adding more ROIs improved the overall performance of the kernel SIME method. ConclusionThe proposed kernel SIME method shows promise to provide an accurate estimation of the blood input function and kinetic parameters for brain PET parametric imaging.

Comparison of Traditional and Sequential Approaches for Estimation of Kinetic Parameters of Heavy Oil Upgrading

Comparison of Traditional and Sequential Approaches for Estimation of Kinetic Parameters of Heavy Oil Upgrading

A novel system to determine activity of individual uridine 5′-diphospho-glucuronosyltransferase (UGT) isoforms: Recombinant UGT-beads

Previous work demonstrated that human liver microsomes (HLM) can spontaneously bind to silica-coated magnetizable beads (HLM-beads) and that these HLM-beads retain UGT activity. However, the contributions of individual UGT isoforms is not directly assessable in this system except through use of model inhibitors. Thus, a preparation wherein rUGT microsomes bound to these same beads to form rUGT-beads of individual UGT isoforms would provide a novel system for measuring the contribution of individual UGT isoforms in a direct manner. To this end, the enzyme activities and kinetic parameter estimates of various rUGT isoforms in rUGT-beads were investigated, as well as the impact of fatty acids (FAs) on enzyme activity. The catalytic efficiencies (Vmax/Km) of the tested rUGTs were 2- to 7-fold higher in rUGT-beads compared to rUGT microsomes, except for rUGT1A6, where Vmax is the maximum product formation rate normalized to mg of microsomal protein (pmol/min/mg protein). Interestingly, in contrast to traditional rUGT preparations, the sequestration of UGT-inhibitory fatty acids (FA) using bovine serum albumin (BSA) did not alter the catalytic efficiency (Vmax/Km) of the rUGTs in rUGT-beads. Moreover, the increase in catalytic efficiency of rUGT-beads over rUGT microsomes was similar to increases in catalytic efficiency noted with rUGT microsomes (not bound to beads) incubated with BSA, suggesting the beads in some way altered the potential for FAs to inhibit activity. The rUGT-bead system may serve as a useful albumin-free tool to determine kinetic constants for UGT substrates, particularly those that exhibit high binding to albumin.