scholarly journals 3D Kinetic Analysis: A Rapid, Model Independent, Method for Enzyme Kinetics

2021 ◽  
Author(s):  
Armina Abbasi ◽  
John T. Rodgers ◽  
Jeffrey P. Jones
Keyword(s):  
Enzyme Kinetics ◽  
Kinetic Models ◽  
Substrate Inhibition ◽  
Enzymatic Reaction ◽  
Time Dependent ◽  
General Strategy ◽  
Enzymatic Reactions ◽  
Maximal Velocity ◽  
Experimental Protocol ◽  
Over Time

Abstract In this work, we present the 3D Kinetics approach as a step forward in the field of enzyme kinetics. Normally in enzyme kinetics, it is first assumed that the kinetics will conform to the assumption of Michaelis and Menten and an experiment is conducted at various concentrations around the concentration that gives half-maximal velocity. Often, these experiments could be compromised by having too much substrate, nonlinear reaction over time, time-dependent or substrate inhibition, or several other kinetic models. Herein, we present a general strategy that will decrease the number of experiments required to develop an accurate representation of the kinetics of an enzymatic reaction. We show that with a single experimental protocol, we can fit a number of the most common kinetic models associated with enzyme-catalyzed reactions. Through this experiment, we introduce the effect of time on saturation curves by modeling the reaction velocity over time and across a set of substrate concentrations. Michaelis-Menten (MM) kinetics and other analytical solutions used to solve more complex kinetic models were introduced to the field of enzymology over a hundred years ago and have only marginally changed over the years with each analytical model requiring a different set of experiments and concentration ranges. Although this approach was necessary at the time, the computational power today makes any such simplifying and limiting efforts unnecessary and avoidable. In this study, we use a single experimental protocol and fit a number of different models to the resulting data. We present four different case studies to compare and contrast the outcomes of 3D Kinetics with MM analysis for different kinetic scenarios such as enzymatic reactions with linear kinetics, biphasic kinetics, substrate inhibition, and time-dependent inhibition (TDI) to confirm the advantage of the 3D Kinetics method to the long-established MM analysis.

Exponential function for calculating saturable enzyme kinetics.

1988 ◽  
Vol 34 (12) ◽  
pp. 2486-2489 ◽  
Author(s):  
F Keller ◽  
C Emde ◽  
A Schwarz
Keyword(s):  
Steady State ◽  
Enzyme Kinetics ◽  
Exponential Function ◽  
Association Constant ◽  
Time Dependent ◽  
Mathematical Representation ◽  
Hyperbolic Function ◽  
Maximal Velocity ◽  
Michaelis Constant ◽  
General Meaning

Abstract Enzyme kinetics are usually described by the Michaelis-Menten equation, where the time-dependent decrease of substrate (-dS/dt) is a hyperbolic function of maximal velocity (Vmax), Michaelis constant (Km), and amount of substrate (S). Because the Michaelis-Menten function in its most general meaning requires an assumption of steady-state, it is less curvilinear than true enzyme kinetics. A saturation-type exponential function is more curvilinear than the hyperbolic function and more closely approximates enzyme kinetics: -dS/dt = Vmax [1 - exp(-S/Km)]. The mathematical representation of enzyme kinetics can be further improved by introducing a deceleration term (Vdec), to make the assumption of a steady state unnecessary. For the action of chymotrypsin on N-acetyltyrosylethylester, the Michaelis-Menten equation yields the following: Vmax = 3.74 mumol/min and Km = 833 mumol. According to decelerated enzyme kinetics, the values Vmax = 4.80 mumol/min, Vdec = 0.0118 mumol/min, and the association constant (Ka) = 0.00111/mumol are more nearly accurate for this reaction (where 1/Ka = 901 mumol approximately Km).

scholarly journals On the Velocity of Enzymatic Reactions in Michaelis–Menten-Like Schemes (Ensemble and Single-Molecule Versions)

2020 ◽  
Vol 65 (5) ◽  
pp. 412 ◽  
Author(s):  
L. N. Christophorov
Keyword(s):  
Binding Site ◽  
Single Molecule ◽  
Substrate Inhibition ◽  
Enzymatic Reaction ◽  
Enzymatic Reactions ◽  
Reaction Velocity ◽  
Conformational Regulation ◽  
Regulatory Properties

In searching non-standard ways of conformational regulation, various Michaelis–Menten-like schemes attract relentless attention, resulting in sometimes too sophisticated considerations. With the example of monomeric enzymes possessing an only binding site, we define the minimal schemes capable of bearing peculiar regulatory properties like “cooperativity” or substrate inhibition. The simplest ways of calculating the enzymatic reaction velocity are exemplified, either in the ensemble or single-molecule case.

scholarly journals Real-Time Interferometric Refractive Index Change Measurement for the Direct Detection of Enzymatic Reactions and the Determination of Enzyme Kinetics

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 539
Author(s):  
Søren Jepsen ◽  
Thomas Jørgensen ◽  
Henrik Sørensen ◽  
Søren Kristensen
Keyword(s):  
Refractive Index ◽  
Enzyme Kinetics ◽  
Adenosine Triphosphate ◽  
Direct Detection ◽  
Enzymatic Reaction ◽  
Refractive Index Change ◽  
Direct Determination ◽  
Initial Reaction ◽  
Enzymatic Reactions

Back scatter interferometry (BSI) is a sensitive method for detecting changes in the bulk refractive index of a solution in a microfluidic system. Here we demonstrate that BSI can be used to directly detect enzymatic reactions and, for the first time, derive kinetic parameters. While many methods in biomedical assays rely on detectable biproducts to produce a signal, direct detection is possible if the substrate or the product exert distinct differences in their specific refractive index so that the total refractive index changes during the enzymatic reaction. In this study, both the conversion of glucose to glucose-6-phosphate, catalyzed by hexokinase, and the conversion of adenosine-triphosphate to adenosine di-phosphate and mono-phosphate, catalyzed by apyrase, were monitored by BSI. When adding hexokinase to glucose solutions containing adenosine-triphosphate, the conversion can be directly followed by BSI, which shows the increasing refractive index and a final plateau corresponding to the particular concentration. From the initial reaction velocities, KM was found to be 0.33 mM using Michaelis–Menten kinetics. The experiments with apyrase indicate that the refractive index also depends on the presence of various ions that must be taken into account when using this technique. This study clearly demonstrates that measuring changes in the refractive index can be used for the direct determination of substrate concentrations and enzyme kinetics.

scholarly journals Detection of Acetaminophen and Its Glucuronide in Fingerprint by SALDI Mass Spectrometry Using Zeolite and Study of Time-Dependent Changes in Detected Ion Amount

Analytica ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 66-75
Author(s):  
Toshiki Horikoshi ◽  
Chihiro Kitaoka ◽  
Yosuke Fujii ◽  
Takashi Asano ◽  
Jiawei Xu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Glucuronic Acid ◽  
Laser Desorption ◽  
The Body ◽  
Time Dependent ◽  
Laser Desorption Ionization ◽  
Fingerprint Analysis ◽  
Desorption Ionization ◽  
Acid Conjugate ◽  
Over Time

The ingredients of an antipyretic (acetaminophen, AAP) and their metabolites excreted into fingerprint were detected by surface-assisted laser desorption ionization (SALDI) mass spectrometry using zeolite. In the fingerprint taken 4 h after AAP ingestion, not only AAP but also the glucuronic acid conjugate of AAP (GAAP), caffeine (Caf), ethenzamide (Eth), salicylamide (Sala; a metabolite of Eth), and urea were detected. Fingerprints were collected over time to determine how the amounts of AAP and its metabolite changed with time, and the time dependence of the peak intensities of protonated AAP and GAAP was measured. It was found that the increase of [GAAP+H]+ peak started later than that of [AAP+H]+ peak, reflecting the metabolism of AAP. Both AAP and GAAP reached maximum concentrations approximately 3 h after ingestion, and were excreted from the body with a half-life of approximately 3.3 h. In addition, fingerprint preservation was confirmed by optical microscopy, and fingerprint shape was retained even after laser irradiation of the fingerprint. Our method may be used in fingerprint analysis.

scholarly journals Risk Factors for Prostate Cancer Detection After a Negative Biopsy: A Novel Multivariable Longitudinal Approach

2010 ◽  
Vol 28 (10) ◽  
pp. 1714-1720 ◽  
Author(s):  
Peter H. Gann ◽  
Angela Fought ◽  
Ryan Deaton ◽  
William J. Catalona ◽  
Edward Vonesh
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Specific Antigen ◽  
Time Dependent ◽  
Negative Biopsy ◽  
Gland Volume ◽  
Piecewise Exponential ◽  
Over Time ◽  
Negative Biopsies

Purpose To introduce a novel approach for the time-dependent quantification of risk factors for prostate cancer (PCa) detection after an initial negative biopsy. Patients and Methods Data for 1,871 men with initial negative biopsies and at least one follow-up biopsy were available. Piecewise exponential regression models were developed to quantify hazard ratios (HRs) and define cumulative incidence curves for PCa detection for subgroups with specific patterns of risk factors over time. Factors evaluated included age, race, serum prostate-specific antigen (PSA) concentration, PSA slope, digital rectal examination, dysplastic glands or prostatitis on biopsy, ultrasound gland volume, urinary symptoms, and number of negative biopsies. Results Four hundred sixty-five men had PCa detected, after a mean follow-up time of 2.8 years. All of the factors were independent predictors of PCa detection except for PSA slope, as a result of its correlation with time-dependent PSA level, and race. PSA (HR = 3.90 for > 10 v 2.5 to 3.9 ng/mL), high-grade prostatic intraepithelial neoplasia/atypical glands (HR = 2.97), gland volume (HR = 0.39 for > 50 v < 25 mL), and number of repeat biopsies (HR = 0.36 for two v zero repeat biopsies) were the strongest predictors. Men with high-risk versus low-risk event histories had a 20-fold difference in PCa detection over 5 years. Conclusion Piecewise exponential models provide an approach to longitudinal analysis of PCa risk that allows clinicians to see the interplay of risk factors as they unfold over time for individual patients. With these models, it is possible to identify distinct subpopulations with dramatically different needs for monitoring and repeat biopsy.

scholarly journals A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making

2018 ◽  
Vol 38 (8) ◽  
pp. 904-916 ◽  
Author(s):  
Aasthaa Bansal ◽  
Patrick J. Heagerty
Keyword(s):  
Decision Making ◽  
Statistical Methods ◽  
Sensitivity And Specificity ◽  
Prognostic Model ◽  
Dynamic Decision Making ◽  
Time Dependent ◽  
Survival Models ◽  
Biliary Cirrhosis ◽  
Time Varying ◽  
Over Time

Many medical decisions involve the use of dynamic information collected on individual patients toward predicting likely transitions in their future health status. If accurate predictions are developed, then a prognostic model can identify patients at greatest risk for future adverse events and may be used clinically to define populations appropriate for targeted intervention. In practice, a prognostic model is often used to guide decisions at multiple time points over the course of disease, and classification performance (i.e., sensitivity and specificity) for distinguishing high-risk v. low-risk individuals may vary over time as an individual’s disease status and prognostic information change. In this tutorial, we detail contemporary statistical methods that can characterize the time-varying accuracy of prognostic survival models when used for dynamic decision making. Although statistical methods for evaluating prognostic models with simple binary outcomes are well established, methods appropriate for survival outcomes are less well known and require time-dependent extensions of sensitivity and specificity to fully characterize longitudinal biomarkers or models. The methods we review are particularly important in that they allow for appropriate handling of censored outcomes commonly encountered with event time data. We highlight the importance of determining whether clinical interest is in predicting cumulative (or prevalent) cases over a fixed future time interval v. predicting incident cases over a range of follow-up times and whether patient information is static or updated over time. We discuss implementation of time-dependent receiver operating characteristic approaches using relevant R statistical software packages. The statistical summaries are illustrated using a liver prognostic model to guide transplantation in primary biliary cirrhosis.

Heat shock increases cytosolic free Ca2+ concentration via Na(+)-Ca2+ exchange in human epidermoid A 431 cells

1992 ◽  
Vol 263 (1) ◽  
pp. C30-C38 ◽  
Author(s):  
J. G. Kiang ◽  
M. L. Koenig ◽  
R. C. Smallridge
Keyword(s):  
Heat Treatment ◽  
Heat Shock ◽  
Time Dependent ◽  
Maximal Velocity ◽  
Dependent Manner ◽  
Michaelis Constant ◽  
Exchange System ◽  
Maximal Increase ◽  
Normal Cells ◽  
Dimethyl Amiloride

This study characterized cytosolic free Ca2+ concentration ([Ca2+]i) in normal and thermally injured human epidermoid A 431 cells. The resting [Ca2+]i in normal cells at 37 degrees C was 87 +/- 5 nM (n = 105). When cells were subjected to hyperthermia (40-50 degrees C), [Ca2+]i increased in a temperature- and time-dependent manner. The maximal increase in cells exposed to 45 degrees C was observed at 20 min; [Ca2+]i returned to normal within 1 h. The heat-induced [Ca2+]i increase depended on the presence of external Ca2+. La3+ and Cd2+ but not Co2+, verapamil, or nifedipine attenuated the heat-induced [Ca2+]i increase. TMB-8 partially blocked the increase in [Ca2+]i but pertussis toxin and cholera toxin pretreatment did not. The magnitude of the heat-induced [Ca2+]i increase or 45Ca2+ uptake depended on the presence of extracellular Na+. Heat treatment reduced the apparent Michaelis constant for external Ca2+ from 490 +/- 91 to 210 +/- 60 microM, whereas the maximal velocity remained the same. The intracellular Na+ concentration decreased 62.5% after heating. The heat-induced [Ca2+]i increase was completely blocked by amiloride (5 microM) and 5'-(N,N-dimethyl)-amiloride (1 microM). These results suggest heat activates the Na(+)-Ca2+ exchange system so as to increase [Ca2+]i and reduce [Na+]i.

scholarly journals A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease

2020 ◽  
Vol 16 (12) ◽  
pp. e1008473
Author(s):  
Pamela N. Luna ◽  
Jonathan M. Mansbach ◽  
Chad A. Shaw
Keyword(s):  
Joint Modeling ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Time Dependent ◽  
Time To Event ◽  
Modeling Framework ◽  
Microbial Composition ◽  
Disease Outcomes ◽  
Microbiome Data ◽  
Over Time

Changes in the composition of the microbiome over time are associated with myriad human illnesses. Unfortunately, the lack of analytic techniques has hindered researchers’ ability to quantify the association between longitudinal microbial composition and time-to-event outcomes. Prior methodological work developed the joint model for longitudinal and time-to-event data to incorporate time-dependent biomarker covariates into the hazard regression approach to disease outcomes. The original implementation of this joint modeling approach employed a linear mixed effects model to represent the time-dependent covariates. However, when the distribution of the time-dependent covariate is non-Gaussian, as is the case with microbial abundances, researchers require different statistical methodology. We present a joint modeling framework that uses a negative binomial mixed effects model to determine longitudinal taxon abundances. We incorporate these modeled microbial abundances into a hazard function with a parameterization that not only accounts for the proportional nature of microbiome data, but also generates biologically interpretable results. Herein we demonstrate the performance improvements of our approach over existing alternatives via simulation as well as a previously published longitudinal dataset studying the microbiome during pregnancy. The results demonstrate that our joint modeling framework for longitudinal microbiome count data provides a powerful methodology to uncover associations between changes in microbial abundances over time and the onset of disease. This method offers the potential to equip researchers with a deeper understanding of the associations between longitudinal microbial composition changes and disease outcomes. This new approach could potentially lead to new diagnostic biomarkers or inform clinical interventions to help prevent or treat disease.

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