Determination of Rapid-Equilibrium Kinetic Parameters of Ordered and Random Enzyme-Catalyzed Reaction A + B = P + Q

This article deals with the rapid-equilibrium kinetics of the forward and reverse reactions together for the ordered and random enzyme-catalyzed A + B = P + Q and emphasizes the importance of reporting the values of the full set of equilibrium constants. Equilibrium constants that are not in the rate equation can be calculated for random mechanisms using thermodynamic cycles. This treatment is based on the use of a computer to derive rate equations for three mechanisms and to estimate the kinetic parameters with the minimum number of velocity measurements. The most general of these three programs is the one to use first when the mechanism for A + B = P + Q is studied for the first time. This article shows the effects of experimental errors in velocity measurements on the values of the kinetic parameters and on the apparent equilibrium constant calculated using the Haldane relation

Determination of Kinetic Parameters of Enzyme-Catalyzed Reaction A + B + C → Products with the Minimum Number of Velocity Measurements

Rapid-equilibrium rate equations are derived for the five different mechanisms for the enzymatic catalysis of A + B + C → products using a computer. These rate equations are used to determine the minimum number of velocities required to estimate the values of the kinetic parameters. The rate equation for the completely ordered mechanism involves four kinetic parameters, and the rate equation for the completely random mechanism involves eight kinetic parameters. Therefore, the four to eight kinetic parameters can be estimated by determining four to eight velocities and solving four to eight simultaneous equations. General recommendations are made as to the choices of triplets of substrate concentrations {[A], [B], [C]} to be used to determine the velocities. The effects of 5% errors in the measured velocities, one at a time, are calculated and are summarized in tables. Calculations of effects of experimental errors are useful in choosing the triplets of substrate concentrations to be used to obtain the most accurate values of the kinetic parameters. When the kinetic parameters for A + B + C → products are to be determined for the first time, it is recommended that the program for the completely random mechanism be used because it can identify the mechanism and determine the kinetic parameters in one operation

The role of histidine residues in modulation of the rat P2X 2 purinoceptor by zinc and pH

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65774/1/jphysiol.2001.013244.pd

Ecosystem biogeochemistry considered as a distributed metabolic network ordered by maximum entropy production

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of The Royal Society for personal use, not for redistribution. The definitive version was published in Philosophical Transactions of the Royal Society B 365 (2010): 1417-1427, doi:10.1098/rstb.2009.0272.We examine the application of the maximum entropy production principle for describing ecosystem biogeochemistry. Since ecosystems can be functionally stable despite changes in species composition, we utilize a distributed metabolic network for describing biogeochemistry, which synthesizes generic biological structures that catalyze reaction pathways, but is otherwise organism independent. Allocation of biological structure and regulation of biogeochemical reactions is determined via solution of an optimal control problem in which entropy production is maximized. However, because synthesis of biological structures cannot occur if entropy production is maximized instantaneously, we propose that information stored within the metagenome allows biological systems to maximize entropy production when averaged over time. This differs from abiotic systems that maximize entropy production at a point in space-time, which we refer to as the steepest descent pathway. It is the spatiotemporal averaging that allows biological systems to outperform abiotic processes in entropy production, at least in many situations. A simulation of a methanotrophic system is used to demonstrate the approach. We conclude with a brief discussion on the implications of viewing ecosystems as self organizing molecular machines that function to maximize entropy production at the ecosystem level of organization.The work presented here was funded by the PIE-LTER program (NSF OCE-0423565), as well as from NSF CBET-0756562, NSF EF-0928742 and NASA Exobiology and Evolutionary Biology (NNG05GN61G)

Calculation of the interfacial free energy of a fluid at a static wall by Gibbs–Cahn integration

This is the publisher's version, also available electronically from http://scitation.aip.org/content/aip/journal/jcp/132/20/10.1063/1.3428383.The interface between a fluid and a static wall is a useful model for a chemically heterogeneous solid-liquid interface. In this work, we outline the calculation of the wall-fluid interfacial free energy(γwf) for such systems using molecular simulation combined with adsorptionequations based on Cahn’s extension of the surface thermodynamics of Gibbs. As an example, we integrate such an adsorptionequation to obtain γwf as a function of pressure for a hard-sphere fluid at a hard wall. The results so obtained are shown to be in excellent agreement in both magnitude and precision with previous calculations of this quantity, but are obtained with significantly lower computational effort

Determination of the solid-liquid interfacial free energy along a coexistence line by Gibbs–Cahn integration

This is the publisher's version, also available electronically from http://scitation.aip.org/content/aip/journal/jcp/131/11/10.1063/1.3231693.We calculate the solid-liquid interfacial free energyγsl for the Lennard-Jones (LJ) system at several points along the pressure-temperature coexistence curve using molecular-dynamics simulation and Gibbs–Cahn integration. This method uses the excess interfacial energy(e) and stress (τ) along the coexistence curve to determine a differential equation for γsl as a function of temperature. Given the values of γsl for the (100), (110), and (111) LJ interfaces at the triple-point temperature (T∗=kT/ϵ=0.618), previously obtained using the cleaving method by Davidchack and Laird [J. Chem. Phys. 118, 7657 (2003)], this differential equation can be integrated to obtain γsl for these interfaces at higher coexistence temperatures. Our values for γsl calculated in this way at T∗=1.0 and 1.5 are in good agreement with those determined previously by cleaving, but were obtained with significantly less computational effort than required by either the cleaving method or the capillary fluctuation method of Hoyt, Asta, and Karma [Phys. Rev. Lett. 86, 5530 (2001)]. In addition, the orientational anisotropy in the excess interfaceenergy, stress and entropy, calculated using the conventional Gibbs dividing surface, are seen to be significantly larger than the relatively small anisotropies in γsl itself

eQuilibrator—the biochemical thermodynamics calculator

The laws of thermodynamics constrain the action of biochemical systems. However, thermodynamic data on biochemical compounds can be difficult to find and is cumbersome to perform calculations with manually. Even simple thermodynamic questions like ‘how much Gibbs energy is released by ATP hydrolysis at pH 5?’ are complicated excessively by the search for accurate data. To address this problem, eQuilibrator couples a comprehensive and accurate database of thermodynamic properties of biochemical compounds and reactions with a simple and powerful online search and calculation interface. The web interface to eQuilibrator (http://equilibrator.weizmann.ac.il) enables easy calculation of Gibbs energies of compounds and reactions given arbitrary pH, ionic strength and metabolite concentrations. The eQuilibrator code is open-source and all thermodynamic source data are freely downloadable in standard formats. Here we describe the database characteristics and implementation and demonstrate its use

Multiplicity Distributions and Rapidity Gaps

I examine the phenomenology of particle multiplicity distributions, with special emphasis on the low multiplicities that are a background in the study of rapidity gaps. In particular, I analyze the multiplicity distribution in a rapidity interval between two jets, using the HERWIG QCD simulation with some necessary modifications. The distribution is not of the negative binomial form, and displays an anomalous enhancement at zero multiplicity. Some useful mathematical tools for working with multiplicity distributions are presented. It is demonstrated that ignoring particles with pt<0.2 has theoretical advantages, in addition to being convenient experimentally.Comment: 24 pages, LaTeX, MSUHEP/94071

Molecular dynamics simulations of the temperature-induced unfolding of crambin follow the Arrhenius equation

Molecular dynamics simulations have been used extensively to model the folding and unfolding of proteins. The rates of folding and unfolding should follow the Arrhenius equation over a limited range of temperatures. This study shows that molecular dynamic simulations of the unfolding of crambin between 500K and 560K do follow the Arrhenius equation. They also show that while there is a large amount of variation between the simulations the average values for the rate show a very high degree of correlation