Determination of thermodynamic potentials and the aggregation number for micelles with the mass-action model by isothermal titration calorimetry: A case study on bile salts

Specific conductivities of local anesthetic heptacainium chloride XlX (HCC XlX) (N-[2-(2-heptyloxyphenylcarbamoyloxy)-ethyl]-piperidinium chloride), in aqueous electrolyte solution (0.05 mol.1−1 NaCl) were measured as a function of the concentration and temperature. The critical micelle concentrations (c.m.c) and the counterion (β) binding of the micelles were estimated from the dependence of the specific conductivity of the substrate concentration. The temperature dependence of ln c.m.c was fitted to the function of the second degree polynomial. From the fitting parameters, Gibss free energies (ΔG0), enthalpies (ΔH0), and entropies (ΔS0) of micellization as a function of temperature were estimated. The "mass action model" was applied to micelle formation to calculate the micellization parameters.

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The use of a mass-action model to validate the output from a stochastic simulation model of bovine viral diarrhoea virus spread in a closed dairy herd

Preventive Veterinary Medicine ◽

10.1016/s0167-5877(96)01129-4 ◽

1997 ◽

Vol 31 (3-4) ◽

pp. 199-209 ◽

Cited By ~ 18

Author(s):

G. Innocent ◽

I. Morrison ◽

J. Brownlie ◽

G. Gettinby

Keyword(s):

Simulation Model ◽

Dairy Herd ◽

Bovine Viral Diarrhoea Virus ◽

Mass Action ◽

Virus Spread ◽

Bovine Viral Diarrhoea ◽

Mass Action Model ◽

Diarrhoea Virus ◽

Action Model ◽

Stochastic Simulation Model

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Steric mass-action model for dye–ligand affinity adsorption of protein

Journal of Chromatography A ◽

10.1016/s0021-9673(02)00346-1 ◽

2002 ◽

Vol 957 (2) ◽

pp. 89-97 ◽

Cited By ~ 24

Author(s):

Songping Zhang ◽

Yan Sun

Keyword(s):

Mass Action ◽

Affinity Adsorption ◽

Ligand Affinity ◽

Mass Action Model ◽

Action Model

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Modelling and Experimental Studies of Sorption in the Near Field of a Cementitious Repository

MRS Proceedings ◽

10.1557/proc-176-591 ◽

1989 ◽

Vol 176 ◽

Author(s):

P.L. Brown ◽

A. Haworth ◽

R. McCrohon ◽

S.M. Sharland ◽

C.J. Tweed

Keyword(s):

Near Field ◽

Experimental Studies ◽

Mass Action ◽

Chemical Equilibria ◽

Transport Code ◽

Mass Action Model ◽

Action Model ◽

Modelling Studies ◽

Simple Mass

ABSTRACTA joint experimental and modelling programme is reported, which aims to improve our understanding of sorption processes of radionuclides onto repository materials. Diffusion/sorption experiments of sorption onto cement are described, although results are limited at this stage. The modelling studies use the coupled chemical equilibria and transport code CHEQMATE to simulate some of these experiments. The chemical part of the model is based on a simple mass-action model of sorption. More detailed comparisons will continue when the experiments are terminated, and the samples are sectioned.

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Inferring gene regulatory networks using a time-delayed mass action model

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720016500128 ◽

2016 ◽

Vol 14 (04) ◽

pp. 1650012

Author(s):

Yaou Zhao ◽

Mingyan Jiang ◽

Yuehui Chen

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Classical Model ◽

Population Based ◽

Mass Action ◽

Mass Action Model ◽

Action Model ◽

Gene Regulatory ◽

Efficient Learning ◽

Delay Differential

This paper demonstrates a new time-delayed mass action model which applies a set of delay differential equations (DDEs) to represent the dynamics of gene regulatory networks (GRNs). The mass action model is a classical model which is often used to describe the kinetics of biochemical processes, so it is fit for GRN modeling. The ability to incorporate time-delayed parameters in this model enables different time delays of interaction between genes. Moreover, an efficient learning method which employs population-based incremental learning (PBIL) algorithm and trigonometric differential evolution (TDE) algorithm TDE is proposed to automatically evolve the structure of the network and infer the optimal parameters from observed time-series gene expression data. Experiments on three well-known motifs of GRN and a real budding yeast cell cycle network show that the proposal can not only successfully infer the network structure and parameters but also has a strong anti-noise ability. Compared with other works, this method also has a great improvement in performances.

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