Analysis of steric mass-action model for protein adsorption equilibrium onto porous anion-exchange adsorbent

2006 ◽  
Vol 61 (21) ◽  
pp. 7068-7076 ◽  
Author(s):  
Wei-Dong Chen ◽  
Han-Hua Hu ◽  
Yan-Dong Wang
Keyword(s):  
Protein Adsorption ◽  
Anion Exchange ◽  
Adsorption Equilibrium ◽  
Mass Action ◽  
Mass Action Model ◽  
Action Model

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

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.

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

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.

Optimal determination of steric mass action model parameters for β-lactoglobulin using static batch experiments

2010 ◽  
Vol 1217 (26) ◽  
pp. 4267-4277 ◽  
Author(s):  
Tilman Barz ◽  
Verena Löffler ◽  
Harvey Arellano-Garcia ◽  
Günter Wozny
Keyword(s):  
Mass Action ◽  
Model Parameters ◽  
Batch Experiments ◽  
Mass Action Model ◽  
Action Model ◽  
Β Lactoglobulin

Cation Exchange in Porous Media With Broad-Equivalent-Weight Sulfonate Micellar Fluids

1985 ◽  
Vol 25 (04) ◽  
pp. 580-586 ◽  
Author(s):  
A.F. Chan ◽  
V.J. Kremesec
Keyword(s):  
Porous Media ◽  
Cation Exchange ◽  
Single Phase ◽  
Divalent Cations ◽  
Mass Action ◽  
Micellar System ◽  
Mass Action Model ◽  
Action Model ◽  
Parameter Values ◽  
Micellar Fluids

Abstract This paper describes experimental and theoretical studies of cation exchange in porous media with micellar fluids formulated using a broad-equivalent-weight (BEW) sulfonate. The sulfonates can be described as composed of two pseudocomponents a quasi-monosulfonate (the oil-moving pseudo components a quasi-monosulfonate (the oil-moving component) and a quasi-disulfonate (the sulfonate-solubilizing component). With this description and a mass-action model for cation exchange between the micelles, clays, and solution, a match between computer model predictions and results of laboratory single-phase flow tests in Berea sandstone was carried out. The assumptions required are reviewed and independent experimental results presented. With these assumptions and parameter values determined from the Berea history match, satisfactory predictions of divalent cation concentrations in field core experiments have been made. The good predictive capability of this model allows initial screening and development of micellar formulations for specific reservoir applications to be conducted at appropriate hardness levels. Introduction It is well known that the oil recovery performance of a micellar fluid is strongly affected by salinity and hardness (calcium and magnesium divalent cations). This is because they have strong effects on the phase behavior and interfacial tension (IFT) of the surfactant/oil/brine system. It is also well known that the hardness and salinity of the micellar fluid can change significantly as a result of cation exchange and dissolution as the micellar fluid propagates through the reservoir. Since a knowledge of the in-situ levels of salinity and hardness is of primary importance in the screening and development of micellar fluids for field applications, an adequate prediction is necessary. Cation exchange between a brine and the clays within a reservoir rock occurs if the injected fluids have a salinity and hardness different from that of the in-place fluids. Smith, Griffith, and Hill and Lake have studied this problem and have shown the significance of the cation-exchange capacity (CEC) of the clays and the selectivity of the cation species with the clays. The clay selectivity is a measure of the preference of the clay for monovalent vs. divalent cations; for a given brine, smaller values indicate a higher fraction of the clays complexed by the divalent cations. Further, Hill and Lake concluded that the law of mass action is the best model with which to describe the process. Smith, and Hill and Lake, also showed that calcium and magnesium ions have the same selectivity with the clays vs. sodium, and hence they can be treated as a single ionic species. Hill and Lake extended their study to systems containing surfactants. They found that cation exchange in the presence of a surfactant system was complicated by interaction between surfactant and divalent cations. To describe the levels of hardness measured in the presence of surfactant micelles, they postulated the formation of a divalent-cation/surfactant complex and modeled the phenomenon with a mass-action isotherm. Gupta provided phenomenon with a mass-action isotherm. Gupta provided additional data supporting the formation of such a complex. Hirasaki and Lawson proposed a Donnan equilibrium model to describe the association of sodium and calcium with the micelles, and they estimated selectivity values from the resulting expressions. Hirasaki has incorporated a mass-action model, surfactant adsorption, and electroneutrality conditions with the mass balances neglecting dispersion to obtain a description of cation exchange during single-phase-flow in porous media. He has solved the system of equations using a method-of-characteristics approach and has been able to describe the experiments of Hill and Lake and Gupta showing good agreement between experiment and theory. The model is limited to one surfactant species and two cations-one monovalent and one divalent. The surfactant system used by Gupta closely conforms to these limitations. The micellar system used by Hill and Lake, however, was composed of two petroleum sulfonates and sodium alkyl ethoxysulfate (Neodol 25–3S). Nevertheless, Hirasaki assumed the surfactant mixture to be acting as a single surfactant species. This paper deals with the cation exchange that occurs during the propagation of a micellar system containing BEW sulfonate. The objective is to history-match limited tests in Berea cores and then to use the understanding gained and parameter values obtained to predict hardness concentrations in field cores over a wide range of micellar compositions. To correlate the Berea data and to extrapolate to other conditions, the approach of Hirasaki is desirable because of its simplicity. However, the complex composition of the BEW sulfonate micellar system, as well as a desire to include an adsorption isotherm and dispersion-important for small slug processes-precluded the straightforward use of the equations and the solution that he put forward. SPEJ P. 580

Thermodynamic properties of alkyldimethylamine oxides in water. Application of a mass-action model for micellization

1983 ◽  
Vol 87 (8) ◽  
pp. 1397-1406 ◽  
Author(s):  
Jacques E. Desnoyers ◽  
Gaston Caron ◽  
Rosario DeLisi ◽  
David Roberts ◽  
Alain Roux ◽  
...  
Keyword(s):  
Thermodynamic Properties ◽  
Mass Action ◽  
Water Application ◽  
Mass Action Model ◽  
Action Model

scholarly journals Input–output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data

2009 ◽  
Vol 5 (1) ◽  
pp. 239 ◽  
Author(s):  
William W Chen ◽  
Birgit Schoeberl ◽  
Paul J Jasper ◽  
Mario Niepel ◽  
Ulrik B Nielsen ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Mass Action ◽  
Input Output ◽  
Dynamic Data ◽  
Mass Action Model ◽  
Erbb Signaling ◽  
Action Model ◽  
Output Behavior

scholarly journals Distribution of vaccine/antivirals and the ‘least spread line’ in a stratified population

2009 ◽  
Vol 7 (46) ◽  
pp. 755-764 ◽  
Author(s):  
E. Goldstein ◽  
A. Apolloni ◽  
B. Lewis ◽  
J. C. Miller ◽  
M. Macauley ◽  
...  
Keyword(s):  
H1n1 Influenza ◽  
Mass Action ◽  
Initial Growth ◽  
Influenza Outbreak ◽  
Mass Action Model ◽  
The Us ◽  
Action Model ◽  
2009 H1n1 ◽  
Influenza Vaccinations ◽  
Prioritization Scheme

We describe a prioritization scheme for an allocation of a sizeable quantity of vaccine or antivirals in a stratified population. The scheme builds on an optimal strategy for reducing the epidemic's initial growth rate in a stratified mass-action model. The strategy is tested on the EpiSims network describing interactions and influenza dynamics in the population of Utah, where the stratification we have chosen is by age (0–6, 7–13, 14–18, adults). No prior immunity information is available, thus everyone is assumed to be susceptible—this may be relevant, possibly with the exception of persons over 50, to the 2009 H1N1 influenza outbreak. We have found that the top priority in an allocation of a sizeable quantity of seasonal influenza vaccinations goes to young children (0–6), followed by teens (14–18), then children (7–13), with the adult share being quite low. These results, which rely on the structure of the EpiSims network, are compared with the current influenza vaccination coverage levels in the US population.

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