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Cell modeling

More sophisticated approaches to describe double layer interactions have been developed more recently. Using cell models, the full Poisson-Boltzmann equation can be solved for ordered stmctures. The approach by Alexander et al shows how the effective colloidal particle charge saturates when the bare particle charge is increased [4o]. Using integral equation methods, the behaviour of the primitive model has been studied, in which all the interactions between the colloidal macro-ions and the small ions are addressed (see, for instance, [44, 45]). [Pg.2678]

Irreversible thermodynamics has also been used sometimes to explain reverse osmosis [14,15]. If it can be assumed that the thermodynamic forces responsible for reverse osmosis are sufficiently small, then a linear relationship will exist between the forces and the fluxes in the system, with the coefficients of proportionality then referred to as the phenomenological coefficients. These coefficients are generally notoriously difficult to obtain, although some progress has been made recently using approaches such as cell models [15]. [Pg.780]

PBM (Photochemical Box Model) is a simple stationary single-cell model with a variable height lid designed to provide volume-integrated hour averages of ozone and otlier photochemical smog pollutants for an urban area for a single day of simulation. [Pg.386]

Now suppose the body s immune system malfunctions and begins attacking the body itself. A typical scenario might involve killer cells K attacking helper and/or suppressor cells. Chowdbury and Stauffer [chowdQO] developed a simple five-cell model using two types of helper cells Hi and H2). two type of suppressors Si and S2) and one killer cell (K) ... [Pg.428]

A recent example of a CA model of the immune response in AIDS is Pandley s four-cell model using interactions among macrophages (= M) containing parts of the virus on their surface, helper T cells (= H), cytotoxic T cells (= C) and the virus (= V) ([pand89], [pandQl]) ... [Pg.428]

To evaluate the effect of holdup on bubble velocity, Marrucci (M3) used a spherical cell model of radius b such that... [Pg.318]

In the absence of convective effect, the profiles of > between any two adjacent bubbles exhibits an extremum value midway between the bubbles. Therefore, there exists around each bubble a surface on which d jdr = 3(C )/3r = 0, and hence the fluxes are zero. Using the cell model [Eqs. (158) or (172)] one obtains the following boundary conditions For t > 0... [Pg.383]

To account for the variation of the dynamics with pressure, the free volume is allowed to compress with P, but differently than the total compressibility of the material [22]. One consequent problem is that fitting data can lead to the unphysical result that the free volume is less compressible than the occupied volume [42]. The CG model has been modified with an additional parameter to describe t(P) [34,35] however, the resulting expression does not accurately fit data obtained at high pressure [41,43,44]. Beyond describing experimental results, the CG fit parameters yield free volumes that are inconsistent with the unoccupied volume deduced from cell models [41]. More generally, a free-volume approach to dynamics is at odds with the experimental result that relaxation in polymers is to a significant degree a thermally activated process [14,15,45]. [Pg.659]

The square cell is convenient for a model of water because water is quadrivalent in a hydrogen-bonded network (Figure 3.2). Each face of a cell can model the presence of a lone-pair orbital on an oxygen atom or a hydrogen atom. Kier and Cheng have adopted this platform in studies of water and solution phenomena [5]. In most of those studies, the faces of a cell modeling water were undifferentiated, that is no distinction was made as to which face was a lone pair and which was a hydrogen atom. The reactivity of each water cell was modeled as a consequence of a uniform distribution of structural features around the cell. [Pg.41]

In a cellular automata model of a solution, there are three different types of cells with their states encoded. The first is the empty space or voids among the molecules. These are designated to have a state of zero hence, they perform no further action. The second type of cell is the water molecule. We have described the rules governing its action in the previous chapter. The third type of cell in the solution is the cell modeling a solute molecule. It must be identified with a state value separate from that of water. [Pg.57]

Figure 5.5. Examples of a ceUular automata modeUng of miceUe formation. The dark faces of each cell model the hydrophihc part of the amphiphile, while the light faces model the hydrophobic features of the amphiphile molecule... Figure 5.5. Examples of a ceUular automata modeUng of miceUe formation. The dark faces of each cell model the hydrophihc part of the amphiphile, while the light faces model the hydrophobic features of the amphiphile molecule...
Neal and Nader [260] considered diffusion in homogeneous isotropic medium composed of randomly placed impermeable spherical particles. They solved steady-state diffusion problems in a unit cell consisting of a spherical particle placed in a concentric shell and the exterior of the unit cell modeled as a homogeneous media characterized by one parameter, the porosity. By equating the fluxes in the unit cell and at the exterior and applying the definition of porosity, they obtained... [Pg.572]

LDPE tabular reactor is divided into several reaction zon acoirding to fhe feed injection points. Here we apply mixing cell model for tobidar rcsictor which considea s the reactor axis as series of cells which is conceptually the same as CSTRs in series. In tiiis study 40 cells are used for each reactor spool of 10 m long. The mass balant equation of a single cell at steady state can be written as follows. [Pg.838]

The reasons for this are diverse and include the fact that models of cardiac cellular activity were among the first cell models ever developed. Analytical descriptions of virtually all cardiac cell types are now available. Also, the large-scale integration of cardiac organ activity is helped immensely by the high degree of spatial and temporal regularity of functionally relevant events and structures, as cells in the heart beat synchronously. [Pg.132]

Cardiac models are amongst the most advanced in silico tools for bio-med-icine, and the above scenario is bound to become reality rather sooner than later. Both cellular and whole organ models have aheady matured to a level where they have started to possess predictive power. We will now address some aspects of single cell model development (the cars ), and then look at how virtual cells interact to simulate the spreading wave of electrical excitation in anatomically representative, virtual hearts (the traffic ). [Pg.135]

A breakthrough in cell modelling occurred with the work of the British scientists. Sir Alan L. Hodgkin and Sir Andrew F. Huxley, for which they were in 1963 (jointly with Sir John C. Eccles) awarded the Nobel prize. Their new electrical models calculated the changes in membrane potential on the basis of the underlying ionic currents. [Pg.136]

These detailed cell models can be used to study the development in time of processes like myocardial ischaemia (a reduction in coronary blood flow that causes under-supply of oxygen to the cardiac muscle), or effects of genetic mutations on cellular electrophysiology. They allow to predict the outcome of changes in the cell s environment, and may even be used to assess drug actions. [Pg.137]

These may be produced by grouping together multiple cell models to form virtual tissue segments, or even the whole organ. The validity of such multi-cellular constructs crucially depends on whether or not they take into account the heart s fine architecture, as cardiac structure and function are tightly interrelated. [Pg.137]

Authors Cell model of bone resorption Effect of isoflavones... [Pg.98]


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See also in sourсe #XX -- [ Pg.139 , Pg.143 , Pg.149 ]




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Cell models

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