Free computational modeling

Farago O (2003) Water-free computer model for fluid bilayer membranes. J Chem Phys 119(l) 596-605... 

O. Farago,/. Chem. Phys., 119, 596 (2003). Water-Free Computer Model for Fluid Bilayer... 

Watson G W, P Tschaufeser, A Wall, R A Jackson and S C Parker 1997. Lattice Energy and Free Energy Minimisation Techniques. Computer Modelling in Inorganic Crystallography. San Diego, Academic Press, pp. 55-81. 

SENSORS BASED ON FREE-STANDING MOLECULARLY IMPRINTED POLYMER MEMBRANES. COMPUTATIONAL MODELLING OF SYNTHETIC MIMICKS OF BIORECEPTORS... 

While one is free to think of CA as being nothing more than formal idealizations of partial differential equations, their real power lies in the fact that they represent a large class of exactly computable models since everything is fundamentally discrete, one need never worry about truncations or the slow aciminidatiou of round-off error. Therefore, any dynamical properties observed to be true for such models take on the full strength of theorems [toff77a]. 

Advanced computational models are also developed to understand the formation of polymer microstructure and polymer morphology. Nonuniform compositional distribution in olefin copolymers can affect the chain solubility of highly crystalline polymers. When such compositional nonuniformity is present, hydrodynamic volume distribution measured by size exclusion chromatography does not match the exact copolymer molecular weight distribution. Therefore, it is necessary to calculate the hydrodynamic volume distribution from a copolymer kinetic model and to relate it to the copolymer molecular weight distribution. The finite molecular weight moment techniques that were developed for free radical homo- and co-polymerization processes can be used for such calculations [1,14,15]. 

We have modelled the [CDopen - methyl pyruvate] complex. The result is shown in Figure 2. In this complex there is no steric hindrance to prevent the free rotation of the substrate around the quinuclidine nitrogen. Thus, in complex shown in Figure 2. there is no preferential stabilization of the substrate. In earlier computer modeling it was suggested that Pt is involved in the stabilization of the [CDopew-a-lfeto ester] complex, i.e. the Pt surface prevent the free rotation of the substrate, however the driving force for enantio-differentiation, i.e. for preferential adsorption of the substrate, was not discussed [14]. 

Fig. 4 HLM Clint, free vs clogD. HLM Clint, app corrected for microsomal protein binding using a computational model for microsomal binding. Open squares and filled triangles represent the same chemical series as in Fig. 3 (series A and B, respectively)...

A first step toward quantum mechanical approximations for free energy calculations was made by Wigner and Kirkwood. A clear derivation of their method is given by Landau and Lifshitz [43]. They employ a plane-wave expansion to compute approximate canonical partition functions which then generate free energy models. The method produces an expansion of the free energy in powers of h. Here we just quote several of the results of their derivation. 

In order to estimate the flux through the SMM cycle and to explore its function, a computer model of methionine metabolism in mature Arabidopsis rosette leaves was developed based on data from radiotracer experiments and on metabolite contents. This model suggested that the cycle serves to stop accumulation of AdoMet, rather than to prevent depletion of free methionine, as proposed by Mudd and Datko.54 Because plants lack the AdoMet feedbacks on MTHFR and AdoMet synthetase that regulate AdoMet pool size in other eucaryotes, the SMM cycle may be the main mechanism whereby plants achieve short-term control of AdoMet level. MMT knockouts of maize and Arabidopsis recently became available, and these can now be used to further investigate the role of the SMM cycle, and to test the predictions of the model. 

Gebe, J.A., Allison, S.A., Clendenning, J.B., and Schurr, J.M. (1995) Monte-Carlo simulations of supercoiling free-energies for unknotted and trefoil knotted DNAs. Biophys. J. 68, 619-633. Beard, D.A. and Schlick, T. (2001) Computational modeling predicts the structure and dynamics of chromatin fiber. Structure 9, 105-114. 

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