Structural-dynamical -SD model

In Section IX we have outlined a nonheuristic way of modeling of specific interactions. This way is based on studies of dynamics of a reasonable but very crude picture of water structure. In view of the obtained results, the absorption R-band arises in water due to elastic interactions. In terms of the structural dynamic (SD) model, these are characterized by two force constants one (k) refers to an expansion AL and another (Ka) refers to a turn a of the H-bond. The success of the composite hat-curved-structural-dynamical (HC-SD) model suggests an idea that this model actually describes two states of water, which... 

Figure 62. Solid lines contribution of the structural-dynamical model to the dimensionless absorption Astr(v) calculated in the R-band by the ACF method, (a) Calculation for H20, the dimensionless collision frequency Y = 0.6, r/L = 0.27,/) = 2.07. (b) Calculation for D20 with Y = 1.3, r/L = 0.4,/j = 3.54. Dotted lines total absorption calculated for the composite HC-SD model. Temperature 22.2°C. The peak ordinates are set equal to 1.

Particle-based simulation techniques include atomistic MD and coarse-grained molecular dynamics (CG-MD). Accelerated dynamics methods, such as hyperdynamics and replica exchange molecular dynamics (REMD), are very promising for circumventing the timescale problem characteristic of atomistic simulations. Structure and dynamics at the mesoscale level can be described within the framework of coarse-grained particle-based models using such methods as stochastic dynamics (SD), dissipative particle dynamics (DPD), smoothed-particle hydrodynamics (SPH), lattice molecular dynamics (LMD), lattice Boltzmann method (IBM), multiparticle collision dynamics (MPCD), and event-driven molecular dynamics (EDMD), also referred to as collision-driven molecular dynamics or discrete molecular dynamics (DMD). 

Kim, S.-H. et al. (2002). Dynamic and clustering model of bacterial chemotaxis receptors Structural basis for signaling and high sensitivity. Proc. Natl. Acad. Sd. 99, 11611-11615. 

The proposed model consists of two components a system d3mamics framework and a Bayesian belief network structure. The system dynamics formalism enables us to represent change over time and change due to feedback. The Bayesian behef network formalism enables us to represent networks of causality and capture stocasticity and uncertainty. BBNs also enable us to incorporate new knowledge and update the model as new evidence becomes available. Combining SD and BBN has been previously proposed and applied in aviation risk context (Mohaghegb, et.al., 2008). 

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