Résumé
Coarse-grained modeling, coarse-grained models, aim at simulating the behaviour of complex systems using their coarse-grained (simplified) representation. Coarse-grained models are widely used for molecular modeling of biomolecules at various granularity levels. A wide range of coarse-grained models have been proposed. They are usually dedicated to computational modeling of specific molecules: proteins, nucleic acids, lipid membranes, carbohydrates or water. In these models, molecules are represented not by individual atoms, but by "pseudo-atoms" approximating groups of atoms, such as whole amino acid residue. By decreasing the degrees of freedom much longer simulation times can be studied at the expense of molecular detail. Coarse-grained models have found practical applications in molecular dynamics simulations. Another case of interest is the simplification of a given discrete-state system, as very often descriptions of the same system at different levels of detail are possible. An example is given by the chemomechanical dynamics of a molecular machine, such as Kinesin. The coarse-grained modeling originates from work by Michael Levitt and Ariel Warshel in 1970s. Coarse-grained models are presently often used as components of multiscale modeling protocols in combination with reconstruction tools (from coarse-grained to atomistic representation) and atomistic resolution models. Atomistic resolution models alone are presently not efficient enough to handle large system sizes and simulation timescales. Coarse graining and fine graining in statistical mechanics addresses the subject of entropy , and thus the second law of thermodynamics. One has to realise that the concept of temperature cannot be attributed to an arbitrarily microscopic particle since this does not radiate thermally like a macroscopic or black body ́ ́. However, one can attribute a nonzero entropy to an object with as few as two states like a bit ́ ́ (and nothing else). The entropies of the two cases are called thermal entropy and von Neumann entropy respectively.
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