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Characterizing Multiscale Mechanical Properties of Brain Tissue Using Atomic Force Microscopy, Impact Indentation, and Rheometry

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Published on: September 6, 2016

The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models.

W G Noid¹, Jhih-Wei Chu, Gary S Ayton

¹Center for Biophysical Modeling and Simulation and Department of Chemistry, University of Utah, Salt Lake City, Utah 84112-0850, USA.

The Journal of Chemical Physics

|July 8, 2008

Summary

This summary is machine-generated.

This study formalizes the multiscale coarse-graining (MS-CG) method, enabling accurate simulations of biological and soft matter systems. The approach ensures coarse-grained models precisely match atomistic simulations for reliable long-timescale process investigations.

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Area of Science:

Computational physics and chemistry
Statistical mechanics
Biomolecular modeling

Background:

Coarse-grained (CG) models offer computational efficiency for studying long time- and length-scale processes in biological and soft matter systems.
The Izvekov-Voth multiscale coarse-graining (MS-CG) method determines effective interactions for CG sites from atomistic simulations.
Existing methods require rigorous frameworks for accurate multiscale modeling.

Purpose of the Study:

To develop a formal statistical mechanical framework for the MS-CG method.
To demonstrate how the variational principle can determine the many-body potential of mean force (PMF) for MS-CG models.
To establish a rigorous multiscale bridge connecting atomistic and CG equilibrium ensembles.

Main Methods:

Developed a formal statistical mechanical framework for the MS-CG method.
Utilized a variational principle to derive the many-body PMF for CG sites.
Employed computer simulations of atomistic models to calculate approximations to the PMF.
Introduced generalizations for systems with intramolecular constraints and momentum distribution consistency.

Main Results:

The MS-CG method, using a derived PMF, generates CG equilibrium distributions consistent with atomistic models.
A rigorous multiscale bridge is established between atomistic and CG equilibrium ensembles.
Optimal algorithms for approximating the many-body PMF were suggested.
Generalizations for constrained systems and momentum distribution were introduced.

Conclusions:

The formalized MS-CG method provides a rigorous connection between atomistic and coarse-grained simulations.
This framework enables accurate and efficient investigation of complex biological and soft matter phenomena.
The developed methods offer practical approaches for constructing accurate CG models.