A Robust Framework for Adaptive Multiscale Modeling of Biopolymers using Highy Parallelizable Methods

Abstract

Due to the challenges involved with modeling complex molecular systems,it is essential that computationally intelligent schemes be produced which put the computational effort where and when it is needed to capture important phenomena, and maintain needed accuracy at minimum costs. In this work, we develop and investigate algorithms for the adaptive modeling and simulation of the dynamic behavior of highly complex multiscale processes. This is accomplished through the appropriate use of an adaptive hybridization of existing, newly developed, and proposed advanced multibody dynamics algorithms and modeling strategies for forward dynamic simulation. The adaptive multiscale simulation technique presented here benefits from the highly parallelizable structure of the divide and conquer (DCA) framework for modeling multibody systems. These algorithms permits a large complex molecule (or systems of molecules) to be seamlessly treated using a hierarchy of reduced order models ranging from atomistic to the continuum scale. The reduced order and low fidelity models, when correctly developed, can provide significant computational savings. The reason is these coarser scale models effectively constrain out high frequency modes which dominate the integration of the equations of motion, but are of little or no relevance to the important overall conformational behavior of the system. When such fine-scale (temporal and/or spatial) scales are not needed the subassemblies of the molecule can be identified and modeled by coarser scale representations which may include rigid-body models, articulated body models, flexible body models, and continuum models. The simulation technique using the DCA framework permits switching between different resolution models adaptively during the simulation. For example, from fully atomistic to a multi-flexible or multi-rigid body representation can be achieved depending on the researcher specified internal metric indicators or error estimates.