A Comprehensive Approach Towards Adaptive Multiscale Modeling of Biopolymers Using Highly Parallelizable Methods

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The principal objective of this work is to research and extend methods which can provide a comprehensive framework for the efficient adaptive modeling and simulation of the dynamic behavior of highly complex bio-polymeric (e.g. RNAs, DNAs, proteins, etc.) systems where important phenomena take place at multiple spatial and temporal scales. In order to alleviate the computational burden of these complex problems it is often required to change the system resolution locally or globally. This research utilizes efficient, highly parallelizable Divide and Conquer Algorithm (DCA)-based methods to model different domains of large systems simultaneously in different resolutions (from atomistic to continuum), as well as, adaptively switching between these resolutions (as guided by internal metrics) so to realize a near optimal combination of simulation speed and accuracy. This research if successful will allow the modeling and detailed dynamic simulation of these systems to a level greatly beyond (in system size, fidelity, and simulation duration) that which is currently possible. Framework components and basic mechanisms will be researched, designed, fabricated, and tested to validate the underlying methods and models as well as demonstrate performance. Deliverables will include a catalog of fundamental modeling model types and model-type transitioning tools, source code, demonstration and validation files, documentation of research results, engineering student education, and engineering research experiences for area STEM teachers.If successful, the impact of this work will be a great increase in the rate and extent to which such complex systems may be modeled and analyzed. This will allow the analyst to treat far more complex systems (not just bio-polymers) in a more cost, time, and resource effective manner than is currently possible. This will provide greater insight into and understanding of key biomolecular processes, which may contribute greatly to our learning to modify and control such essential processes in the future. Such understanding and ability could significantly impact human health in many positive respects. Key aspects of this work will be presented as appropriate in undergraduate and graduate level courses in dynamics, and computation, demonstrating how this research relates to the course topics, and familiarizing the students with the advanced analysis, multiscale, numerical, and adaptive methods available. Related instructional aids and web-based tools will be developed, with the associated open source programs being freely distributed (this work will also become and integral part of the POEMS, LAMMPS, and SimTK computational tools). More direct undergraduate involvement by some students will be achieved through Undergraduate Research Program (URP) participation, targeting students from under-represented groups.