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

使用高度并行化方法进行生物聚合物自适应多尺度建模的综合方法

• 批准号: 1161872
• 负责人: Kurt Anderson
• 金额: $ 30万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1161872&HistoricalAwards=false
• 关键词: Comprehensive; Approach; Towards; Adaptive; Multiscale

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.

这项工作的主要目标是研究和扩展方法，为高效自适应建模和模拟高度复杂的生物聚合物(如RNA、DNA、蛋白质等)的动态行为提供一个全面的框架。重要现象在多个空间和时间尺度上发生的系统。为了减轻这些复杂问题的计算负担，经常需要改变系统的局部或全局分辨率。该研究利用高效、高度并行的基于分治算法(DCA)的方法，以不同的分辨率(从原子级到连续体)同时对大系统的不同域进行建模，并在这些分辨率之间进行自适应切换(根据内部度量)，从而实现模拟速度和精度的近乎最佳的组合。这项研究如果成功，将使这些系统的建模和详细的动态模拟大大超出目前可能的水平(在系统规模、保真度和模拟持续时间方面)。将对框架组件和基本机构进行研究、设计、制造和测试，以验证基本方法和模型，并展示性能。交付成果将包括基本建模模型类型和模型类型转换工具的目录、源代码、演示和验证文件、研究结果文档、工程学生教育以及STEM领域教师的工程研究经验。如果成功，这项工作的影响将是极大地提高对此类复杂系统进行建模和分析的速度和程度。这将使分析师能够以比目前更具成本、时间和资源效益的方式来处理更复杂的系统(不仅仅是生物聚合物)。这将提供对关键生物分子过程的更多洞察和理解，这可能对我们未来学习修改和控制这些关键过程有很大帮助。这种理解和能力可以在许多积极方面对人类健康产生重大影响。这项工作的关键方面将在动力学和计算的本科生和研究生水平的课程中适当地介绍，展示这项研究如何与课程主题相关，并使学生熟悉可用的高级分析、多尺度、数值和自适应方法。将开发相关的教学辅助工具和基于网络的工具，相关的开放源码程序将免费分发(这项工作也将成为POEMS、LAMMPS和SimTK计算工具的组成部分)。一些学生将通过参与本科生研究计划(URP)实现更直接的本科生参与，目标是来自代表性不足群体的学生。