Framework for the Adaptive Multiscale Modeling of Biopolymers

生物聚合物自适应多尺度建模框架

• 批准号: 0757936
• 负责人: Kurt Anderson
• 金额: $ 33.94万
• 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: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0757936&HistoricalAwards=false
• 关键词: Framework; Adaptive; Multiscale; Modeling; Biopolymers

The principal objective of the proposed work is to research new methods, which provide a means for the efficient modeling and simulation of the behavior of complex bio-polymeric systems. These systems often have important phenomena taking place at multiple spatial and temporal scales (levels). Systems where fine scale (small and rapid, e.g. motions of individual atoms) phenomena contributes significantly to coarse scale (large and slow; e.g. gene expression depends to significant degree on the shape(conformation) of the molecule) behavior are common in macro-molecular processes and are essential to human health.To gain true insight into the behavior and control of important cellular processes, one must understand the physical principles and mechanisms that underlie them. Physics based modeling and simulation will play a critical role towards gaining such an understanding. Unfortunately, these molecular systems are so computationally costly that they cannot currently be modeled and simulated to an adequate level (in accuracy and duration). Because many of the important aspects of these molecular processes change significantly during the process of interest, the model itself must be similarly able to adapt so that it can accurately represent the important process, while remaining fast and cost effective.The proposed work is devoted to this end. This work involves to production of an adaptive, multi-level modeling strategy, utilizing advanced multibody methods. Physics-based internal metrics guide the division of the system model into regions, each with its own local temporal and spatial set of scales. The region boundaries and model types, which may vary from atomistic (fine scale) to continuum (coarsest scale), are then dynamically (adaptively) adjusted, as needed to capture important system behavior at minimum computational cost. The underlying FDCA and ODCA formulations produce equations which are inherently divided into such regions (subdomains). Additionally, the overall structure of these equations are those of a binary-tree, so the resulting formulation is highly conducive to effective parallel computer implementation. The impact of this work will be a great increase in the rate and extent to which such complex molecular dynamic systems may be modeled and analyzed. This will allow the analyst to treat far more complex systems in a more cost, time, and resource effective manner than is currently possible, thus leading to greater understanding. Examples of such systems where the proposed adaptive multiscale strategy should be particularly suitable are biopolymeric systems which include RNA, DNA, and proteins. The proposed framework is expected to provide a means to obtain greater insight into and understanding of key biomolecular processes, which may contribute greatly to our learning to modify and control such processes in the future. Such understanding and ability could significantly impact human health in many positive respects.

拟议工作的主要目标是研究新方法，该方法为复杂生物聚合系统的行为有效建模和模拟提供了一种手段。这些系统通常具有重要的现象，发生在多个空间和时间尺度（水平）。 精细尺度（例如各个原子的运动）现象显着贡献了粗糙量表（大而慢；例如基因表达）在很大程度上取决于宏观分子过程中的形状（分子的形状（构象）），在人类健康中至关重要，并且在人类健康中必不可少。在人类健康中，在重要的蜂窝手术过程中，必须了解重要的蜂窝手术。 基于物理的建模和模拟将在获得这种理解方面发挥关键作用。 不幸的是，这些分子系统在计算上是如此昂贵，以至于目前无法对其进行建模和模拟，以达到足够的水平（准确性和持续时间）。 由于这些分子过程的许多重要方面在感兴趣的过程中发生了很大变化，因此该模型本身必须同样能够适应，以便它可以准确地表示重要过程，同时保持快速和成本效益。拟议的工作专门用于此目的。这项工作涉及利用高级多体方法的自适应，多层建模策略的生产。基于物理学的内部指标将系统模型的分割指导为区域，每个区域都有其自身的局部时间和空间尺度集。然后根据需要（自适应）调整区域边界和模型类型，可能从原子（细尺度）到连续体（最粗糙），以最低的计算成本捕获重要的系统行为。 基本的FDCA和ODCA公式产生的方程固有地分为此类区域（子域）。此外，这些方程的整体结构是二进制树的整体结构，因此由此产生的公式极大地有助于有效的并行计算机实现。这项工作的影响将大大提高可以对这种复杂的分子动态系统进行建模和分析的速率和程度。这将使分析师能够以比目前更多的成本，时间和资源有效的方式来处理更复杂的系统，从而导致更大的理解。 此类系统的示例应特别合适，其中包括RNA，DNA和蛋白质包括生物聚合系统。预期该拟议的框架将提供一种对关键生物分子过程的更深入了解和理解的方法，这可能会极大地促进我们将来修改和控制此类过程的学习。在许多积极的方面，这种理解和能力可能会对人类健康产生重大影响。