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.

所提出的工作的主要目标是研究新方法，为复杂生物聚合系统的行为的有效建模和模拟提供一种手段。这些系统通常在多个空间和时间尺度（水平）上发生重要的现象。 精细尺度（小而快速，例如单个原子的运动）现象对粗尺度（大而慢；例如基因表达在很大程度上取决于分子的形状（构象））行为有显着贡献的系统在大分子中很常见为了真正了解重要细胞过程的行为和控制，我们必须了解其背后的物理原理和机制。 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，这些分子系统的计算成本非常高，目前无法对它们进行建模和模拟到足够的水平（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显着变化，因此模型本身必须同样能够适应，以便能够准确地表示重要过程，同时保持快速和成本效益。拟议的工作致力于这结束。这项工作涉及利用先进的多体方法制定自适应、多层次建模策略。基于物理的内部指标指导将系统模型划分为多个区域，每个区域都有自己的局部时间和空间尺度集。区域边界和模型类型可能从原子（精细尺度）到连续体（最粗尺度）不等，然后根据需要动态（自适应）调整，以最小的计算成本捕获重要的系统行为。 底层的 FDCA 和 ODCA 公式产生的方程本质上分为这些区域（子域）。此外，这些方程的整体结构是二叉树的结构，因此所得的公式非常有利于有效的并行计算机实现。这项工作的影响将大大提高对此类复杂分子动力学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有效的方式处理更加复杂的系统，从而节省成本、时间和资源，从而加深理解。 所提出的自适应多尺度策略特别适合的此类系统的例子是包括 RNA、DNA 和蛋白质的生物聚合物系统。所提出的框架预计将提供一种更深入地了解和理解关键生物分子过程的方法，这可能会极大地有助于我们将来学习修改和控制此类过程。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。