AMC-SS: Theory and Modeling of Rare Events

AMC-SS：罕见事件的理论和建模

• 批准号: 0708140
• 负责人: Eric Vanden-Eijnden
• 金额: $ 36.67万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0708140&HistoricalAwards=false
• 关键词: AMC; SS; Theory; Modeling; Rare

The present proposal focuses on the study of rare events as they arise from real applications in computational chemistry, material sciences or molecular biology (for details on the latter, see the second paragraph below). Examples of such rare events include phenomena as diverse as nucleation events during phase transitions, chemical reactions, conformation changes of biomolecules, or bistable behaviors in genetic switches. The study of these rare events requires going beyond Freidlin-Wentzell theory of large deviations, which in mathematics has been the traditional tool to analyze rare events. It also requires integrating the theory within a computational perspective, which is necessary since traditional numerical tools such as Monte Carlo or direct simulation of SDEs are highly ineffective for rare events. Here the PI proposes to do so by (i) generalizing the theoretical framework of Transition Path Theory (TPT), which allows one to describe the statistical mechanics of rare events in situations when large deviation theory does not apply, and (ii) developing associated numerical algorithms such as the String Method for the effective computation of the various objects in TPT (like the probability density of reactive trajectories, their probability current and flux, and the rate of the reaction).The excitement of using molecular dynamics as a tool to do biology stems for the fact that it would enable us to understand the behavior of biomolecules such as protein, enzymes, ion channels, etc. from the "jiggling and wiggling" of the atoms they are made of (to quote Richard Feynman). This would allow for a much deeper understanding of their function and one that goes beyond what is currently achievable via experiments (in which it is hard or impossible to resolve the actual dynamics of the atoms). But this objective comes with tremendous challenges. While biomolecules are tiny objects from our perspective, they are also huge in that they are typically made of thousands of atoms (hundreds of thousands if one accounts for the molecules of solvent surrounding them). Since these atoms move very fast, the equations of motion governing their evolution must be integrated on the computer using a very small time step--typically of the order of one femtosec (1 femtosec = 1e-15 sec = 0.000000000000001 sec). Every such time step takes some time because it involves updating the position of so many atoms. As a result, it is only possible to simulate directly the motion of a typical biomolecule such as Hemoglobin over a few nanoseconds (1 nanosec = 1e-9 sec = 0.000000001 sec). Such a calculation already takes several days on a large computer. This, however, is a problem because the motion of these large molecules which governs their actual function only arise on a much slower time scale, typically of the order of the microseconds or even more (1 microsec = 1e-6 sec = 0.000001 sec). This is because such motion typically involves reactive events, e.g. large-scale reorganization of the shape of the molecule, which are very rare on the time scale of the molecule (though obviously, they are not on our own daily time scale). The direct simulation of any such reactive event would typically require years of computations, which is neither affordable nor practical. On the other hand, various techniques have been designed recently to bypass this difficulty and describe statistically (rather than on a one to one basis) the reactive events. This proposal is about developing these techniques, first at a theoretical level then by exploiting the theory to design efficient numerical algorithms for the computation of the reactive events which are so important in molecular biology.

本提案侧重于研究在计算化学、材料科学或分子生物学中的实际应用中出现的罕见事件(后者的详细情况见下文第二段)。这种罕见事件的例子包括各种各样的现象，如相变中的成核事件、化学反应、生物分子的构象变化或遗传开关中的双稳态行为。对这些罕见事件的研究需要超越Freidlin-Wentzell的大偏差理论，该理论在数学上一直是分析罕见事件的传统工具。它还需要在计算角度内整合理论，这是必要的，因为传统的数值工具，如蒙特卡洛或直接模拟SDE，对罕见事件非常无效。在这里，PI建议这样做：(I)推广跃迁路径理论(TPT)的理论框架，该理论框架允许在大偏差理论不适用的情况下描述罕见事件的统计机制，以及(Ii)开发相关的数值算法，例如用于有效计算TPT中各种对象(如反应轨迹的概率密度、它们的概率电流和通量以及反应速度)的弦方法。使用分子动力学作为研究生物学的工具令人兴奋，因为它将使我们能够理解生物分子的行为，如蛋白质、酶、离子通道、等等，来自组成它们的原子的“摆动和摆动”(引用理查德·费曼的话)。这将使我们能够更深入地了解它们的功能，并超越目前通过实验(很难或不可能解决原子的实际动力学)所能达到的水平。但这一目标伴随着巨大的挑战。虽然从我们的角度来看，生物分子是微小的物体，但它们也是巨大的，因为它们通常由数千个原子组成(如果考虑到它们周围的溶剂分子，则有数十万个)。由于这些原子移动非常快，控制它们演化的运动方程必须使用非常小的时间步长在计算机上积分--通常是一飞秒的量级(1飞秒=1e-15秒=0.000000000000001秒)。每一个这样的时间步长都需要一些时间，因为它涉及到更新这么多原子的位置。因此，只能直接模拟典型生物分子如血红蛋白在几纳秒内的运动(1纳秒=1e-9秒=0.000000001秒)。在一台大型计算机上，这样的计算已经需要几天时间。然而，这是一个问题，因为这些控制其实际功能的大分子的运动只出现在更慢的时间尺度上，通常是微秒甚至更长的量级(1微秒=1e-6秒=0.000001秒)。这是因为这种运动通常涉及反应事件，例如分子形状的大规模重组，这在分子的时间尺度上是非常罕见的(尽管很明显，它们不在我们自己的日常时间尺度上)。任何此类反应事件的直接模拟通常都需要多年的计算，这既负担不起，也不实用。另一方面，最近已经设计了各种技术来绕过这一困难，并在统计上(而不是在一对一的基础上)描述反应事件。这项建议是关于发展这些技术，首先在理论层面上，然后利用该理论来设计有效的数值算法来计算在分子生物学中如此重要的反应事件。