CAREER: Stochastic Modeling and Inference in Biophysics

职业：生物物理学中的随机建模和推理

• 批准号: 0449204
• 负责人: Samuel Kou
• 金额: $ 40万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0449204&HistoricalAwards=false
• 关键词: CAREER; Stochastic; Modeling; Inference; Biophysics

Prop ID: DMS-0449204 P I: Kou, Samuel Organization: Harvard University Program: Career 2005Title: CAREER: Stochastic Modeling and Inference in Biophysics Abstract Advances of experimental and computing technology have profoundly changed the field of biophysics. On the experimental side, recent developments in nanotechnology have allowed scientists to follow biological processes on single-molecule basis, providing scientists with powerful means of studying many biophysical processes that were inaccessible just a decade ago. This new frontier also raises significant statistical challenges. It calls upon an urgent need for stochastic modeling, because many classical models derived from oversimplified assumption are no longer valid for single-molecule experiments. Parallel to the experimental progress, the rapid advance of computing resources and Monte Carlo methods also offers great potential for biophysical studies, because in most biophysical experiments inferenceon the underlying stochastic dynamics is complicated by latent processes, and many biophysical problems are computing intensive. The proposal consists of three projects: (A) Constructing stochastic models that account for the subdiffusion phenomenon in single-molecule biophysics, where the goal is to provide models that are not only physically meaningful, butalso capable of explaining the subdiffusion phenomenon that eludes classical diffusion models. (B) Developing data augmentation tools to handle latent processes in biophysical experiments, where the goal is to use the data augmentation techniques to augment the hidden processes so as to efficiently infer from the experimental data the biophysical properties of interest. (C) Developing more efficient Monte Carlo method for computingintensive biophysics problems, where the goal is to construct new Monte Carlo methods capable of sampling complicated distributions, and apply the new method to study the HP protein folding problem. Technology advances have profoundly changed the society and sciencein the recent decades. The field of biophysics benefits from these advances in particular, as the technology advances have allowed scientists to study many biological processes that were inaccessible just a decade ago. These advances also raise significant challenges for statistics, because the unprecedented amount of data brought by the new technology requires critical statistical analysis. While the United States leads the world in both statistics and biophysics research, future prominence dependson innovations in both fields and the interdisciplinary collaborations between them. The research in this proposal aims at developing new statistical models and inference tools that can be directly applied to biophysics studies and will lead to new insight about the physics of complexbiological processes. The development of statistics and biophysics not onlypresents many interdisciplinary research opportunities, but also attracts many bright students in mathematical, biological and physical sciences. The proposed research is integrated with the principal investigator's educational activities at both the graduate and undergraduate level.It seeks to meet high academic standards, while providing graduate training that will prepare students for interdisciplinary research careers.

项目编号：DMS-0449204项目编号：Kou， Samuel组织机构：哈佛大学项目：职业名称：职业：生物物理学中的随机建模与推理摘要实验和计算技术的进步深刻地改变了生物物理学领域。在实验方面，纳米技术的最新发展使科学家能够在单分子的基础上跟踪生物过程，为科学家提供了研究许多生物物理过程的强大手段，而这在十年前是无法实现的。这一新领域也提出了重大的统计挑战。由于许多基于过度简化假设的经典模型在单分子实验中已不再有效，因此迫切需要建立随机模型。与实验的进步并行，计算资源和蒙特卡罗方法的快速发展也为生物物理研究提供了巨大的潜力，因为在大多数生物物理实验中，对潜在随机动力学的推断是由潜在过程复杂的，许多生物物理问题是计算密集型的。该提案包括三个项目：(A)构建解释单分子生物物理学中亚扩散现象的随机模型，其目标是提供不仅具有物理意义的模型，而且能够解释经典扩散模型无法解释的亚扩散现象。(B)开发数据增强工具来处理生物物理实验中的潜在过程，其目标是使用数据增强技术来增强隐藏过程，从而有效地从实验数据中推断出感兴趣的生物物理特性。(C)为计算密集型生物物理问题开发更有效的蒙特卡罗方法，其目标是构建能够对复杂分布进行采样的新蒙特卡罗方法，并将新方法应用于HP蛋白质折叠问题的研究。近几十年来，技术进步深刻地改变了社会和科学。生物物理学领域尤其受益于这些进步，因为技术的进步使科学家们能够研究许多十年前无法实现的生物过程。这些进步也对统计提出了重大挑战，因为新技术带来的空前数量的数据需要关键的统计分析。虽然美国在统计学和生物物理学研究方面都处于世界领先地位，但未来的突出地位取决于这两个领域的创新以及它们之间的跨学科合作。本提案的研究旨在开发新的统计模型和推理工具，可以直接应用于生物物理学研究，并将导致对复杂生物过程的物理学的新见解。统计与生物物理学的发展不仅提供了许多跨学科的研究机会，而且吸引了许多数学、生物和物理科学领域的优秀学生。拟议的研究与主要研究者在研究生和本科水平的教育活动相结合。它力求达到较高的学术标准，同时提供研究生培训，为学生从事跨学科研究做好准备。