ITR - ASE - Sim: Iterative Algorithms for solving Difficult Inverse Problems

ITR - ASE - Sim：解决困难反问题的迭代算法

• 批准号: 0426568
• 负责人: Veit Elser
• 金额: $ 10.24万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0426568&HistoricalAwards=false
• 关键词: ITR; ASE; Sim; Iterative; Algorithms

This award was made on a proposal submitted to the Division of Materials Research under the Information Technology Research solicitation NSF-04-012. Research activities covered by this award fall under the National Priority Area, "Advances in Science and Engineering," and the Technical Focus Area, "Innovation in Computational Modeling or Simulation in Research." It supports a pilot project of computational research and algorithmic development on inverse problems with a focus on protein folding. The work may lead to broad applications in condensed matter physics and in other disciplines. Inverse problems are frequently encountered in science and engineering, and especially difficult ones -one-way functions-are exploited in cryptography and data security schemes. Two important examples are the recovery of molecular structure from x-ray diffraction data and factoring the modulus in RSA cryptosystems. As investigations of matter increase in complexity and alternative one-way functions are explored for information security, there is a greater need for efficient and general-purpose algorithms for solving difficult inverse problems. This project will further develop a promising, recently discovered algorithm with an eye to one of the most challenging inverse problems in biology, protein structure prediction from primary sequence data. The new algorithm is a significant departure from the optimization strategies used in the past, which do not exploit the highly designed nature of the protein's energy landscape. The PI aims to demonstrate the advantages of this algorithm over more traditional optimization algorithms. This project will focus on folding model proteins. The PI also aims to implement the key operations of the algorithm for realistic protein potentials. The next generation of scientists and engineers will likely increasingly rely on shared databases and standardized computing protocols in the conduct of their work. The PI aims to develop a miniature realization of such a work environment called "semiprotein world" for Ithaca area high school students. Semiproteins are model proteins with highly simplified properties, but which pose many of the same challenges posed by real proteins. Through a collection of software tools, including a web-based semiprotein data bank, semiprofessional researchers with web access will be able to design and fold semiproteins, and then deposit their findings in the database. The design of semiprotein world will involve on-site participation of Ithaca area high school students and undergraduates in the Cornell Center for Materials Research NSF-REU program. This award also helps support the PI's pilot efforts in developing this outreach program.%%%This award was made on a proposal submitted to the Division of Materials Research under the Information Technology Research solicitation NSF-04-012. Research activities covered by this award fall under the National Priority Area, "Advances in Science and Engineering," and the Technical Focus Area, "Innovation in Computational Modeling or Simulation in Research." It supports computational research and algorithmic development on inverse problems with a focus on protein folding. The work may lead to broad applications in condensed matter physics and in other disciplines. Inverse problems are frequently encountered in science and engineering, and especially difficult ones -one-way functions-are exploited in cryptography and data security schemes. Two important examples are the recovery of molecular structure information from x-ray diffraction data and factoring in cryptography. As investigations of matter increase in complexity and new one-way functions are explored for information security, there is a greater need for efficient and general-purpose algorithms for solving difficult inverse problems. This project will further develop a promising, recently discovered algorithm with an eye to one of the most challenging inverse problems in biology, protein structure prediction from primary sequence data. The new algorithm is a significant departure from traditional optimization algorithms. The PI aims to demonstrate the advantages of his new algorithm. This project will focus on folding model proteins and laying the foundations for more realistic protein models. The next generation of scientists and engineers will likely increasingly rely on shared databases and standardized computing protocols in the conduct of their work. The PI aims to develop a miniature realization of such a work environment called "semiprotein world" for Ithaca area high school students. Semiproteins are model proteins with highly simplified properties, but which pose many of the same challenges posed by real proteins. Through a collection of software tools, including a web-based semiprotein data bank, semiprofessional researchers with web access will be able to design and fold semiproteins, and then deposit their findings in the database. The design of semiprotein world will involve on-site participation of Ithaca area high school students and undergraduates in the Cornell Center for Materials Research NSF-REU program. This award also helps support the PI's pilot efforts in developing this outreach program.***

该奖项是根据信息技术研究招标NSF-04-012提交给材料研究部的提案而颁发的。该奖项涵盖的研究活动属于国家优先领域，“科学与工程的进步”和技术重点领域，“计算建模或模拟研究的创新”。“它支持一个关于逆问题的计算研究和算法开发的试点项目，重点是蛋白质折叠。这项工作可能会在凝聚态物理和其他学科中得到广泛的应用。逆问题在科学和工程中经常遇到，特别是困难的-单向函数-被利用在密码学和数据安全方案。两个重要的例子是从X射线衍射数据中恢复分子结构和RSA密码系统中的模因子分解。随着调查的问题增加的复杂性和替代的单向函数的信息安全进行了探索，有一个更大的需要，高效和通用的算法来解决困难的反问题。该项目将进一步开发一个有前途的，最近发现的算法，着眼于生物学中最具挑战性的逆问题之一，从一级序列数据预测蛋白质结构。新算法与过去使用的优化策略有很大不同，过去使用的优化策略没有利用蛋白质能量景观的高度设计性质。PI的目的是证明这种算法的优势，更传统的优化算法。该项目将重点关注折叠模型蛋白质。PI还旨在实现算法的关键操作，以获得真实的蛋白质势。下一代科学家和工程师在开展工作时可能会越来越多地依赖共享数据库和标准化计算协议。PI旨在为伊萨卡地区的高中生开发一个名为“半蛋白质世界”的工作环境的微型实现。半蛋白质是具有高度简化性质的模型蛋白质，但其提出了许多与真实的蛋白质相同的挑战。通过一系列软件工具，包括一个基于网络的半蛋白质数据库，半专业的研究人员可以通过网络设计和折叠半蛋白质，然后将他们的发现存款数据库。半蛋白质世界的设计将涉及伊萨卡地区高中生和康奈尔大学材料研究中心NSF-REU计划的本科生的现场参与。该奖项还有助于支持PI在开发此推广计划方面的试点工作。%%%该奖项是根据信息技术研究招标NSF-04-012提交给材料研究部的提案而颁发的。该奖项涵盖的研究活动属于国家优先领域，“科学与工程的进步”和技术重点领域，“计算建模或模拟研究的创新”。“它支持反问题的计算研究和算法开发，重点是蛋白质折叠。这项工作可能会在凝聚态物理和其他学科中得到广泛的应用。逆问题在科学和工程中经常遇到，特别是困难的-单向函数-被利用在密码学和数据安全方案。两个重要的例子是从X射线衍射数据中恢复分子结构信息和密码学中的因子分解。随着对物质复杂性的研究和对信息安全的新单向函数的探索，人们越来越需要有效和通用的算法来解决困难的逆问题。该项目将进一步开发一个有前途的，最近发现的算法，着眼于生物学中最具挑战性的逆问题之一，从一级序列数据预测蛋白质结构。新算法是一个显着偏离传统的优化算法。PI旨在展示他的新算法的优点。该项目将专注于折叠模型蛋白质，并为更现实的蛋白质模型奠定基础。下一代科学家和工程师在开展工作时可能会越来越多地依赖共享数据库和标准化计算协议。PI旨在为伊萨卡地区的高中生开发一个名为“半蛋白质世界”的工作环境的微型实现。半蛋白质是具有高度简化性质的模型蛋白质，但其提出了许多与真实的蛋白质相同的挑战。通过一系列软件工具，包括一个基于网络的半蛋白质数据库，半专业的研究人员可以通过网络设计和折叠半蛋白质，然后将他们的发现存款数据库。半蛋白质世界的设计将涉及伊萨卡地区高中生和康奈尔大学材料研究中心NSF-REU计划的本科生的现场参与。该奖项还有助于支持PI在开发此推广计划方面的试点工作。*